evaluating threading building blocks pipelines · choice. in this report we evaluate intel...

Evaluating Threading Building Blocks

Pipelines

Sunu Antony Joseph

Master of Science

Computer Science

School of Informatics

University of Edinburgh

2010

Abstract

Parallel programming is the need in the muti-core era. With many parallel program-

ming languages and libraries developed with the aim of providing higher levels of ab-

straction, which allows programmers to focus on algorithms and data structures rather

than the complexity of the machines they are working on, it becomes difficult for pro-

grammers to choose the right programming environment best suited for their applica-

tion development. Unlike serial programming languages there are very few evaluations

done for parallel languages or libraries that can help programmers to make the right

choice. In this report we evaluate Intel Threading Building Blocks library which is a

library in C++ language that supports scalable parallel programming. The evaluation

is done specifically for the pipeline applications that are implemented using filter and

pipeline class provided by the library. Various features of the library which help during

pipeline application development are evaluated. Different applications are developed

using the library and are evaluated in terms of their usability and expressibility. All

these evaluations are done in comparison to POSIX thread implementation of different

applications. Performance evaluation of these applications are also done to understand

the benefits threading building blocks have in comparison to the POSIX thread imple-

mentations. In the end we provide a guide to future programmers that will help them

decide the best suited programming library for their pipeline application development

depending on their needs.

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Acknowledgements

First, I would like to thank my supervisor, Murray Cole, for his guidance and help

throughout this project and mostly for the invaluable support in difficult times of the

project period. I would also like to thank my family and friends for standing always

by me in every choice I make.

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Declaration

I declare that this thesis was composed by myself, that the work contained herein is

my own except where explicitly stated otherwise in the text, and that this work has not

been submitted for any other degree or professional qualification except as specified.

(Sunu Antony Joseph)

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To my parents and grandparents...

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Table of Contents

1 Introduction 11.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2 Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.4 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2 Background 82.1 Parallel Programming Languages . . . . . . . . . . . . . . . . . . . . 8

2.2 Task and Data Parallelism . . . . . . . . . . . . . . . . . . . . . . . . 8

2.3 Parallel Design Patterns . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3.1 Pipeline Pattern . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.3.2 Pipeline using both data and task parallelism . . . . . . . . . 11

2.4 Intel Threading Building Blocks . . . . . . . . . . . . . . . . . . . . 12

2.4.1 Threading Building Blocks Pipeline . . . . . . . . . . . . . . 13

2.5 POSIX Threads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3 Issues and Methodology 143.1 Execution modes of the filters/stages . . . . . . . . . . . . . . . . . . 14

3.2 Setting the number of threads to run the application . . . . . . . . . . 15

3.3 Setting an upper limit on the number of tokens in flight . . . . . . . . 15

3.4 Nested parallelism . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.5 Usability, Performance and Expressibility . . . . . . . . . . . . . . . 16

4 Design and Implementation 184.1 Selection of Pipeline Applications . . . . . . . . . . . . . . . . . . . 18

4.1.1 Fast Fourier Transform . . . . . . . . . . . . . . . . . . . . . 19

4.1.2 Filter bank for multi-rate signal processing . . . . . . . . . . 19

4.1.3 Bitonic sorting network . . . . . . . . . . . . . . . . . . . . . 20

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4.2 Fast Fourier Transform Kernel . . . . . . . . . . . . . . . . . . . . . 20

4.2.1 Application Design . . . . . . . . . . . . . . . . . . . . . . . 20

4.2.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . 21

4.3 Filter bank for multi-rate signal processing . . . . . . . . . . . . . . . 26


4.3.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.4 Bitonic sorting network . . . . . . . . . . . . . . . . . . . . . . . . . 28


4.4.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . 29

5 Evaluation and Results 325.1 Bitonic Sorting Network . . . . . . . . . . . . . . . . . . . . . . . . 32

5.1.1 Usability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5.1.2 Expressibility . . . . . . . . . . . . . . . . . . . . . . . . . . 34

5.1.3 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5.2 Filter bank for multi-rate signal processing . . . . . . . . . . . . . . . 37

5.2.1 Usability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

5.2.2 Expressibility . . . . . . . . . . . . . . . . . . . . . . . . . . 39

5.2.3 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . 39

5.3 Fast Fourier Transform Kernel . . . . . . . . . . . . . . . . . . . . . 41

5.3.1 Usability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

5.3.2 Expressibility . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5.3.3 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5.4 Feature 1: Execution modes of the filters/stages . . . . . . . . . . . . 44

5.4.1 serial out of order and serial in order Filters . . . . . . . . . 44

5.4.2 Parallel Filters . . . . . . . . . . . . . . . . . . . . . . . . . 46

5.5 Feature 2: Setting the number of threads to run the application . . . . 47

5.6 Feature 3: Setting an upper limit on the number of tokens in flight . . 50

5.7 Feature 4: Nested parallelism . . . . . . . . . . . . . . . . . . . . . . 53

6 Guide to the Programmers 546.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

6.2 Experience of the programmer . . . . . . . . . . . . . . . . . . . . . 54

6.3 Design of the application . . . . . . . . . . . . . . . . . . . . . . . . 55

6.4 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

6.5 Scalability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

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6.6 Load Balancing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

6.7 Application Development Time . . . . . . . . . . . . . . . . . . . . . 57

7 Future Work and Conclusions 587.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

7.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

7.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Bibliography 61

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List of Figures

1.1 Parallel Programming Environments[2] . . . . . . . . . . . . . . . . 2

2.1 Data Parallelism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2 Task Parallelism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3 Linear Pipeline Pattern . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.4 Non-Linear Pipeline Pattern . . . . . . . . . . . . . . . . . . . . . . 10

2.5 Flow of tokens through the stages in a pipeline along the timeline. . . 11

2.6 Using the Hybrid approach. Multiple workers working on multiple

data in stage 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4.1 Structure of the Fast Fourier Transform Kernel Pipeline. . . . . . . . 20

4.2 Structure of the Fast Fourier Transform Kernel Pipeline implemented

in TBB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

5.1 Performance of the Bitonic Sorting application(TBB) on machines with

different number of cores. . . . . . . . . . . . . . . . . . . . . . . . . 36

5.2 Performance of the Bitonic Sorting application(pthread) on machines

with different number of cores. . . . . . . . . . . . . . . . . . . . . . 36

5.3 Performance of the Bitonic Sorting application(pthread) with and with-

out Locks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

5.4 Performance of the Filter Bank application(TBB) on machines with


5.5 Performance of the Filter Bank application(pthread) on machines with


5.6 Performance of the Fast Fourier Transform Kernel application(TBB)

on machines with different number of cores. . . . . . . . . . . . . . . 43

5.7 Performance of the Fast Fourier Transform Kernel application(pthread)

on machines with different number of cores. . . . . . . . . . . . . . . 44

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5.8 Latency difference for linear and non-linear implementation assuming

equal execution times of the pipelines stages. . . . . . . . . . . . . . 45

5.9 Performance of Filter bank application(TBB) for different modes of

operation of the filters. . . . . . . . . . . . . . . . . . . . . . . . . . 46

5.10 Performance of Filter bank application with stages running with data

parallelism. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

5.11 Performance of Bitonic Sorting Network varying the number of threads

in execution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

5.12 Performance of Fast Fourier Transform Kernel varying the number of

threads in execution. . . . . . . . . . . . . . . . . . . . . . . . . . . 48

5.13 Performance of Filter Bank varying the number of threads in execution. 49

5.14 Performance of the FFT Kernel for different input sizes and number of

cores of the machine. . . . . . . . . . . . . . . . . . . . . . . . . . . 50

5.15 Performance of the Filter Bank application different input sizes and

number of cores of the machine. . . . . . . . . . . . . . . . . . . . . 50

5.16 Performance of Bitonic Sorting Network varying the limit on the num-

ber of tokens in flight. . . . . . . . . . . . . . . . . . . . . . . . . . . 51

5.17 Performance of Fast Fourier Transform varying the limit on the number

of tokens in flight. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

5.18 Performance of Filter Bank varying the limit on the number of tokens

in flight. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

5.19 Performance of pthread applications varying the limit on the number

of tokens in flight. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

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List of Tables

4.1 Applications selected for the evaluation. . . . . . . . . . . . . . . . . 19

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Chapter 1

Introduction

Programmers have got used to serial programming and expect the programs they de-

velop to run faster with the next generation processor that have been coming out in the

market in the past years. But those days are over and the next generation chips will

have more processors with each individual processor not being faster than the previous

years model [8]. The clock frequencies of chips are no longer increasing making par-

allelism the only way to improve the speed of the computer. Parallel computer systems

are useless without having parallel software to utilise their full potential. The idea

of converting serial programs to run in parallel can be limiting since its performance

may be much lower than the best parallel algorithms. In sequential programming lan-

guages like conventional C/C++, it is assumed that the set of instructions are executed

sequentially by a single processor whereas a parallel programming language assumes

that simultaneous streams of instructions are fed to multiple processors. Multithread-

ing aims at exploiting the full potential of multi-core processors. The transition to

the multithreaded applications is inevitable and leveraging the present multithreading

libraries can help the developers in threading their application in many ways.

Parallel programming languages or libraries should help software developers to

write parallel programs that work reliably giving good performance. Parallel programs

execute non-deterministically, asynchronously and with the absence of total order of

events, so they are hard to test and debug. The parallel programs developed should

also scale with the additions of more processors into the hardware system. The ul-

timate challenge of parallel programming languages is to overcome these issues and

help developers to develop software with the same ease as in the case with serial pro-

gramming languages.

Several parallel programming languages, libraries and environments have been de-

1

Chapter 1. Introduction 2

veloped to ease the task of writing programs for multiprocessors. Proponents of each

approach often point out various language features that are designed to provide the

programmer with a simple programming interface. But then why are programmers

hesitant to go parallel? For large codes, the cost of parallel software development can

easily surpass that of the hardware on which the code is intended to run. As a result,

users will often choose a particular multiprocessor platform based not only on abso-

lute performance but also the ease with which the multiprocessor may be programmed

[18]. In the past two decades there were many parallel programming environments

developed. So the next question in mind is why have these parallel programming lan-

guages not been so productive? Why are there only a small fraction of the programmers

that write parallel code? This could be because of the hassle of designing, developing,

debugging, testing and maintaining large parallel codes.

Figure 1.1: Parallel Programming Environments[2]

One of the most promising techniques to make parallel programming available for

the general users is the use of parallel programming patterns. Parallel programming

patterns gives software developers a language to describe the architecture of parallel

software. The use of design patterns promotes faster development of structurally cor-

rect parallel programs. All design patterns are structured description of high quality

solutions to recurring problems.

An interesting parallel programming framework that provides templates for the


common patterns in parallel object-oriented design is Threaded Building Blocks (TBB).

Intel Thread Building Blocks1 is a library in C++ language which was built on the no-

tion to separate logical task patterns from physical threads and to delegate task schedul-

ing to the multi-core systems [2]. Threading Building Blocks uses templates for com-

mon parallel patterns. A common parallel programming pattern is the pipeline pattern.

The functional pipeline parallelism is a pattern that is very well suited and used for

many emerging applications, such as streaming and Recognition, Mining and Synthe-

sis (RMS) workloads [12].

In this report we evaluate the pipeline template provided by the Intel threading

building blocks library. We evaluate the pipeline class in terms of its usability, ex-

pressibility and performance. The evaluation is done is comparison to the conven-

tional POSIX2 thread library. This comparative evaluation is based on many experi-

ments that we conducted on both the parallel programming libraries. We implemented

various features that the threading building block library provides in pthread to under-

stand how much easier the threading building blocks library make the programmer’s

job during pipeline application development.

The main intention of the project is to provide a guide to future programmers about

Intel Threading Building Blocks pipelines and also to provide a comparative analysis

of the TBB pipelines with the corresponding pipeline implementations using POSIX

thread.

1.1 Related Work

The growth in commercial and academic interest in the parallel systems has seen an

increase in the number of parallel programming languages and libraries. Development

of usable and efficient parallel programming systems have received much attention

from across the computing and research community. Szafron and Schaeffer in their

paper [17] evaluate parallel programming systems focusing on 3 primary issues of

performance, applicability and usability of many different parallel programming sys-

tems. A controlled experiment was conducted in which half of the graduate students

in a parallel/distributed computing class solved a problem using the Enterprise parallel

programming system [16] while the rest used a parallel programming system con-

sisting of a PVM[5]-like library of message passing calls (NMP [9]). They perform

1www.threadingbuildingblocks.org/2http://standards.ieee.org/regauth/posix/


an objective evaluation during system development that gave them valuable feedback

on the programming model, completeness of the programming environment, ease of

use and learning curves. They perform two experiments in the first one they measure

the ease with which novices can learn the parallel programming systems and produce

correct, but not necessarily efficient, programs and in the second experiment they mea-

sure the productivity of the system in the hands of an expert. When experimented with

novices the primary aim was to to measure how quickly they can learn the system and

produce correct programs. When experimented with experts, the primary aim was to

know the time it takes to produce a correct program that achieves a specified level

of performance on a given machine. They collected statistics like number of lines of

code, number of edits, compiles, program runs, development time, login hours etc. to

measure the usability of the parallel programming systems.

Another work done to analyse the usability of Enterprise parallel programming

system was done by Wilson, Schaeffer and Szafron [22]. Before this work there were

not many comparable work done on parallel programming systems other than by Rao,

Segall and Vrsalovic [15] where they developed a level of abstraction called the imple-

mentation machine. The implementation machine layer provides the user with a set of

commonly used parallel programming paradigms. They analyse how the implemen-

tation machine template helps the user in implementing the chosen implementation

machine efficiently on the underlying physical machine.

Another notable work is by VanderWiel, Nathanson and Lilja [18] where they

evaluate the relative ease of use of parallel programming languages. They borrow

techniques from the software engineering field to quantify the complexity of the three

prominent progamming models: shared memory, message passing and High perfor-

mance Fortan. They use McCabes Cyclomatic program complexity [10] and non-

commented source code lines [6] to quantify the relative complexity of the several

parallel programming languages. They conclude by saying that traditional software

complexity metrics are effective indicators of the relative complexity of parallel pro-

gramming languages or libraries.

1.2 Goals

The primary goal of the project is to evaluate Intel Threading Building Blocks Pipelines

and to understand if the library is best for pipeline application development in terms

of its performance, scalability, expressibility and usability of the library. We need


to objectively compare parallel programming languages for the pipeline applications.

This can be done by understanding the needs of pipeline application developer during

their pipeline application development and use those as the criteria to evaluate Intel

Threading Building Blocks Pipelines.

The Evaluation of Intel Threading Building Blocks pipeline class is done in com-

parison to the conventional POSIX thread implementations. Many features provided

by the library is put into test to understand how helpful it is to a pipeline application

developer. The evaluation includes understanding of the usability of Intel Threading

Building Blocks which is one of the factors that attract developers to use the library in

the complex world of parallel programming. The usability tests have to be done con-

sidering a novice parallel programmer so that at this time when software developers

are making the transition to parallel programming, this information will be helpful for

them to take the right choice during their pipeline application development. Further

we look into the expressibility of the library by understanding how suitable the library

is for different pipeline applications which will help future developers understand how

the library adapts to the different variety of pipeline patterns that are commonly used

for pipeline application development. Finally we evaluate the library in terms of scal-

ability and performance of the pipeline applications developed using the library. This

will help pipeline application developers to understand the performance drawbacks or

benefits of using Intel TBB for their pipeline application development.

1.3 Motivation

There have been many usability experiments conducted for serial programming lan-

guages but very few for the parallel languages or libraries. Such experiments are neces-

sary to help narrow the gap between what parallel programmers want and what current

parallel programming systems provide[17]. Many parallel programming languages are

developed with the aim to simplify the task of writing parallel programs that run on

multi-core machines and there is very little data that tells us about the complexity of

the different parallel programming languages.

There are many parallel programming languages/libraries developed and each of

these languages/libraries may have its own pros and cons for the development of dif-

ferent applications. It is necessary to understand the pros and cons, and highlight it

to the developers so that they can take the right decision to select the apt language/li-

brary for their application development. Since design patterns are the solutions to


the commonly seen problems, categorising pros and cons of different programming

languages/libraries on the different design patterns is a good way to provide the infor-

mation to the developers.

Parallel programs are hard to develop, test and debug. Hence, the usability of the

parallel programming languages play a very important role in the success of the pro-

gramming language. Usability is definitely a factor that programmers will be looking

for when developers are going for the development of applications under fast approach-

ing deadlines or when its a novice programmer who is trying to enter into the parallel

programming world and does not have much idea of the multi-threading concepts.

Parallel programming languages that are developed to make the job of parallel pro-

gramming easier may at times have drawbacks in terms of expressibility, performance

or scalability of the programs developed. Understanding the languages in terms of

these factors is really important for the proper evaluation of the parallel language/li-

brary.

1.4 Thesis Outline

The thesis has been divided into 7 chapters including this chapter which is the intro-

duction. The remaining chapters are laid out as follows:

* Chapter 2 gives an overview about the basic concepts and terminologies that

help to understand the report better.

* Chapter 3 discusses the various features of threading building blocks that help

during pipeline application development and the methodology used for the eval-

uation. It also discusses about how we evaluate threading building blocks in

terms of usability, performance and expressibility of threading building blocks

in comparison to the pthread library.

* Chapter 4 focuses on the design and implementation of the various applications

that are developed in threading building blocks and pthread library.

* Chapter 5 discusses about the evaluations and the results obtained.

* Chapter 6 is a Guide to the future programmers that will help them choose be-

tween threading building blocks and pthread library for their pipeline application

development depending on their needs.


* Chapter 7 draws an overall conclusion and suggests future work.

Chapter 2

Background

In this chapter we discuss the basic concepts that help to better understand the dis-

cussions and explanations given in the report. We discuss about parallel programming

languages, different forms of parallelism, the pipeline design pattern and about Intel

threading building blocks and POSIX threads.

2.1 Parallel Programming Languages

Parallel programming languages and libraries have been developed for programming

parallel computers. These can generally be divided into classes based on the assump-

tions they make about the underlying memory architecture - shared memory, dis-

tributed memory, or shared distributed memory. Shared memory programming lan-

guages communicate by manipulating shared memory variables. Distributed memory

uses message passing. OpenMP and POSIX Threads are two widely used shared mem-

ory APIs, whereas Message Passing Interface (MPI) is the most widely used message-

passing system API [21]. Intel Threading building blocks is an example of shared

memory programming.

2.2 Task and Data Parallelism

Data parallelism is used when you have large amount of data and you want the same

operation to be performed on all the data. This data by data processing can be done in

parallel if they have no other dependencies with each other. A simple example of this

can be seen in Figure 2.1. Here the tasks can be done concurrently because there are

no dependencies between them. Another point to be noted is that the data parallelism

8

Chapter 2. Background 9

is limited to the number of data items you have to process.

Figure 2.1: Data Parallelism

Task parallelism is used when you have multiple operations to be performed on a

data. In task parallelism there will be multiple tasks working on the same data con-

currently. A simple example of this can be seen in Figure 2.2. Here the multiple tasks

perform different independent operations on the same set of data concurrently.

Figure 2.2: Task Parallelism

2.3 Parallel Design Patterns

Parallel software usually does not make full utilisation of the underlying parallel hard-

ware. It is difficult for the programmers to program patterns that utilise the maximum

potential of the hardware and most of the parallel programming environments do not

focus on the design issues. So programmers need a guide to help them during their

application development that would actually enable them to get the maximum parallel


performance. Parallel design patterns are expert solutions to the common occurring

problems that achieve this maximum parallel performance. These patterns provide

quick and reliable parallel applications.

2.3.1 Pipeline Pattern

The Pipeline pattern is a common design pattern that is used when the need is to per-

form computations over many sets of data. This computation that is to be performed on

a set of data, can be viewed as many stages of processing to be performed in a partic-

ular order. The whole computation can be seen as data flowing through a sequence of

stages. A good analogy of this parallel design pattern is a factory assembly line. Like

in an assembly line where each worker is assigned a component of work and all the

workers work simultaneously on their assigned task. A simple example of a pipeline

can be seen in the figure 2.3

Figure 2.3: Linear Pipeline Pattern

In the example in Figure 2.3 stage 1, stage 2, stage 3 and stage 4 together form

the total computation that has to be performed on each set of data. The input data are

fed to stage 1 of the pipeline where the data is processed one after the other and then

passed on to the next stage in the pipeline. When the arrangement is a single straight

chain(Figure 2.3), its called a Linear Pipeline Pattern.

Figure 2.4: Non-Linear Pipeline Pattern

Figure 2.4 is an example for a Non-Linear pipeline pattern. Here you can see stages

with multiple operations happening concurrently. Non Linear pipelines allow feedback


and feed forward connections and also allow more than one output stages, that need

not be the last stage in the pipeline.

Figure 2.5: Flow of tokens through the stages in a pipeline along the timeline.

In Figure 2.5 you can see how the sets of data moves through the stages as the time

passes. Assuming that work load is balanced equally among all the stages and that each

stage takes one time step to finish its processing on a set of data, after the first five time

steps a data set is obtained fully processed at every time step. So a data set that serially

take four time steps to process, when processed using a pipeline, comes out processed

at every time step after first four time steps. This gives good throughput to the system.

The initial four time step delay is because all the four stages are not working together

initially and some resources remain idle till all the stages are occupied with useful

work. This is referred to as filling the pipeline or the latency of the pipeline.

The amount of concurrency in the pipeline is dependent on the number of stages in

the pipeline. More the number of stages more is the concurrency. The data tokens need

to be transferred between the stages which introduces an overhead on the work to be

done by a stage. Thus, when the computation to be done on a set of data is divided into

stages then the work done by the stage should be comparable to the communication

overhead between the stages. The pipeline design pattern works the best when the

all of its stages are equally computationally intensive. If the stages vary widely in

the amount of work done by them then the slowest stage will be a bottleneck in the

performance of the pipeline.

2.3.2 Pipeline using both data and task parallelism

Pipeline is a type of task parallelism where many tasks or computations are applied

to a stream of data. This is an example of fine grained parallelism because the design


has frequent interaction between the stages of the pipeline. The data parallel way of

doing this is by letting a single thread do the task done by the entire pipeline but let

different threads work on different data concurrently. This is an example of coarse

grained parallelism since the interaction between the stages are infrequent.

In most of the cases the pipeline stages will not be doing work of the same com-

putationally complexity, some stages may take much longer time to perform its task

and some less. Mixing up data parallelism and task parallelism together gives you a

good solution to this problem. The computationally intensive stages can be run by

multiple threads doing the same work concurrently over different sets of data. As seen

in Figure 2.6 it introduces parallelism within each stage of the pipeline and makes the

throughput of the computationally intensive stages better.

Figure 2.6: Using the Hybrid approach. Multiple workers working on multiple data in

stage 2.

2.4 Intel Threading Building Blocks

Threading Building Blocks is a library that supports scalable parallel programming on

standard C++ code. This library does not need any additional language or compiler

support and can work on any processor or operating system that has a C++ compiler

[2]. Intel Threading Building Blocks implement most of the common iteration pat-

terns using templates and thus the user does not have to be a threading expert knowing

the details about synchronisation, cache optimisations or load balancing. The most

important feature of the library is that you just need to specify task to be performed

and nothing about the threads. The library itself does the job of mapping task onto

the threads. Threading Building Blocks supports nested parallelism that allowed larger

parallel components to incorporate smaller parallel components in it. Threading Build-


ing Blocks also allows scalable data parallel programming.

Threading Building Blocks provide a task scheduler which is the main engine that

drives all the templates. The scheduler maps the tasks that you have created onto the

physical threads. The scheduler follows a work stealing scheduling policy. When the

scheduler maps task to physical threads they are made non-preemptive. The physical

thread works on the task to which it is mapped until it is finished and it may perform

other task only when it is waiting on any child tasks or when there are not child task it

would perform the tasks created by other physical threads.

2.4.1 Threading Building Blocks Pipeline

In Threading Building Blocks the pipeline pattern is implemented using the pipeline

and filter classes. A series of filters represent the pipeline structure in threading build-

ing blocks. These filters can be configured to execute concurrently on distinct data

packets or to only process a single packet at a time.

Pipelines in Threading Building Blocks are organized around the notion that the

pipeline data represents a greater weight of memory movement costs than the code

needed to process it. Rather than “do the work, toss it to the next guy,” it’s more like

the workers are changing places while the work stays in place[7].

2.5 POSIX Threads

POSIX thread(pthread) is the extension of the already existing process model to in-

clude the concept of concurrently running threads. The idea was to take some process

resources and make multiple instances of them so that they can run concurrently within

a single process, the multiple instance of the resources that were made are the bare

minimum needed for the instance to execute concurrently[11]. Pthread provided the

programmers access to low level details in programming. Programmers sees this as a

powerful options were they manipulate low level details to the needs of the application

they are developing. But then the programmer has to handle many design issues while

developing application. The Native POSIX Thread Library is the software that allows

the Linux kernel to execute POSIX thread programs efficiently. The Thread schedul-

ing implementations differs on how threads are scheduled to run. The pthread API

provides routines to explicitly set thread scheduling policies and priorities which may

override the default mechanisms.

Chapter 3

Issues and Methodology

In this chapter we discuss about the different features of the Intel threading building

block library that we intend to evaluate and the methodology that we would use to

evaluate them. We also discuss the methodology used to evaluate the library in terms

of usability, performance and expressibility. The initial phase of the project is to under-

stand the pipeline application development in TBB and to know what are the features

that TBB has to offer so that we can test those features for our evaluation and analyse

to see if these are really useful during pipeline application development.

3.1 Execution modes of the filters/stages

Many key features in the Threading Building Blocks library is worth noting and tested.

As we already discussed in the earlier chapter, Threading Building Blocks imple-

mented pipelines using the pipeline and the filter class. This filter class could be

made to run parallel, serially in order or serially out of order. When you

set the filter to run serially in order, the stage will run serially and would process

each input in the same order as they came into the input filter. Setting the filter to

serial out of order make the stages run in parallel but then the order of the input

data may not be maintained. The filters can also be set to parallel by which each stage

can work concurrently in a data parallel way, by this the same operation will be per-

formed on different data in parallel. These three filter modes are implemented and seen

if it provides any favourable results. Pthread applications having parallel stages are to

be designed so that a comparative analysis can be done. A performance analysis will

be done by calculating the speedup of the application.

14

Chapter 3. Issues and Methodology 15

3.2 Setting the number of threads to run the application

Another feature of the Threading Building Blocks is to run the pipeline by manually

setting the number of threads to run the pipeline with. We need to test if this facility

of manually deciding the number of threads to work on the implementation is a good

option provided by the library. If the user decides not to set the number of threads, then

the library sets the value for the number of threads which is usually the same as the

number of physical threads/processors in the system. This TBB philosophy[2], of hav-

ing one thread for each available concurrent execution unit/processor, is put under test

and checked if it is efficient for the different pipeline applications developed. Each of

the application is run with different number of threads which made us understand how

it helped in increasing the performance of the application. We also test if setting the

number of threads manually is more beneficial than letting the library set it automati-

cally. The results obtained from the automatic initialisation of the number of threads

is compared with the results obtained with the manual initialisation of the number of

threads.

Pthread application which runs with varying number of threads working on it is

checked if it can give better performance results that the threading building block coun-

terpart. Performance is measured in terms of speedup of the applications.

3.3 Setting an upper limit on the number of tokens in

flight

Threading Building Blocks gives you the feature to set an upper limit on the number

of tokens in flight on the pipeline. The number of tokens in flight is the number of data

items that is running through the pipeline or in other words the number of data sets that

are being processed in the pipeline at a particular instance of time. This controls the

amount of parallelism in the pipeline. In serial in order filter stages this will not

have an effect as the tokens coming in are executed serially in order. But in the case

of parallel filter stages there can be multiple tokens that can be processed by a stage

in parallel, so if the number of tokens in pipeline is not kept under check then there

can be a case where there can be excessive resource utilisation by the stage. There

might also be a case where the following serial stages may not be able to keep up with

the fast parallel stages before it. The pipeline’s input filter will stop pushing in tokens

once the number of tokens in flight reaches this limit and will continue only when


the output filter has finished processing the elements. Each of the application is to be

run with different number of token limits which tells us how it helped in increasing

the performance of the application. Performance here is measured in terms of the

speedup of the application. Similar feature is implemented in the pthread applications

and analysed.

3.4 Nested parallelism

Threading Building Blocks supports nested parallelism by which you can nest small

parallel executing components inside large parallel executing components. It is dif-

ferent from running the stages with data parallelism. With nested parallelism it is

possible to run in parallel the different processing for a single token within a stage.

So in our pipeline implementation we would incorporate nested parallelism in these

pipelines but incorporating parallel constructs like parallel for within the stages of

the pipeline and see if its useful to the overall implementation. It will help understand

how different it is from the option of running a stages in parallel by setting the filter

to run in parallel. A series of performance tests are done to understand concurrently

running filters and nested parallelism in the stages. The results tell us which is more

efficient for the pipeline application development.

3.5 Usability, Performance and Expressibility

The next step was to decide apt pipeline applications by which the various features

provided by the Threading Building Blocks library can be evaluated. Application of

various types are taken with varying input size and with varying complexity of the

computation it has to perform. Applications with different pipeline patterns are taken

into consideration to understand the expressibility of the library in comparison to the

pthread version. Implementation of the Linear and Non-Linear pipelines need to be

compared with the pthread versions in terms of the performance of the applications and

how easy it is for the user to make enhancements or changes into the program without

actually changing much in the design of the program. Scalability is another factor that

needs to be considered, it should to be understood to see if the same program gave

proportional performance when ran with more or less number of processors. Usability

of the languages is analysed all through the process of the software development life

cycle by putting ourselves in the shoes of parallel software programmer who can be


new to parallel programming or a can be a threading expert.

Pthread applications are developed so that they perform same as the threading

building blocks applications following the same structure of the pipeline and com-

putation complexity of the algorithm. This is done so that there is fair comparison of

the application developed in both the libraries.

Chapter 4

Design and Implementation

In this chapter we discuss in detail about the designing and implementation issues

during the development of various applications developed in both the parallel pro-

gramming libraries. Designing an application is a very important phase in the software

development life cycle and an easy designing phase will really help a programmer

build applications faster. The designing phase in this project will help understand the

pros and cons of the abstraction that the threading building blocks library provides. So

carefully analysing the designing efforts put in by a programmer due to the flexibil-

ity and the constraints the programming library provides, we can evaluate threading

building blocks library. Implementing these designs is a challenging task. During the

implementation phase the expressibility of the two parallel libraries will be understood

and how the various features provided by the library is helpful in implementing the

intended design of application will be understood. During this phase we primarily un-

derstand the usability and expressibility of the Threading Building Blocks library in

comparison to the pthread library. As a neophyte to TBB we found it very easy to un-

derstand the pipeline and filter class in the library and was quickly able to implement

applications in it.

4.1 Selection of Pipeline Applications

For evaluating TBB we need apt applications that would bring out the pros and cons

of the library. For this we used StreamIt1 benchmarks suite. The StreamIt[19] bench-

marks is a collection of streaming applications. Here the applications are developed

using the StreamIt language so it can not be directly used for our purpose. These ap-

1http://groups.csail.mit.edu/cag/streamit/

18

Chapter 4. Design and Implementation 19

Application Computational Complexity No. of stages Pattern

Bitonic Sorting Network Low 4 Linear

Filter Bank Average 6 Linear

Fast Fourier Transform High 5 Non-Linear

Table 4.1: Applications selected for the evaluation.

plications need to be coded in Threading Building Blocks and pthread for our purpose.

The selection of the applications are done such that they vary in their computa-

tional complexity, pattern of the pipeline, number of stages in the pipeline and input

size. Because in Intel threading building blocks we cannot determine the number of

stages in the pipeline during runtime thus we could not include applications like the

Sieve of Eratosthenes [20] where the number of stages was determined during the run-

time whereas this was possible in pthread[4]. This was one of the drawback found with

Intel threading building blocks during this phase of the project. The final set of appli-

cations selected were Fast Fourier Transform kernel, Filter bank for multi-rate signal

processing and Bitonic sorting network as seen in Table 4.1.

4.1.1 Fast Fourier Transform

The coarse-grained version of Fast Fourier Transform kernel was selected. The im-

plementation was a non-linear pipeline pattern with 5 stages. Fast Fourier Transform

is done on a set of n points which is one of the inputs given to the program. An-

other input given to the program is the permuted roots-of-unity look-up table which

is an array of first n/2 nth roots of unity stored in a permuted bit reversal order. The

Fourier Fourier implementation done here is a Decimation In Time Fast Fourier

Transform with input array in correct order and output array in bit-reversed order.

Details about Decimation In Time Fast Fourier Transform can be seen at [3].

The only requirement in the implementation is that n should be a power of 2 for it to

work properly.

4.1.2 Filter bank for multi-rate signal processing

An application that creates a filter bank to perform multi-rate signal processing was

selected. The coefficients for the sets of filters are created in the top-level initialisation

function, and passed down through the initialisation functions to filter objects. On each


branch, a delay, filter, and down-sample is performed, followed by an up-sample, delay

and filter [1].

4.1.3 Bitonic sorting network

An application that performs bitonic sort was selected from the StreamIt benchmark

suite. The program does high performance sorting network ( by definition of sorting

network, comparison sequence not data-dependent ). Sorts in O(n∗log(n)2) comparisons[1].

4.2 Fast Fourier Transform Kernel

4.2.1 Application Design

The Fast Fourier Transform kernel implementation was a 5 stages pipeline with struc-

ture as shown in Figure4.1.

Figure 4.1: Structure of the Fast Fourier Transform Kernel Pipeline.

The intended design of the pipeline is as in Figure 4.1. Stage 1 is the input signal

generator which generates the set of n points and stores it in two arrays. Stage 2

generates the set of Bit-reversal permuted roots-of-unity look-up table which is an

array of first n/2 nth roots of unity stored in a permuted bit reversal order. Both of

these stages generates the arrays on the run and not reading from a file so as to avoid

the overhead due to the I/O which may over shadow the performance of the pipeline.

Stage 3 is the Fast Fourier transform stage where the Decimation In Time Fast

Fourier transform with input array in correct order is computed. Stage 4 is where

the output array in bit-reversed order is created and passed on to the last stage in the

pipeline where the output is shown to the user.


4.2.2 Implementation

4.2.2.1 Threading Building Blocks

In the Threading Building Blocks implementation the pipeline was implemented as in

Figure 4.2.

Figure 4.2: Structure of the Fast Fourier Transform Kernel Pipeline implemented in TBB.

The implementation had a class that represented the data structure that is passed

along the different stages of the pipeline. The implementation of the class is shown in

Listing 4.1.

Listing 4.1: Data structure representing Tokens in the Fast Fourier Transform Kernel

Application�1 c l a s s d a t a o b j

2 {3 p u b l i c :

4 d ou b l e ∗A re ;

5 d ou b l e ∗A im ;

6 d ou b l e ∗W re ;

7 d ou b l e ∗W im ;

8 s t a t i c i n t n ;

9 s t a t i c d a t a o b j ∗ a l l o c a t e ( i n t n )

10 {11 n= n ;

12 d a t a o b j ∗ t =( d a t a o b j ∗ ) t b b a l l o c a t o r <char >() . a l l o c a t e ( s i z e o f ( d a t a o b j

) ) ;

13 t−>A re = ( do ub l e ∗ ) t b b a l l o c a t o r <double >() . a l l o c a t e ( s i z e o f ( do ub l e )

∗ n ) ;

14 t−>A im = ( d ou b l e ∗ ) t b b a l l o c a t o r <double >() . a l l o c a t e ( s i z e o f ( do ub l e )

∗ n ) ;

15 t−>W re = ( d oub l e ∗ ) t b b a l l o c a t o r <double >() . a l l o c a t e ( s i z e o f ( do ub l e )

∗ n / 2 ) ;


16 t−>W im = ( d ou b l e ∗ ) t b b a l l o c a t o r <double >() . a l l o c a t e ( s i z e o f ( do ub l e )

∗ n / 2 ) ;

17 r e t u r n t ;

18 }19 vo id f r e e ( )

20 {21 t b b a l l o c a t o r <double >() . d e a l l o c a t e ( t h i s−>A re , s i z e o f ( do ub l e ) ∗n ) ;

22 t b b a l l o c a t o r <double >() . d e a l l o c a t e ( t h i s−>A im , s i z e o f ( dou b l e ) ∗n ) ;

23 t b b a l l o c a t o r <double >() . d e a l l o c a t e ( t h i s−>W re , s i z e o f ( do ub l e ) ∗n / 2 ) ;

24 t b b a l l o c a t o r <double >() . d e a l l o c a t e ( t h i s−>W im , s i z e o f ( dou b l e ) ∗n / 2 ) ;

25 t b b a l l o c a t o r <char >() . d e a l l o c a t e ( ( c h a r ∗ ) t h i s , s i z e o f ( d a t a o b j ) ) ;

26 }27 } ;� �

The static function allocate function creates the instance of the class allocating

the memory required to perform Fast Fourier Transform on the n input points and

returns the pointer to the object created. The function free does the job to free the

allocated memory when the computations is done at the end of the pipeline and the

token is destroyed.

Stage 1 generates the input points at run time same as in the algorithm described

in the benchmark suite and stores the values in the data structure dataobj as array

A re and A im and passed on to the stage 2. Stage 2 creates the roots-of-unity look-up

table which is stored in the W re and W im array. From the data structure with array

A and W is passed to the Fast Fourier transform stage where values are computed and

stored in array A itself and passed on to the next stage. Stage 4 finds the bit-reversed

order of array A and passes it to the next stage to output values. The pipeline made

was a linear pipeline which implements the same logic of the original algorithm. The

computation of each stage was written in the overloaded operator()(void*) function of

that classes representing different stages of the pipeline. Each of these classes inherit

the filter class.

The pointer that the overloaded operator()(void*) function returns is the pointer to

the token that has to be passed on to the next stage in the pipeline. So this imposed

a restriction of having the need to represent all the components of a single token as a

single data structure so that it can be passed along the stages in the pipeline in threading

building blocks.


4.2.2.2 Pthread

The pthread implementation has the same pipeline structure as the original pipeline

taken from benchmark suite. The implementation has two input stages in the pipeline

joining at stage 3 and then having stage 4 and 5 following linearly. The overall pipeline

structure is defined as shown in Listing 4.2.

Listing 4.2: Data structure representing a pipe in the Fast Fourier Transform Kernel

Pthread Application�1 s t r u c t p i p e t y p e {2 p t h r e a d m u t e x t mutex ; /∗ Mutex t o p r o t e c t p i p e d a t a ∗ /

3 s t a g e t ∗ head1 ; /∗ F i r s t head ∗ /

4 s t a g e t ∗ head2 ; /∗ Second head ∗ /

5 s t a g e t ∗ t a i l ; /∗ F i n a l s t a g e ∗ /

6 i n t s t a g e s ; /∗ Number o f s t a g e s ∗ /

7 i n t a c t i v e ; /∗ A c t i v e d a t a e l e m e n t s ∗ /

8 } ;� �Here the mutex variable is used to obtain the lock over the pipeline information

variables(stages and active) and protect it during concurrent access. The variables

head1 and head2 are pointers to the two heads of the pipeline respectively. The vari-

able tail is the pointer to the last stage in the pipeline, stages being the count of

the number of stages and active being the count of number of tokens active in the

pipeline.

In the present pipeline structure since we have two kinds of stages, the stages are

represented by two kinds of structures. The structure that represent the stages that

receive input from a single stage and pass the token to a single stage is as shown in

Listing 4.3.

Listing 4.3: Data structure representing a stage type-1 in the Fast Fourier Transform

Kernel Application�1 s t a g e t y p e {2 p t h r e a d m u t e x t mutex ; /∗ P r o t e c t d a t a ∗ /

3 p t h r e a d c o n d t a v a i l ; /∗ Data a v a i l a b l e ∗ /

4 p t h r e a d c o n d t r e a d y ; /∗ Ready f o r d a t a ∗ /

5 i n t d a t a r e a d y ; / ∗ Data p r e s e n t ∗ /

6 d ou b l e ∗ A re ; /∗ Data t o p r o c e s s ∗ /

7 d ou b l e ∗ A im ; /∗ Data t o p r o c e s s ∗ /

8 d ou b l e ∗ W re ; /∗ Data t o p r o c e s s ∗ /

9 d ou b l e ∗ W im ; /∗ Data t o p r o c e s s ∗ /


10 p t h r e a d t t h r e a d ; /∗ Thread f o r s t a g e ∗ /

11 s t r u c t s t a g e t y p e ∗ n e x t ; /∗ Next s t a g e ∗ /

12 } ;� �The mutex variable is used to protect the data in the pipeline stage. The vari-

ables avail and ready are conditional variables, the avail variables indicates to the

pipelines stage that there is data ready for it to consume/process. The ready condi-

tional variable is an indicator to the earlier stage that the stage has finished the pro-

cessing the data and ready to receive new data to process. data ready is an integer

variable indicating to the data sending process that the data is still now consumed in the

receiving process. The structure also includes the data items that are to be processed

by the stage, here these are pointers to the memory location that contains the data.

There is also a pthread t variable which is the thread to process the stage. The last

variable next points to the next stage in the pipeline structure. In the implementation

this structure is used to represent stages 1, 2, 4 and 5 in Figure 4.1. The structure that

represent the stage that receives tokens from two stages and send tokens to only single

stage is as in Listing 4.4.

Listing 4.4: Data structure representing a stage type-2 in the Fast Fourier Transform

Kernel Application�1 s t r u c t s t a g e t y p e {2 p t h r e a d m u t e x t mutex ; /∗ P r o t e c t a r r a y A ∗ /

3 p t h r e a d c o n d t a v a i l ; /∗ Array A a v a i l a b l e ∗ /

4 p t h r e a d c o n d t r e a d y ; /∗ Ready f o r Array A ∗ /

5 p t h r e a d m u t e x t mutex1 ; /∗ P r o t e c t a r r a y W ∗ /

6 p t h r e a d c o n d t a v a i l 1 ; /∗ Array W a v a i l a b l e ∗ /

7 p t h r e a d c o n d t r e ad y1 ; /∗ Ready f o r Array W ∗ /

8 i n t d a t a r e a d y ; /∗ Array A p r e s e n t ∗ /

9 i n t d a t a r e a d y 1 ; / ∗ Array W p r e s e n t ∗ /

10 d ou b l e ∗ A re ; /∗ Data t o p r o c e s s ∗ /

11 d ou b l e ∗ A im ; /∗ Data t o p r o c e s s ∗ /

12 d ou b l e ∗ W re ; /∗ Data t o p r o c e s s ∗ /

13 d ou b l e ∗ W im ; /∗ Data t o p r o c e s s ∗ /



16 } ;� �The structure has two sets of mutex, avail, data ready and ready variable for

the two stages that try to send data to this stages. By doing so, the granularity of

locking is lowered and allows the two stages to work independently of each other.


This implementation works because both the stages write into different locations in the

data structure. This structure is used to implement stage 3 in the pipeline in Figure 4.1.

Stage 1 generates the input points at run time same as in the algorithm described

in the benchmark suite and sends pointer to arrays A re and A im to stage 3. Stage

2 creates the roots-of-unity look-up table in parallel to stage 1, stores it in the W re

and W im array and passes the pointers to stage 3. Stage 3 on receiving these values

performs the Fast Fourier Transform of these values and send it to stage 4 where the

bit reverse order of the array is created and passed on to the last stage for output.

The passing of tokens in the pthread application was done using the function as in

Listing 4.5. Here initially the thread tries to attain lock to write into the buffer of the

next stage, then it waits on a condition variable ready that tells the thread when the next

stage thread is ready to accept new tokens. After copying the values of the token to the

buffer of the next stage, the thread signals the avail condition variable telling the next

stage thread that the new token is ready to be processed. Variations of this function

was used to send tokens with different contents.

Listing 4.5: Function to pass a token to the specified pipe stage.�1 i n t p i p e s e n d ( s t a g e t ∗ s t a g e , dou b l e ∗A re , do ub l e ∗A im , d ou b l e ∗

W re , do ub l e ∗W im )

2 {3 i n t s t a t u s ;

4

5 s t a t u s = p t h r e a d m u t e x l o c k (& s t a g e−>mutex ) ;

6 i f ( s t a t u s != 0 )

7 r e t u r n s t a t u s ;

8 /∗9 ∗ I f t h e p i p e l i n e s t a g e i s p r o c e s s i n g da t a , w a i t f o r i t

10 ∗ t o be consumed .

11 ∗ /

12 w h i l e ( s t a g e−>d a t a r e a d y ) {13 s t a t u s = p t h r e a d c o n d w a i t (& s t a g e−>ready , &s t a g e−>mutex ) ;

14 i f ( s t a t u s != 0 ) {15 p t h r e a d m u t e x u n l o c k (& s t a g e−>mutex ) ;


17 }18 }19

20 /∗21 ∗ Copying t h e d a t a t o t h e b u f f e r o f t h e n e x t s t a g e .


22 ∗ /

23 s t a g e−>A re = A re ;

24 s t a g e−>A im = A im ;

25 s t a g e−>W re = W re ;

26 s t a g e−>W im = W im ;

27 s t a g e−>d a t a r e a d y = 1 ;

28 s t a t u s = p t h r e a d c o n d s i g n a l (& s t a g e−>a v a i l ) ;

29 i f ( s t a t u s != 0 ) {30 p t h r e a d m u t e x u n l o c k (& s t a g e−>mutex ) ;


32 }33 s t a t u s = p t h r e a d m u t e x u n l o c k (& s t a g e−>mutex ) ;


35 }� �4.3 Filter bank for multi-rate signal processing


The application design is a 6 stage linear pipeline. The stage 1 is the input generation

stage that creates an array of signal values. The input signal is then convoluted with the

first filter’s coefficient matrix in stage 2. The signal is then down-sampled in stage 3

and then up-sampled in stage 4. The signal is then passed on to the next stage where the

signal is convoluted with the second filter’s co-efficient matrix. the values are added

up the into an output array until an algorithmically determined number of token arrive

and then the values are output.



The implementation of the pipeline in Threading building blocks has the same structure

as the original intended pipeline.The token passed between the stages are arrays which

are dynamically allocated using the tbb allocator at each stage in the pipeline.The input

signal is generated in stage 1 is put in an array and passed to stage 2 in the pipeline.

Stage 2 does the convolution of the signal with the filter coefficient matrix. The convo-

lution matrix is created during the initialisation phase of the program. The convoluted

values are stored in an array and passed on to stage 3 where it is down-sampled and


then up-sampled in stage 4, each time allocating new arrays to hold the new processed

values and then passed on to the next stage. Stage 5 does the convolution of the signal

with the second filter co-efficient matrix that is created during the initialisation phase

of the program. Finally in stage 6 the signal values are added up into an array until a

predetermined number of tokens arrive, after which the values are output. The send-

ing of tokens was done in the same way as it was done in the case of Fast Fourier

Transform Kernel.

4.3.2.2 Pthread

The pipeline implemented in pthread is same as the intended pipeline structure having

6 stages and performing the same functions as discussed for the threading building

blocks version. Since the pattern is a linear pipeline and the stages have the same

structure, that is each have a single source for tokens and single recipient for the tokens

the structure of the stages are the same and is represented as a structure shown in

Listing 4.6.

Listing 4.6: Data structure representing a stage in the Filter bank for multi-rate signal

processing application�1 s t r u c t s t a g e t y p e {2 p t h r e a d m u t e x t mutex ; /∗ P r o t e c t d a t a ∗ /



5 i n t d a t a r e a d y ; /∗ Data p r e s e n t ∗ /

6 f l o a t ∗ d a t a ; /∗ Data t o p r o c e s s ∗ /



9 } ;� �Here you have the mutex variable to protect the data in the stage. The avail and

ready condition variables to indicate the availability of the data for processing and the

readiness of the stage to accept new data for processing. The structure also has the

pointer to the data item and variables for the thread that processes the stage and the

pointer to the next stage in the pipeline structure.

The overall pipeline is defined with the structure as shown in Listing 4.7 having

a mutex variable which is used to obtain a lock over the pipeline information vari-

ables(stages and active). The head and tail pointers point to the first and last stage

in the pipeline. The variables, stages and active maintain the count of the number of


stages and the number of tokens in the pipeline. The sending of tokens to the next

stage was done using the function as in 4.5 except for the difference in the data that is

copied to buffer of the next stage.

Listing 4.7: Data structure representing the pipeline in the Filter bank for multi-rate

signal processing application�1 s t r u c t p i p e t y p e {2 p t h r e a d m u t e x t mutex ; /∗ Mutex t o p r o t e c t p i p e ∗ /

3 s t a g e t y p e ∗ head ; /∗ F i r s t s t a g e ∗ /

4 s t a g e t y p e ∗ t a i l ; /∗ L a s t s t a g e ∗ /

5 i n t s t a g e s ; /∗ Number o f s t a g e s ∗ /

6 i n t a c t i v e ; /∗ A c t i v e d a t a e l e m e n t s ∗ /

7 } ;� �The Filter Bank application was redesigned to implement the stages with data par-

allelism. This included the addition of a shared memory data structure were all the

threads working in a stage can access the tokens to be processed. The shared mem-

ory data structure is as shown in Listing 4.8. One instance of this data structure is

shared between all the threads working in a particular stage. The functionality of the

components are same the one discussed for Listing 4.6.

Listing 4.8: The Shared Memory data structure for the threads working in the same

stage�1 t y p e d e f s t r u c t s h a r e d d a t a {2 p t h r e a d m u t e x t mutex ; /∗ P r o t e c t d a t a ∗ /



5 i n t d a t a r e a d y ; /∗ Data p r e s e n t ∗ /

6 f l o a t ∗ d a t a ; /∗ Data t o p r o c e s s ∗ /

7 } shared mem ;� �4.4 Bitonic sorting network


The bitonic sorting network application taken was a 3 stage pipeline was a 4 stage

pipeline. Stage 1 for the input generation, Stage 2 for the creation of the bitonic se-

quence from the input values and then stage 3 for the sorting of the bitonic sequence


and the final stage to output the sorted values. The application has been altered from

the original to benchmark suite design to incorporate varied size inputs. The applica-

tion is designed to sort many fixed size arrays of numbers. Initially the application was

designed to read values from a file which was sorted and then written into an output

file which was later changed due to reasons that we would discuss in the later part of

the report. So stage 1 and stage 4 was initially designed to read values from an input

file and to write values into an output file respectively.



The implementation is same as the intended pipeline design having 4 pipeline stages

and having a linear pipeline structure. The stage 1 input filter class generates a set of

randomly generated numbers and passes on to the next stages. The class also maintains

a count of the number of input tokens generated and stops when a required limit is

reached. The stage 2 class implements the logic for the bitonic sequence generation

from the array received from the input generation class. Here the computation involves

only comparing and swapping of values. The stage 3 implements the merge phase of

the bitonic sequence and does almost the same amount of computation as stage 2 in

terms if number of comparison and swaps. The final stage is where the sorted values are

output. The sending of data in all the above cases involves only the passing on pointers

that point to the array of numbers. The passing tokens to the next stages involved

passing of the pointers to the array of data values which was done returning the token

pointer in the overloaded operator()(void*) function in the classes representing the

stages. In the initial implementation of the application the input was read from a file

rather than generating the input during the execution. The initial implementation was

changed due to reasons we discuss in the evaluation section. The data structure to

represent tokens was initially represented as a circular buffer of fixed size. Each array

in the buffer was filled with input values and passed on to the next stage. This can be

seen in Listing 4.9. Here buff is the circular buffer of arrays. It has to be ensured in

the implementation that the number of tokens in flight is not more than SIZE which is

easily possible in threading building blocks.

Listing 4.9: The Circular buffer to hold tokens�1 c l a s s I n p u t F i l t e r : p u b l i c t b b : : f i l t e r {2 B u f f e r b u f f [ SIZE ] ;


3 s i z e t n e x t B u f f e r ;

4

5 p u b l i c :

6 vo id ∗ o p e r a t o r ( ) ( vo id ∗ ) ;

7 I n p u t F i l t e r ( ) : f i l t e r ( s e r i a l i n o r d e r ) , n e x t B u f f e r ( 0 ) {8 }9 ˜ I n p u t F i l t e r ( ) {

10 }11 vo id Token ize ( c o n s t s t r i n g& s t r , v e c t o r<s t r i n g >& tokens , c o n s t

s t r i n g& d e l i m i t e r s = ” ” ) ;

12 } ;

13

14 vo id I n p u t F i l t e r : : Token ize ( c o n s t s t r i n g& s t r , v e c t o r<s t r i n g >& tokens

, c o n s t s t r i n g& d e l i m i t e r s = ” ” )

15 {16 s t r i n g : : s i z e t y p e l a s t P o s = s t r . f i n d f i r s t n o t o f ( d e l i m i t e r s , 0 ) ;

17 s t r i n g : : s i z e t y p e pos = s t r . f i n d f i r s t o f ( d e l i m i t e r s , l a s t P o s

) ;

18 w h i l e ( s t r i n g : : npos != pos | | s t r i n g : : npos != l a s t P o s ) {19 t o k e n s . p u s h b a c k ( s t r . s u b s t r ( l a s t P o s , pos − l a s t P o s ) ) ;

20 l a s t P o s = s t r . f i n d f i r s t n o t o f ( d e l i m i t e r s , pos ) ;

21 pos = s t r . f i n d f i r s t o f ( d e l i m i t e r s , l a s t P o s ) ;

22 }23 }24

25 vo id ∗ I n p u t F i l t e r : : o p e r a t o r ( ) ( vo id ∗ ) {26 s t r i n g l i n e ;

27 s t a t i c f s t r e a m i n p u t f i l e ( Inpu tF i l eName ) ;

28 v e c t o r<s t r i n g > t o k s ;

29 i f ( g e t l i n e ( i n p u t f i l e , l i n e ) ) {30 B u f f e r &b u f f e r = b u f f [ n e x t B u f f e r ] ;

31 n e x t B u f f e r = ( n e x t B u f f e r +1)%SIZE ;

32 Token ize ( l i n e , t o k s ) ;

33 f o r ( i n t y =0; y<t o k s . s i z e ( ) ; y ++)

34 {35 b u f f e r . a r r a y [ y ]= a t o i ( t o k s [ y ] . c s t r ( ) ) ;

36 }37 r e t u r n &b u f f e r ;

38 }39 e l s e {40 r e t u r n NULL;

41 }


42 }� �4.4.2.2 Pthread

The pthread implementation has the same intended linear pipeline design with 4 stages.

The structure of the pipeline stage is similar to Listing 4.6. It contains two condition

variables used to synchronise the sending and receiving of data and the mutex variable

to protect the data in the pipeline. It contains the pointer data that points to the array

and the another pointer next pointing to the next stage in the pipeline which is used

during sending of data.

The main structure of the pipeline is similar to Listing 4.7.The structure contains

the pointer to the head and tail stage of the pipeline structure and also has a mutex

variable to protect the pipe information data. The variable stages and active con-

tains the information about the pipeline like the number of stages in the pipelines and

number of tokens in flight in the pipeline respectively. The sending of tokens to the

next stage was done using the function as in Listing 4.5 except for the difference in the

data that is copied to buffer of the next stage.

Chapter 5

Evaluation and Results

In this chapter we discuss the evaluation done on the threading building blocks library.

We evaluate the usability and expressibility of the two parallel programming libraries

and also the performance of the applications developed using those libraries. The

different features in threading building blocks library is evaluated and a comparative

analysis is done by implementing these features in the pthread applications developed.

We start with the discussion about the evaluation and the results obtained for the

three pipeline applications implemented. Here we discuss the usability and express-

ibility issues faced during each application development and also do a performance

analysis of the applications. We then discuss about the results and evaluation of the

different features provided by threading building blocks.

We ran various applications on multi-core machines and measured the speedup of

the different applications developed. The experiments were run on 16-core and 2-

core machines. The 2-core machine used had Intel(R) Xeon(TM) 3.20GHz processors

with 2 GB RAM and the 16-core machine had Intel(R) Xeon(R) 2.13GHz processors

with 63 GB RAM. Most of the experiments are carried out on the 16-core machine.

Executions done on the 2-core machines have been explicitly mentioned.

5.1 Bitonic Sorting Network

The evaluation of threading building blocks started with the bitonic sorting network

application. This was challenging in many ways because it was the first pipeline appli-

cation we were developing using the library. As a newcomer to the threading building

blocks library we had the initial usual difficulties until we were actually used to the

constructs and the features that the library provided.

32

Chapter 5. Evaluation and Results 33

5.1.1 Usability


During the initial programming phase the challenging part was to create the right data

structure that we would use to represent tokens and pass them efficiently across stages.

We had tried many data structures before we actually decided on one. One of the

data structures tried had a large array which was divided into n buffers of data that

represented a token in the pipeline which had to be sorted. The size of the large array

was fixed and could incorporate only a fixed number of tokens. The input filter would

fill these buffers one after the other and then pass it on to the next stage for processing.

After all the n buffers were filled, it would start again by filling in the first buffer. This

implementation worked because we were able to limit the number of tokens in flight

using the Threading Building Blocks library and also for the fact that we could program

the stages to run sequentially and process the tokens in a fixed order. By limiting the

number of tokens in flight to n it was ensured that when the input filter comes to the

next round of filling up of the large array starting from the first buffer, the first buffer

in the previous round had already finished processing at the last stage of the pipeline.

Making the stages run serially and to process the tokens in the same order as created

by the input filter also ensured that the buffers that had not finished processing, will

not be overwritten with new values. This implementation was later changed since there

was an intention to experiment by changing the number of tokens in flight in pipeline

and hence this would not be the best design suited for the purpose. But then we could

not fail to notice that since the threading building blocks library provided features like

limiting the number of tokens in flight and to make the stages run sequentially and to

perform in order processing of the tokens easily allowed us to implement a design like

this.

As for the implementation of the computational part of performing the bitonic sort

was concerned we just had to implement the C++ serial code for each stage and place

them in the overloaded operator()(void*) function in the respective classes that

represented the stages. As soon as we got the right data structure for the tokens that

are passed along the pipeline and implemented the computational task done by each of

the stages we had been able to implement a correctly working pipeline without much

hassle. As a parallel programmer we did not have to bother anything about the low

level threading concepts like synchronising, load balancing or cache efficiency.


5.1.1.2 POSIX Thread

Bitonic Sorting Network being our first pipeline application in pthread we had to get

a lot of concepts thorough before we actually started writing the program. We had to

understand in detail about the use of thread spawning, mutexes and condition variables

for synchronising the threads, inter thread communications etc. The most important

challenge during the application development in pthread was to get right design for

the pipeline. It was a difficult task because of the amount of the implementation detail

we needed to handle. We had to fix the structure of the pipeline so that they correctly

send and receive data between the threads handling each stage which included the

use of mutexes to protect the critical section and the use of condition variable for the

synchronisation of sending and receiving data. Getting the right design was the most

difficult task but then there were many difficulties we faced during the later develop-

ment stages. To implement a feature like limiting the count of tokens in flight we had

to include a counter in the design that kept the count and had to include mutexes for

the access of the counter variable.

The work that had to be done by each stage was written in separate function and

had to be assigned to each thread during the thread initialisation. During the first exe-

cution of the program we had discovered a few errors and debugging a multi-threaded

application was not an easy task. It was really hard to determine if the errors were due

to the improper synchronisation of threads or due to the wrong computational logic

implemented. There were many cases in which the program could go wrong and iden-

tifying them was really hard.

5.1.2 Expressibility


As for the expressibility of the threading building blocks library we had no issue im-

plementing the desired design of the pipeline. We could easily set the input and output

stages to work sequentially when the data was read and written into files. We could

easily write data into the output file in the same order they were read from the input file

by just passing the serial in order keyword to the inherited filter class constructor

during the constructor call of the classes that represented the input and output stages.

We were able to run the middle stages of the pipeline in parallel(Data parallelism)

just by passing the keyword parallel to the filter class constructor. When we imple-


mented the design where we had to restrict the number of tokens in flight, we were

easily able to do so because the library gave us the feature to set the maximum limit by

passing it to the run() function in the pipeline class.


In the pthread implementation we were able to implement the intended design for the

application. We could limit the number of tokens in flight by including a counter in

the design that maintained the count of the number of tokens in flight which was not

as easy as that in the case of threading building blocks.

5.1.3 Performance

The Bitonic sorting network application was initially developed with a design to read

the values to be sorted from files and then write the sorted values into another file. The

implementation was working fine except for a problem that when the performance of

the application was calculated it was noted that the run-time of the application varied

greatly for each execution. Initially the assumption was that the varied execution times

may be because the amount of computation in each stage was very less and must be

over shadowed by the overhead of synchronisation of the threads done internally by the

threading building blocks library. On detailed reading about Intel threading building it

was found that threading building block is not ideal for I/O bound application as the

threading building block task scheduler is unfair and non-preemptive [13]. Thus the

design of the application had to be changed for further tests.

By replacing I/O stages with the stage that generated input during the execution, the

application was ready to be evaluated. The speedup of the application was calculated in

its best configuration and it was found that the speedup of the application was less the

1. This could be because of the computational complexity of the application being low

and this was overshadowed by the synchronisation mechanism implemented internally

in threading building block library. The threading building block application was easily

scaled to a machine with different number of cores without the need to the change

anything in the code and the results were obtained as in Figure5.1.

The initial design of the pthread application with stages that reads and writes data

into files, the application gave steady results for execution times unlike the threading

building block version. Though the results were stable, the execution time of the appli-

cation was very high. Later, the pthread version of the application was designed with


Figure 5.1: Performance of the Bitonic Sorting application(TBB) on machines with dif-

ferent number of cores.

input generation stages that removed the overhead due to I/O operations in the pipeline.

On the performance analysis of the application it was noted that the pthread application

took very large amount of time to execute compared to the threading building blocks

version which can be seen in Figure5.2. The pthread application was also evaluated

on machine with different number of cores without any change in the code this would

show how a newcomer programmer can achieve good performance dependent on the

machine without much effort in threading building blocks. Results shown in Figure5.2

were obtained on evaluation.

Figure 5.2: Performance of the Bitonic Sorting application(pthread) on machines with

different number of cores.

The bad performance was assumed to be caused due to the synchronisation mecha-

nism implemented in the program and the stages being less computationally intensive.


To confirm this, a test was done by removing all the thread synchronisation mecha-

nisms in the application and calculating the run-time of the application. Though the

application was giving incorrect output values it could be understood from the run-time

if most of the time was taken for the synchronisation of threads in the application. The

results obtained are shown in Figure 5.3.

Figure 5.3: Performance of the Bitonic Sorting application(pthread) with and without

Locks.

It can be seen that there is a drastic reduction in the execution time of the applica-

tion with and without locks. So it was understood that the application being very less

computationally intensive, most of the threads were idle most of the time waiting on

locks to be released.

5.2 Filter bank for multi-rate signal processing

The Filter Bank application was the second application that was developed. With the

experience we attained with Bitonic sorting network application we could immediately

start the work with the second application because we had familiarised ourself with

both the parallel programming libraries and had a basic idea of how we would go

about designing and implementing a pipeline application in both pthread and threading

building blocks. The Filter bank application is more computationally intensive than the

bitonic sort application having to work on large signal arrays and large filter co-efficient

matrices. The pipeline had a longer chain with 6 stages in a linear pipeline structure.


5.2.1 Usability


The development of bitonic sorting network made us familiar with threading building

blocks due to which the Filter Bank application was developed much faster than what

we took for the bitonic sorting network. We just had to paste in the computation for

each stage into the operator() function of the appropriate classes. To create the right

data structure for the token movement in the pipeline was the only challenge in the

implementation.

As a parallel programmer we wanted to make our pipeline application run faster

and we were easily able to identify the bottleneck stages by using the serial in order,

serial out of order and parallel options in the filter classes and finding their speedup.

By using these options we were very easily able to tweak the application for the best

performance.


Similar to the case of Threading building blocks the bitonic sort application imple-

mented in pthread gave us a quick start because we had already figured out a generic

structure for the pipeline. With a few application dependent changes in the design we

were immediately ready to start with the implementation. Getting the right design for

the application was the toughest part in bitonic sorting network, which we were able

to get done with moderate ease for the filter bank application. The reuse of the design

made development easy for us but then it was not the same in the case of threading

building blocks because many of the design issues were abstracted by the library and

the only notable challenge in threading building blocks was to get the right data struc-

ture for the tokens.

With the bottleneck stages easily identified in the threading building block applica-

tion we were easily able to tweak our application for performance but then in the case

of the pthread application we had to find the single token execution time in each stage

to understand which were the bottleneck stages in the pipeline. This was comparatively

a tougher task than what we had to do for the threading building block application.

Since we had found out the bottleneck stages in the pipeline the next step was to run

the stage with data parallelism. Implementing the stages to run in parallel needed many

changes in the already implemented design of the pthread application. This redesign

though built up on the already existing design had many challenges. Because of the


cases where data had to be sent to many recipients and received from many senders.

Many issues like synchronisation of threads and efficiency had to be considered for

the right design. A lot of amount of time had to be spent on the redesign, testing and

debugging the application which was even more harder than in the case of sequential

stages whereas in the threading building block there was no need for redesigning the

application as we just had to pass an argument ‘parallel’ to the filter class constructor

to make the stages run in parallel. The idea of collapsing stages if needed was also very

easy in the case of threading building blocks. We just had to paste the computation of

the collapsed stages into one single class and that to without much change in the design

of the application.



In terms of expressibility TBB library provided us with all the needed features for the

implementation of the intended design of the application. It provided us with features

using which we could find the bottlenecks in the application and also run in paral-

lel those stages with great ease, thereby expressing both task and data parallelism.

Changes like collapsing of stages was also possible.


Pthread library provided the required flexibility to express the intended design for the

pipeline applications. The bottleneck stages were identified and we were also able

to run these stages in data parallel to make the implementation efficient. It was also

possible to collapse the stages for better load balance between different stages.This

was possible in both threading building blocks and pthread without much hassle.

5.2.3 Performance

Filter Bank application being a computationally intensive application and having no

I/O operations in it, showed no problems during the performance evaluation of the

application. The long chained pipeline application worked perfectly with threading

building blocks giving good speedup. The threading building blocks application was

easily scalable and gave good speedup results even when tested over different machines

with different number of cores as shown in Figure5.4.


Figure 5.4: Performance of the Filter Bank application(TBB) on machines with different

number of cores.

The pthread application was also able to give good speedup. The speedup obtained

can be seen in the Figure 5.5. Pthread application does not scale on its own like in the

case of threading building blocks. So to understand how pthread would work without

any change in the code, the application was run on machines with different number of

cores which gave the result as shown in Figure 5.5.

Figure 5.5: Performance of the Filter Bank application(pthread) on machines with dif-

ferent number of cores.

It can be seen that the speedup obtained in the threading building block versions is

much better than the pthread version which can be attributed to scheduler that threading

blocks uses and also the thread abstractions that the library provides.


5.3 Fast Fourier Transform Kernel

Fast Fourier Transform kernel was the third application that was developed to evaluate

threading building blocks pipeline. This application was particularly taken because of

the non-linear pipeline pattern that was required in the implementation. Its a 5 stage

pipeline performing a reasonably good amount of computation at each stage.

5.3.1 Usability


After implementing two applications in threading building blocks, the Fast Fourier

Transform kernel took only a few hours for us to implement. This is because of the

abstractions threading building blocks provided us. Since we already had the required

algorithm at the benchmark suite we just had to put in the code at the appropriate place.

We had the application up and working with just a few execution tries and found the

pipeline application development extremely fast and trouble free after implementing

this application.

Just like the earlier application implementations, the only phase that took some

time was to decide the correct and efficient data structure for the tokens. Even though

the pipeline application was a non-linear pipeline, designing the application was not

any different because the non-linear pipeline is implemented as a linear pattern in

threading building blocks. We just had to decide on the correct order of the stages

in the linear pipeline as the non-linear pattern was converted to a linear pattern and

put in the computation in the filter classes to implement the pipeline. From the pro-

gramming point of you it was not any different than implementing a linear pipeline.

So there were no extra usability issues implementing a non-linear pipeline in threading

building blocks in comparison to implementing a linear pipeline.


The experience working with the development of the previous two pthread application

helped developing the pthread version of Fast Fourier Transform kernel. But the Fast

Fourier Transform kernel having a non-linear pipeline pattern had demanded extra

attention into the design of the application. Because of the non-linear structure of the

pipeline the stages were not all the same. So the stages were represented using different

structure incorporating extra measures for thread synchronisation and access to shared


resources. Designing the right structure was not a simple task as in threading building

blocks. A lot of time had to be spent testing and implementing the correct design which

consumed a lot of time. Appropriate checks had to be done at the combining stages to

ensure the correct order of data was combined together. Lot of issues like these had

to be handled which made the application development a tougher task as compared to

threading building block where these issues did not come up. In pthread every phase

was tougher than that in the threading building blocks because pthread programming

required attention to a lot of low level details to implement an application.

Implementing a non-linear pipeline had its difficulties in pthread but then if the

application was implemented as a linear pattern just like it was done for the thread-

ing building blocks, then there we could easily overcome the troubles we went into

implementing the non-linear pattern.



The intended design for the application was a non-linear pipeline but then it was not

possible to implement it in threading building blocks because the library does not

support non-linear pipeline patterns. The work around for this is that the non-linear

pipeline has to be converted to a linear pipeline and then implemented with the library.

The expressibility of threading building blocks is flawed if the need is to implement a

non-linear pipeline.


Pthread library gives you the flexibility of implementing non-linear pipelines. The

Fast Fourier Transform application was developed with the intended design using the

pthread library. One of the good things in the pthread library because it lets the pro-

grammer work on such low details is the flexibility that it gives the programmer to

implement application the way he needs it and providing fewer library related restric-

tions.

5.3.3 Performance

The Fast Fourier Transform kernel application’s intended design was a non-linear

pipeline implementation and it was important to understand the performance of the


threading building blocks application in comparison to the pthread application because

threading building blocks does not support non-linear pipelines whereas pthread gives

the flexibility to implement them. Even though threading building blocks works around

the problem and implements the non-linear pipeline in a linear pattern it is necessary

to understand if the threading building blocks still gave the performance benefits it

gave like in the other application implemented. The linear pipeline version of the Fast

Fourier transform application was evaluated for performance which gave the results as

shown in Figure 5.6.

Figure 5.6: Performance of the Fast Fourier Transform Kernel application(TBB) on ma-

chines with different number of cores.

It can be seen that despite the inability of threading building block library to ex-

press non-linear pipelines, it gave really good speedup for the application which also

scaled well for the machines with different number of cores. The non-linear pipeline

implemented using the pthread version of the application gave good speedup when run

on machines with different cores but not as much as in threading building blocks. The

results are as shown in Figure 5.7.

It is observed that despite the flexibility that pthread offers to implement non-linear

pipeline the performance results of the of the pthread application is not better than

the threading building blocks performance results. In the 5 stage non-linear pipeline

the first two stages concurrently generate the data required to process a single token

which results in a single output set. In the threading building blocks version this was

implement linearly with stage 2 after stage 1 thereby not concurrently generating the

data needed to process a token. But then the pipeline starts to output the results in the

last stage at ever time interval which is equal to the execution time of the slowest stage


Figure 5.7: Performance of the Fast Fourier Transform Kernel application(pthread) on

machines with different number of cores.

in the pipeline. And this time interval is the same for both the linear and non-linear

implementation. The only difference that arises is in the latency of the pipeline, that is

the initial start up time for the pipeline before it starts to output data.

The Figure 5.8 explains how the latency varies for the linear and non-linear imple-

mentation of pipeline assuming that all stages take equal amount of time to execute.

This small difference in the latency of the pipelines does not make a huge difference

in the performance of the pipeline most of time because the pattern is used to process

large amount of data which takes lots of time to process. This time is very large com-

pared to the latency advantage the non-linear pipeline provides. But the priorities can

change depending on the application needs and there are many cases where latency of

the pipeline is a crucial factor.

5.4 Feature 1: Execution modes of the filters/stages

The selection of the mode of operation of the filters is one of the most powerful feature

in the pipeline implementation. The ease at which a programmer can set the way the

stages should work definitely facilitates in faster programming.

5.4.1 serial out of order and serial in order Filters

serial in order stages was used when there was the need for certain operation to be

done only by a single thread and also when the order in which the tokens are processed

is to be maintained. serial out of order stages was used in cases when the certain


Figure 5.8: Latency difference for linear and non-linear implementation assuming equal

execution times of the pipelines stages.

operations had to be performed serially and the order of processing of the tokens was

not important. But then there were not many cases observed where the output tokens

came out of order in any of our implementation using the serial out of order stages

even though there were parallel stages in between the serial out of order stages.

This might be because the processing time in the parallel stages for each and every

token was the same.

Running the stages in the serial out of order rarely gave performance benefits

compared to the serial in order stages even though serial in order stages intro-

duces some delays in between processing of tokens to ensure the tokens are processed

in the right order. This might again be because of the reason that the the processing

time in the middle parallel stages for each and every token was the same and thereby

most of the tokens reached the serial in order stage in order and there was rarely

any delay introduced by the filter to order the token processing.

The Filter Bank pthread application was redesigned to do a comparison test with the

serial in order and serial out of order filters developed in the threading build-

ing blocks library. Implementation of serial in order stages required the stages to

be run only by a single thread and to provide an additional information attached to the

tokens in flight that gave information on the order in which the tokens are to be pro-

cessed. This implementation needed a lot more work compared to that in the threading


building blocks library. Being ignorant of the order of tokens made the stages run

serially out of order execution of tokens.

5.4.2 Parallel Filters

The overall performance of a pipeline application is determined by the slowest se-

quential stage in the pipeline. If all the stages in the pipeline are parallel then its a

different scenario where performance depends on other factors. Making the slow bot-

tleneck stages run in parallel(Data Parallel) increases the throughput of the pipeline.

Thereby introducing both task and data parallelism in the pipeline design helps in

proper load balancing and achieve good performance. This performance benefits was

easily obtained wherever stages could be run with data parallelism and where the order

of tokens processed was not important. This can be seen from Figure 5.9. Significant

performance benefits were easily obtained when the application was developed in the

threading building blocks.

Figure 5.9: Performance of Filter bank application(TBB) for different modes of operation

of the filters.

The Filter Bank application having stages with data parallelism was implemented

in pthread to understand how the performance of the pthread application would in-

crease by running the bottleneck stages in data parallel and to know if it gives better

results than the threading building blocks library. After identification of the bottle-

neck stages, the stages were run in parallel with different number of threads. Results

obtained are as in Figure 5.10.

The results obtained here are very significant because you can see the pthread ver-

sion of the application out performing the threading building blocks version of the


Figure 5.10: Performance of Filter bank application with stages running with data par-

allelism.

application. The pthread version of the application gives good performance with fewer

threads. It should also be noted that only two bottleneck stages were designed to

run data parallel in the pthread application and still obtaining better results that the

threading building blocks version. This performance was not obtained by the thread-

ing building block library with the same thread count and even when all the stages

were run in parallel.

5.5 Feature 2: Setting the number of threads to run the

application

The Threading building blocks scheduler gives the programmer the options to initialise

it with the number of threads he/she wants to run the application with. In case the

programmer does not have a count of threads in mind then the programmer can let the

threading building blocks library decide the number of threads that is needed to run

the application. This feature was tested across all the three applications implemented.

The initial test was to vary the number of threads that was initialised in the scheduler

and see how the application performed. The experiment was carried out for different

values for the maximum number of tokens in flight. The results obtained are as in

Figure 5.11.

The Bitonic sorting network degraded in performance as the number of threads

was increased irrespective of the limit on the number of token in flight. This to certain

extent confirms to the assumption we made about the stages of the pipelines performing


Figure 5.11: Performance of Bitonic Sorting Network varying the number of threads in

execution.

less computation intensive tasks in the case of Bitonic sorting network.

Figure 5.12: Performance of Fast Fourier Transform Kernel varying the number of

threads in execution.

In the Fast Fourier Transform it can be noted from the Figure 5.12 that the increase

in the number of threads gave better speedup for the application. Providing the appli-

cation with more thread to work with and more tokens to work on, the performance

kept on increasing. The performance stabilised after a particular count of threads was

reached.

In the Filter Bank application the speedup of the application increased proportion-

ally to the number of threads which can be seen in Figure 5.13. The speedup values

stabilised after reaching a particular count of threads.

From all the three examples it was seen that it was easy to identify the value of


Figure 5.13: Performance of Filter Bank varying the number of threads in execution.

the number of threads for the best performance of the application. This feature gave

the programmer a very easy and powerful way to tweak his/her application to get good

performance.

Threading building block supports automatic initialisation of the scheduler with

the number of threads and this made the application scalable to different machines

with different number of cores. This is very powerful because there was no need for

the programmer to make any changes in the code depending on the machines he/she is

trying to run the application on. On experimenting it was understood that the count of

the number threads initialised in the scheduler was equal to the number of processors in

the machine. This was in support to the claim by the developers of threading building

block that good performance is achieved when there is one thread per processor of the

machine. This can be seen from the graphs, where the tests were run on a 16-core

shared memory machine. Except for the bitonic sorting network, the applications gave

good performance when the number of the threads was 16. The experiment was run

with different sizes of input and on machines having different number of cores. The

results obtained were as shown in Figure 5.14 and Figure 5.15.

It can be noted that the automatic initialisation done by the threading building

blocks library works in most of the cases. But then there are cases where there were

better results. It can be understood from this evaluation that if the application devel-

opment in threading building blocks is for a particular machines with a fixed number

of cores then it is best to trial run the application for different values of thread count

to choose the most ideal value. In the case of designing application for heterogeneous

machines with different number of cores, it is ideal to leave the initialisation of the


Figure 5.14: Performance of the FFT Kernel for different input sizes and number of

cores of the machine.

Figure 5.15: Performance of the Filter Bank application different input sizes and number

of cores of the machine.

number of threads to the threading building blocks library that does it based on the

machine it is running on.

5.6 Feature 3: Setting an upper limit on the number of

tokens in flight

Setting number of tokens in flight define the amount of parallelism in the pipeline.

When you limit the maximum number of tokens in flight to N, then there wont be

more than N operations happening in the pipeline at any instant of time. Thus setting


the right values for the number of tokens in flight is very crucial to the performance

of the pipeline. A low value would actually reduce the amount of parallelism and

will not utilise the full computing power the hardware provides. There will be many

threads spawned to perform the operations in the pipeline but because there are less

tokens, most of the threads will be idle most of the time. Having a large value for

the number of tokens in flight can also be a problem because it may cause excessive

resource utilisation (example Memory).

This feature was useful in many ways during the application development. The

best example was in the bitonic sorting network as discussed earlier where one of the

designs for a data structure, it was needed to keep a check on the number of tokens in

flight to ensure that the application worked correctly. More over in any pipeline appli-

cation there is a need to have this check over the number of tokens in flight for proper

functioning of the pipeline. So this threading building blocks feature is advantages

because the programmer does not have to go into extra trouble of considering issues

like these unlike in pthread programming.

To further evaluate this feature, it was necessary to understand the performance

benefits that the feature easily provided to the programmer. The three application

were run for different values of tokens for a given thread count. The speedup of the

applications were calculated and plotted on graphs as seen in Figure 5.16. The Bitonic

Figure 5.16: Performance of Bitonic Sorting Network varying the limit on the number of

tokens in flight.

sorting network was computationally less intensive thus increasing the limit on number

of tokens in flight did not create much difference in the speedup values.

In the Fast Fourier Transform kernel application it can be seen from the Figure 5.17


Figure 5.17: Performance of Fast Fourier Transform varying the limit on the number of

tokens in flight.

that the performance of the application increased with the increase in the limit on the

number of tokens in flight. For large number of threads after reaching the value 10 the

speedup values stabilised, by which we understand it to be the optimum value for the

number of tokens in flight.

Figure 5.18: Performance of Filter Bank varying the limit on the number of tokens in

flight.

In the Filter Bank application something similar to the Fast Fourier Transform ker-

nel can be observed in Figure 5.18. The performance of the application increased

proportionally to the number of tokens and stabilised after reaching a particular limit.

This feature was implemented in the corresponding pthread application to see if it

gave similar performance results like in the case of threading building blocks. The re-


Figure 5.19: Performance of pthread applications varying the limit on the number of

tokens in flight.

sults were obtained as in Figure 5.19. The graph levels out after a step rise in speedup.

This might be because the stages here are run serially, so the number of threads ex-

ecuting the application is constant. The number of threads processing tokens should

increase proportionally to the number of tokens in flight to ensure that there are enough

workers to process the tokens.

5.7 Feature 4: Nested parallelism

The threading building blocks library supported nested parallelism and so it was de-

cided to evaluate this feature to understand how this could help pipeline application de-

velopment. So as a programmer trying to achieve the best performance for his applica-

tion, the for loops performing computation on large array and filter co-efficient matri-

ces in the Filter Bank application was made to run in parallel using the parallel for

construct provided by the library. This gave performance benefits to a small amount

than the ones earlier in the pthread application obtained with all the stages of the filter

running in parallel mode with speedup of 9.3.

Chapter 6

Guide to the Programmers

6.1 Overview

There are many parallel programming environments available now for developing par-

allel applications. It is a very crucial decision for any programmer to choose the best

programming language/library that is best suited for their parallel application develop-

ment. Choosing from so many variety of languages/libraries is not an easy task.

This guide provides a helping hand to future programmer developing shared mem-

ory pipeline applications, to make a choice between the conventional POSIX thread

programming and Intel Threading Building Blocks. The guide will help programmer

realise their priorities and to make the right choice between the two parallel program-

ming libraries. This guide will help the programmers make a choice depending on

their experience in parallel programming, design requirements of their application, de-

velopment time for their application, performance requirements and scalability.

6.2 Experience of the programmer

Novice programmer: Threading Building Block is definitely the best choice. It ab-

stracts all the lows level threading details and guides an inexperienced program-

mer in developing efficient, scalable and reliable applications.

Expert programmer: Threading Building Blocks definitely guides to better applica-

tions but is not a perfect solution to all application needs. It does not provide the

flexibility to alter low level details like in the case of pthread.

54

Chapter 6. Guide to the Programmers 55

6.3 Design of the application

Number of stages in the pipeline

TBB: If the application requires to decide the number of stages in the pipeline dynam-

ically during runtime then the application cannot be implemented in threading

building blocks as the library does not support it.

POSIX Thread: Runtime determination of the number of stages in the pipeline is

possible in pthread programming.

I/O bound operations

TBB: If the application to be developed has I/O bound operations then Threading

Building Blocks is not the right choice because of its unfair and non pre-emptive

task scheduler.

POSIX Thread: The library is good for I/O bound operations because of its deter-

ministic nature.

Real-Time applications

TBB: Real-time applications cannot be implement in Threading Building Block li-

brary because of the unfair and non pre-emptive task scheduler.

POSIX Thread: The library is good for Real-time operations because of its deter-

ministic scheduling policy.

Non Linear Pipeline pattern

TBB: If the application design strictly requires the programmer to implement the

pipeline as a non-linear pipeline then threading building block is not a good

choice as it does not support non-linear pipelines. But if application design

allows to change the pattern of the pipeline to a linear pattern with a little increase

in the latency of the pipeline then threading building blocks can be used for the

pipeline application development.

POSIX Thread: Programmer is given the flexibility to implement the pipeline of

any pattern as per the design requirements.


Stages with data parallelism

TBB: It is very easy to make the stages run with data parallelism by setting the mode

of operation of the filters accordingly.

POSIX Thread: Data parallelism in the stages is not easily achieved. Programmer

will have to handle all the thread synchronisation issues and ensure exclusive

access on shared resources.

Number of Tokens in Flight

TBB: If application design requires to maintain a check on the number of tokens in

flight then threading building blocks will be of good help as it provides a feature

that allows to keep an upper limit on the number of tokens in flight.

POSIX Thread: The programmer has to implement the feature if needed.

Serial and ordered processing of tokens

TBB: If the design requires, it is very easy to make the all stages run serially and

ensure ordered processing of tokens by setting the mode of operation of the filters

accordingly.

POSIX Thread: Serial and order processing of tokens in all the stages is not easily

achieved. Its put to the programmer to implement the feature if needed.

Nested Parallelism

TBB: The operations done on a token within a stage can be broken down and made to

run in parallel with the help of parallel constructs like parallel for, parallel while

etc. that the library provides.

POSIX Thread: Nested Parallelism is difficult to incorporate.

6.4 Performance

TBB: Good performance is easily available using threading building block library

because it efficiently abstracts all the threading details which is needed to achieve


good performance. Data parallelism and nested parallelism which will improve

performance is easily implemented in the pipeline using threading building blocks.

POSIX Thread: Good performance is not easily achieved. Requires the program-

mer to know all the optimisation strategies needed to improve the performance

of the application.

6.5 Scalability

TBB: Due to the automatic scheduler initialisation functionality, the number of

threads to run the application will be automatically decided by the TBB library

depending on the number of processors in the machine on which the application

is run. This makes the application scalable without the need to change anything

in the code.

POSIX Thread: Automatic scaling depending on the machine is not present in the

library. The programmer will have to write code to implement the feature which

is a difficult task.

6.6 Load Balancing

TBB: It is easily possible to collapse fast running stages and make slow bottleneck

stages to run with data parallelism to ensure a load balanced pipeline. It is also

achieved due to the working stealing scheduling policy of the scheduler.

POSIX Thread: It is not easily achieved.

6.7 Application Development Time

TBB: If the need is faster development of efficient pipeline applications then thread-

ing building block will help you achieve this because of the abstractions it pro-

vides to do so.

POSIX Thread: Development time is higher compared to threading building blocks.

Plenty of time is spend testing and debugging the application.

Chapter 7

Future Work and Conclusions

7.1 Overview

In this project we tried to evaluate Intel Threading Building Blocks Pipelines. The

commercial and academic community look forward to evaluations of parallel program-

ming languages and libraries because there are so many options to choose and deciding

on the best options is really difficult. There has not been much work done to evaluate

parallel programming languages and libraries but now since the world is moving to-

wards parallel programming, evaluations of these languages/libraries can help parallel

programmer to a large extent in their decision making.

The evaluation of the threading building block library was done in comparison

to POSIX thread. Various features that the library provides was put under test and

evaluated in terms of usability, expressibility and performance. Implementation of

various pipeline application also helped in properly evaluating the library which gave

much deeper analysis of the library and how it can be useful during pipeline application

development. The features threading building blocks provided was implemented in the

pthread library to understand how the abstraction provided by the threading building

blocks library made the job of the programmer easy.

7.2 Conclusions

In general, the project was a success. We were able to evaluate threading building

blocks pipelines to a large extent. We were also able to do a comparative study with

the POSIX thread library which brought out the pros and cons in each of the library. We

went through the entire life cycle of software development for different pipeline appli-

58

Chapter 7. Future Work and Conclusions 59

cations in both the programming libraries. In the designing phase we found out about

the limitation of the threading building block library to express non-linear pipelines but

on the other hand it provided with many features like setting the limit on the number

of tokens in flight, setting the number of threads to run the application and setting the

mode of operation of stages that reduced the amount of the designing details that the

programmer had to handle. The designing phase was much more difficult for pthread

application because the amount of details the programmer had to have knowledge about

and also to be able get the right design incorporating all the low levels details.

During the implementation phase of the applications it was found that the thread-

ing building blocks was much more usable for the programmer because of the abstrac-

tions the library provided. As the library provided most of the abstraction there were

very less scope for errors done by the programmer which made debugging and test-

ing the application much more easy. Pthread on the other hand due the details that

the programmer had to handle was prone to a lot many errors and took much longer

development time than the threading building blocks applications.

During the performance analysis of the application developed in the two program-

ming libraries we were able to learn in great detail about both the libraries. From the

performance results its was found that the threading building blocks is not ideal for

I/O bound tasks and real time applications. The speedup obtained for the applications

developed in threading building block was much higher than the pthread versions but

then on further optimising the pthread applications like introducing data parallel stages

which was not an easy task we were able to obtain speedup better than in the case of

threading building blocks. With the nested parallelism feature that threading building

blocks supported with other parallel constructs like parallel for, parallel while etc. the

performance of the threading building blocks overtook the performance of the pthread

application. Even though threading building blocks achieved good performance the

ease at which the performance was achieved was much easier than in the case of the

pthread applications.

It is very necessary to understand that pthread though difficult to program gives

the programmer the flexibility to manipulate the lowest detail. Having such deeper

access, a programmer can easily optimise programs in application specific ways and

may be get optimisations that are far better than what the threading building blocks

abstractions provide. Threading Building Blocks whereas gives only fewer option to

optimise the programs which may not give the best possible results.

In the end we cannot actually conclude by saying that one library is better than

Chapter 7. Future Work and Conclusions 60

the other for pipeline application development because each library had its pros and

cons depending on the experience of the programmer, the design of the application,

the required development time of the application etc. So its upto the programmer to

decide from the evaluation that we have done here and from his/her needs whether

threading building blocks is best suitable for their pipeline application development.

7.3 Future Work

We have evaluated only the pipelines in threading building blocks library and there are

many other features and parallel constructs that the library provides that can be put

under test and analysed. The combination of all these parallel constructs may really

be helpful to develop efficient parallel applications. In this project we have evaluated

only the high level details of the threading building blocks library, as further work

there could be much deeper evaluation of the library taking into consideration how the

library handles low level details and also the working of the scheduler. The inability

of the threading building blocks to handle I/O bound task is a serious issue. Since

threading building blocks can work in combination with the pthread library a detail

study can be done to analyse if the combination of these two libraries by letting pthread

to handle I/O operations gives better results. Intel TBBs pipeline can now perform

DirectX, OpenGL, and I/O parallelisation by the use of the thread bound filter feature.

In many cases we can see that there are certain types of operations that require that

they are used from the same thread every time and by using a filter bound to a thread,

you can guarantee that the final stage of the pipeline will always use the same thread

[14]. This is definitely an area were there can be further detailed low level evaluations.

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