01 01 parallel computing explained

7/28/2019 01 01 Parallel Computing Explained

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Slides Prepared from the CI-Tutor Courses at

NCSA

http://ci-tutor.ncsa.uiuc.edu/

ByS. Masoud Sadjadi

School of Computing and Information

Sciences

Florida International University

Parallel Computing Explained

Parallel Computing Overview

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Agenda

1 Parallel Computing Overview

2 How to Parallelize a Code

3 Porting Issues

4 Scalar Tuning5 Parallel Code Tuning

6 Timing and Profiling

7 Cache Tuning

8 Parallel Performance Analysis

9 About the IBM Regatta P690

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Agenda

1 Parallel Computing Overview

1.1 Introduction to Parallel Computing

1.1.1 Parallelism in our Daily Lives

1.1.2 Parallelism in Computer Programs

1.1.3 Parallelism in Computers1.1.4 Performance Measures

1.1.5 More Parallelism Issues

1.2 Comparison of Parallel Computers

1.3 Summary

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Parallel Computing Overview

Who should read this chapter? New Users to learn concepts and terminology.

Intermediate Users for review or reference.

Management Staff to understand the basic

conceptseven if you dont plan to do any

programming.

Note: Advanced users may opt to skip this chapter.

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Introduction to Parallel

Computing High performance parallel computers

can solve large problems much faster than adesktop computer fast CPUs, large memory, high speed interconnects, and

high speed input/output

able to speed up computations

by making the sequential components run faster

by doing more operations in parallel

High performance parallel computers are in

demand need for tremendous computational capabilities in

science, engineering, and business. require gigabytes/terabytes f memory and

gigaflops/teraflops of performance

scientists are striving for petascale performance5


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Introduction to Parallel

Computing HPPC are used in a wide variety of disciplines.

Meteorologists: prediction of tornadoes andthunderstorms

Computational biologists: analyze DNA sequences

Pharmaceutical companies: design of new drugs Oil companies: seismic exploration

Wall Street: analysis of financial markets

NASA: aerospace vehicle design

Entertainment industry: special effects in moviesand commercials

These complex scientific and businessapplications all need to perform computations onlarge datasets or large equations.

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Parallelism in our Daily Lives There are two types of processes that occur in

computers and in our daily lives: Sequential processes

occur in a strict order

it is not possible to do the next step until the current one iscompleted.

Examples

The passage of time: the sun rises and the sun sets.

Writing a term paper: pick the topic, research, and writethe paper.

Parallel processes many events happen simultaneously

Examples

Plant growth in the springtime

An orchestra7


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Agenda

1 Parallel Computing Overview1.1 Introduction to Parallel Computing



1.1.2.1 Data Parallelism1.1.2.2 Task Parallelism

1.1.3 Parallelism in Computers

1.1.4 Performance Measures



1.3 Summary

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Parallelism in Computer

Programs Conventional wisdom:

Computer programs are sequential in nature

Only a small subset of them lend themselves to

parallelism.

Algorithm: the "sequence of steps" necessary to do acomputation.

The first 30 years of computer use, programs were run

sequentially.

The 1980's saw great successes with parallelcomputers.

Dr. Geoffrey Fox published a book entitled Parallel

Computing Works!

many scientific accomplishments resulting from parallel

computing9


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Parallel Computing What a computer does when it carries out more than

one computation at a time using more than oneprocessor.

By using many processors at once, we can speedupthe execution

If one processor can perform the arithmetic in time t. Then ideally p processors can perform the arithmetic in

time t/p. What if I use 100 processors? What if I use 1000

processors?

Almost every program has some form of parallelism. You need to determine whether your data or your

program can be partitioned into independent pieces thatcan be run simultaneously.

Decomposition is the name given to this partitioningprocess.

Types of parallelism:10


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Data Parallelism

The same code segment runs concurrently oneach processor, but each processor is assigned

its own part of the data to work on.

Do loops (in Fortran) define the parallelism.

The iterations must be independent of each other.

Data parallelism is called "fine grain parallelism"

because the computational work is spread into

many small subtasks.

Example

Dense linear algebra, such as matrix multiplication,

is a perfect candidate for data parallelism.

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An example of data parallelism

Original SequentialCode Parallel Code

DO K=1,N

DO J=1,NDO I=1,N

C(I,J) = C(I,J) +

A(I,K)*B(K,J)

END DO

END DO

END DO

!$OMP PARALLEL DO

DO K=1,N

DO J=1,NDO I=1,N

C(I,J) = C(I,J) +A(I,K)*B(K,J)

END DO

END DO

END DO

!$END PARALLEL DO

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Quick Intro to OpenMP

OpenMP is a portable standard for paralleldirectives covering both data and task parallelism.

More information about OpenMP is available on the

OpenMP website.

We will have a lecture on Introduction to OpenMPlater.

With OpenMP, the loop that is performed in

parallel is the loop that immediately follows the

Parallel Do directive. In our sample code, it's the K loop:

DO K=1,N

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OpenMP Loop Parallelism

Iteration-ProcessorAssignments

The code segment

running on eachprocessor

DO J=1,N

DO I=1,N

C(I,J) = C(I,J) +

A(I,K)*B(K,J)

END DO

END DO

ProcessorIterations

of K

Data

Elements

proc0 K=1:5A(I, 1:5)

B(1:5 ,J)

proc1 K=6:10A(I, 6:10)

B(6:10 ,J)

proc2 K=11:15

A(I, 11:15)

B(11:15 ,J)

proc3 K=16:20A(I, 16:20)

B(16:20 ,J)

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OpenMP Style of Parallelism

can be done incrementally as follows:1. Parallelize the most computationally intensive

loop.

2. Compute performance of the code.

3. If performance is not satisfactory, parallelizeanother loop.

4. Repeat steps 2 and 3 as many times as needed.

The ability to perform incremental parallelism is

considered a positive feature of data parallelism.

It is contrasted with the MPI (Message Passing

Interface) style of parallelism, which is an "all or

nothing" approach.15


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Task Parallelism Task parallelism may be thought of as the opposite of

data parallelism.

Instead of the same operations being performed ondifferent parts of the data, each process performsdifferent operations.

You can use task parallelism when your program canbe split into independent pieces, often subroutines,that can be assigned to different processors and runconcurrently.

Task parallelism is called "coarse grain" parallelism

because the computational work is spread into just afew subtasks.

More code is run in parallel because the parallelism isimplemented at a higher level than in data parallelism.

Task parallelism is often easier to implement and has

less overhead than data parallelism.16


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Task Parallelism

The abstract code shown in the diagram isdecomposed into 4 independent code segments

that are labeled A, B, C, and D. The right hand

side of the diagram illustrates the 4 code

segments running concurrently.

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Task Parallelism

Original Code Parallel Code

program main

code segment labeled A

code segment labeled B

code segment labeled C

code segment labeled D

end

program main

code segment labeled A




end

program main

!$OMP PARALLEL

!$OMP SECTIONS

code segment labeled A!$OMP SECTION


!$OMP SECTION


!$OMP SECTION


!$OMP END SECTIONS

!$OMP END PARALLEL

end

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OpenMP Task Parallelism

With OpenMP, the code that follows eachSECTION(S) directive is allocated to a different

processor. In our sample parallel code, the

allocation of code segments to processors is as

follows. Processor Code

proc0code segment

labeled A

proc1code segment

labeled B

proc2code segment

labeled C

proc3code segment

labeled D

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Parallelism in Computers

How parallelism is exploited and enhanced withinthe operating system and hardware components

of a parallel computer:

operating system

arithmetic

memory

disk

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Operating System Parallelism All of the commonly used parallel computers run a

version of the Unix operating system. In the tablebelow each OS listed is in fact Unix, but the name ofthe Unix OS varies with each vendor.

For more information about Unix, a collection ofUnixdocuments is available.

Parallel Computer OS

SGI Origin2000 IRIX

HP V-Class HP-UX

Cray T3E Unicos

IBM SP AIX

WorkstationClusters

Linux

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Two Unix Parallelism Features background processing facility

With the Unix background processing facility youcan run the executable a.outin the background andsimultaneously view the man page for the etimefunction in the foreground. There are two Unixcommands that accomplish this:

a.out > results &

man etime

cron feature With the Unix cron feature you can submit a job that

will run at a later time.

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Arithmetic Parallelism

Multiple execution units facilitate arithmetic parallelism.

The arithmetic operations of add, subtract, multiply, and divide (+- * /) are each done in a separate execution unit. This allowsseveral execution units to be used simultaneously, because theexecution units operate independently.

Fused multiply and add is another parallel arithmetic feature. Parallel computers are able to overlap multiply and add. This

arithmetic is named MultiplyADD (MADD) on SGI computers, andFused Multiply Add (FMA) on HP computers. In either case, thetwo arithmetic operations are overlapped and can complete inhardware in one computer cycle.

Superscalar arithmetic is the ability to issue several arithmetic operations per computer

cycle. It makes use of the multiple, independent execution units. On

superscalar computers there are multiple slots per cycle that canbe filled with work. This gives rise to the name n-way superscalar,

where n is the number of slots per cycle. The SGI Origin2000 iscalled a 4-way superscalar computer.

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Memory Parallelism

memory interleaving memory is divided into multiple banks, and consecutive data

elements are interleaved among them. For example if yourcomputer has 2 memory banks, then data elements with evenmemory addresses would fall into one bank, and data elementswith odd memory addresses into the other.

multiple memory ports Port means a bi-directional memory pathway. When the dataelements that are interleaved across the memory banks areneeded, the multiple memory ports allow them to be accessedand fetched in parallel, which increases the memory bandwidth(MB/s or GB/s).

multiple levels of the memory hierarchy There is global memory that any processor can access. There is

memory that is local to a partition of the processors. Finally thereis memory that is local to a single processor, that is, the cachememory and the memory elements held in registers.

Cache memory Cache is a small memory that has fast access compared with the

larger main memory and serves to keep the faster processor filledwith data.

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Memory Parallelism

Memory Hierarchy Cache Memory

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Disk Parallelism

RAID (RedundantArray ofInexpensive Disk) RAID disks are on most parallel computers.

The advantage of a RAID disk system is that itprovides a measure of fault tolerance.

If one of the disks goes down, it can be swappedout, and the RAID disk system remains operational.

Disk Striping When a data set is written to disk, it is striped

across the RAID disk system. That is, it is broken

into pieces that are written simultaneously to thedifferent disks in the RAID disk system. When thesame data set is read back in, the pieces are readin parallel, and the full data set is reassembled inmemory.

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Agenda




1.1.3 Parallelism in Computers1.1.4 Performance Measures



1.3 Summary

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Performance Measures

Peak Performance is the top speed at which the computer can operate.

It is a theoretical upper limit on the computer's performance.

Sustained Performance is the highest consistently achieved speed. It is a more realistic measure of computer performance.

Cost Performance is used to determine if the computer is cost effective.

MHz is a measure of the processor speed. The processor speed is commonly measured in millions of cycles

per second, where a computer cycle is defined as the shortest timein which some work can be done.

MIPS is a measure of how quickly the computer can issue instructions. Millions of instructions per second is abbreviated as MIPS, where

the instructions are computer instructions such as: memory reads

and writes, logical operations , floating point operations, integeroperations, and branch instructions.28


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Performance Measures

Mflops (Millions of floating point operations per second) measures how quickly a computer can perform floating-point

operations such as add, subtract, multiply, and divide.

Speedup measures the benefit of parallelism. It shows how your program scales as you compute with more

processors, compared to the performance on one processor. Ideal speedup happens when the performance gain is linearly

proportional to the number of processors used.

Benchmarks are used to rate the performance of parallel computers and

parallel programs. A well known benchmark that is used to compare parallel

computers is the Linpack benchmark. Based on the Linpack results, a list is produced of the Top 500

Supercomputer Sites. This list is maintained by the Universityof Tennessee and the University of Mannheim.

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More Parallelism Issues

Load balancing is the technique of evenly dividing the workload among the

processors. For data parallelism it involves how iterations of loops are

allocated to processors. Load balancing is important because the total time for the program

to complete is the time spent by the longest executing thread. Theproblem size

must be large and must be able to grow as you compute with moreprocessors.

In order to get the performance you expect from a parallelcomputer you need to run a large application with large data sizes,

otherwise the overhead of passing information betweenprocessors will dominate the calculation time.

Good software tools are essential for users of high performance parallel computers. These tools include:

parallel compilers

parallel debuggers30


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More Parallelism Issues

The high performance computing market is risky andchaotic. Many supercomputer vendors are no longer inbusiness, making the portability of your applicationvery important.

A workstation farm

is defined as a fast network connecting heterogeneousworkstations. The individual workstations serve as desktop systems for

their owners. When they are idle, large problems can take advantage of

the unused cycles in the whole system.An application of this concept is the SETI project. You can

participate in searching for extraterrestrial intelligencewith your home PC. More information about this project isavailable at the SETI Institute.

Condor

is software that provides resource management services forapplications that run on heterogeneous collections of31
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Agenda


1.2 Comparison of Parallel Computers1.2.1 Processors

1.2.2 Memory Organization

1.2.3 Flow of Control

1.2.4 Interconnection Networks

1.2.4.1 Bus Network

1.2.4.2 Cross-Bar Switch Network

1.2.4.3 Hypercube Network1.2.4.4 Tree Network

1.2.4.5 Interconnection Networks Self-test

1.2.5 Summary of Parallel Computer Characteristics

1.3 Summary

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C i f P ll l


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Comparison of Parallel

Computers

Now you can explore the hardware componentsof parallel computers:

kinds of processors

types of memory organization

flow of control

interconnection networks

You will see what is common to these parallel

computers, and what makes each one of them

unique.

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Kinds of Processors

There are three types of parallel computers:1. computers with a small number of powerful

processors

Typically have tens of processors.

The cooling of these computers often requires verysophisticated and expensive equipment, making these

computers very expensive for computing centers.

They are general-purpose computers that perform especially

well on applications that have large vector lengths.

The examples of this type of computer are the Cray SV1 andthe Fujitsu VPP5000.

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Kinds of Processors

There are three types of parallel computers:2. computers with a large number of less powerful

processors

Named a Massively Parallel Processor (MPP), typically have

thousands of processors. The processors are usually proprietary and air-cooled.

Because of the large number of processors, the distance

between the furthest processors can be quite large requiring a

sophisticated internal network that allows distant processors to

communicate with each other quickly. These computers are suitable for applications with a high

degree of concurrency.

The MPP type of computer was popular in the 1980s.

Examples of this type of computer were the Thinking Machines

CM-2 computer, and the computers made by the MassPar35
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Kinds of Processors

There are three types of parallel computers:3. computers that are medium scale in between the two

extremes

Typically have hundreds of processors.

The processor chips are usually not proprietary; rather they arecommodity processors like the Pentium III.

These are general-purpose computers that perform well on a

wide range of applications.

The most common example of this class is the Linux Cluster.

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Trends and Examples

Processor trends :

The processors on todays commonly used parallel

computers:

Decade Processor Type Computer Example

1970s Pipelined, Proprietary Cray-1

1980s Massively Parallel, Proprietary Thinking Machines CM2

1990s Superscalar, RISC, Commodity SGI Origin2000

2000s CISC, Commodity Workstation Clusters

Computer Processor

SGI Origin2000 MIPS RISC R12000

HP V-Class HP PA 8200

Cray T3E Compaq Alpha

IBM SP IBM Power3

Workstation Clusters Intel Pentium III, Intel Itanium37


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Memory Organization

The following paragraphs describe the threetypes of memory organization found on parallel

computers:

distributed memory

shared memory

distributed shared memory

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Distributed Memory

In distributed memory computers, the total memory ispartitioned into memory that is private to eachprocessor. There is a Non-Uniform MemoryAccess time (NUMA),

which is proportional to the distance between the twocommunicating processors.

On NUMAcomputers, data is

accessed the

quickest from a

private memory,

while data from themost distant

processor takes the

longest to access.

Some examples are

the Cray T3E, the39
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Distributed Memory

When programming distributed memorycomputers, the code and the data should bestructured such that the bulk of a processorsdata accesses are to its own private (local)memory.

This is called havinggood data locality.

Today's distributedmemory computersuse message

passingsuch asMPItocommunicatebetween processors

as shown in the40


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Distributed Memory

One advantage of distributed memory computersis that they are easy to scale. As the demand for

resources grows, computer centers can easily

add more memory and processors.

This is often called the LEGO blockapproach.

The drawback is that programming of distributed

memory computers can be quite complicated.

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Shared Memory

In shared memory computers, all processors have accessto a single pool of centralized memory with a uniformaddress space.

Any processor can address any memory location at thesame speed so there is Uniform MemoryAccess time(UMA).

Processors communicate with each other through theshared memory.

The advantages anddisadvantages ofshared memorymachines are roughlythe opposite ofdistributed memory

computers. They are easier to

program because theyresemble theprogramming of singleprocessor machines

But they don't scalelike their distributed42


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Distributed Shared Memory

In Distributed Shared Memory (DSM) computers, a cluster orpartition of processors has access to a common shared memory. It accesses the memory of a different processor cluster in a NUMA

fashion.

Memory is physically distributed but logically shared. Attention to data locality again is important. Distributed shared

memory computerscombine the bestfeatures of bothdistributed memorycomputers and sharedmemory computers.

That is, DSM computershave both the scalabilityof distributed memorycomputers and the easeof programming ofshared memorycomputers.

Some examples of DSMcom uters are the SGI

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Trends and Examples

Memoryorganization

trends:

The memoryorganization of

todays

commonly used

parallel

Decade Memory Organization Example

1970s Shared Memory Cray-1

1980s Distributed Memory Thinking Machines CM-2

1990s Distributed Shared Memory SGI Origin2000

2000s Distributed Memory Workstation Clusters

Computer Memory Organization

SGI Origin2000 DSM

HP V-Class DSM

Cray T3E Distributed

IBM SP Distributed

Workstation Clusters Distributed44


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Flow of Control

When you look at the control of flow you will seethree types of parallel computers:

Single Instruction Multiple Data (SIMD)

Multiple Instruction Multiple Data (MIMD)

Single Program Multiple Data (SPMD)

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Flynns Taxonomy

Flynns Taxonomy, devised in 1972 by Michael Flynn ofStanford University, describes computers by how streamsof instructions interact with streams of data.

There can be single or multiple instruction streams, andthere can be single or multiple data streams. This gives rise

to 4 types of computers as shown in the diagram below: Flynn's taxonomy

names the 4computer typesSISD, MISD, SIMDand MIMD.

Of these 4, onlySIMD and MIMDare applicable toparallel computers.

Another computer

type, SPMD, is a46


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SIMD Computers

SIMD stands forSingle Instruction Multiple Data. Each processor follows the same set of instructions.

With different data elements being allocated to each processor.

SIMD computers have distributed memory with typically thousands ofsimple processors, and the processors run in lock step.

SIMD computers, popular in the 1980s, are useful for fine grain data

parallel applications, such as neural networks. Some examples of SIMD

computers were the ThinkingMachines CM-2 computer andthe computers from theMassPar company.

The processors are

commanded by the globalcontroller that sendsinstructions to the processors. It says add, and they all add.

It says shift to the right, andthey all shift to the right.

The processors are like

obedient soldiers, marching inunison.

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MIMD Computers

MIMD stands forMultiple Instruction Multiple Data. There are multiple instruction streams with separate code

segments distributed among the processors.

MIMD is actually a superset of SIMD, so that the processors canrun the same instruction stream or different instruction streams.

In addition, there are multiple data streams; different data

elements are allocated to each processor. MIMD computers can have either distributed memory or shared

memory. While the processors on

SIMD computers run in lockstep, the processors onMIMD computers run

independently of each other. MIMD computers can be

used for either data parallelor task parallel applications.

Some examples of MIMDcomputers are the SGI

Origin2000 computer and-

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SPMD Computers

SPMD stands forSingle Program Multiple Data. SPMD is a special case of MIMD.

SPMD execution happens when a MIMD computer is programmedto have the same set of instructions per processor.

With SPMD computers, while the processors are running thesame code segment, each processor can run that code segment

asynchronously. Unlike SIMD, the synchronous execution of instructions is relaxed.

An example is the execution of an if statement on a SPMDcomputer. Because each processor computes with its own partition of the

data elements, it may evaluate the right hand side of the if

statement differently from another processor. One processor may take a certain branch of the if statement, and

another processor may take a different branch of the same ifstatement.

Hence, even though each processor has the same set ofinstructions, those instructions may be evaluated in a different

order from one processor to the next. The analo ies we used for describin SIMD com uters can be

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Summary of SIMD versus


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Summary of SIMD versus

MIMD

SIMD MIMD

Memorydistributed

memory

distriuted memory

or

shared memory

Code Segment same perprocessor

same

ordifferent

Processors

Run Inlock step asynchronously

Data

Elements

different per

processor

different per

processor

Applications data parallel

data parallel

or

task parallel

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Trends and Examples

Flow of control trends:

The flow of control on today:

Decade Flow of Control Computer Example

1980's SIMD Thinking Machines CM-2

1990's MIMD SGI Origin2000

2000's MIMD Workstation Clusters

Computer Flow of Control

SGI Origin2000 MIMD

HP V-Class MIMD

Cray T3E MIMD

IBM SP MIMD

Workstation Clusters MIMD51


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Agenda


1.2 Comparison of Parallel Computers1.2.1 Processors

1.2.2 Memory Organization

1.2.3 Flow of Control

1.2.4 Interconnection Networks

1.2.4.1 Bus Network

1.2.4.2 Cross-Bar Switch Network

1.2.4.3 Hypercube Network

1.2.4.4 Tree Network

1.2.4.5 Interconnection Networks Self-test

1.2.5 Summary of Parallel Computer Characteristics

1.3 Summary

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Interconnection Networks

What exactly is the interconnection network? The interconnection networkis made up of the wires and cables

that define how the multiple processors of a parallel computer areconnected to each other and to the memory units.

The time required to transfer data is dependent upon the specifictype of the interconnection network.

This transfer time is called the communication time.

What network characteristics are important? Diameter: the maximum distance that data must travel for 2

processors to communicate.

Bandwidth: the amount of data that can be sent through a networkconnection.

Latency: the delay on a network while a data packet is being storedand forwarded.

Types of Interconnection NetworksThe network topologies (geometric arrangements of the computer

network connections) are:

Bus

Cross-bar Switch Hybercube

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Interconnection Networks

The aspects of network issues are: Cost Scalability Reliability Suitable Applications Data Rate Diameter Degree

General Network Characteristics Some networks can be compared in terms of their degree and

diameter.

Degree: how many communicating wires are coming out ofeach processor. A large degree is a benefit because it has multiple paths.

Diameter: This is the distance between the two processorsthat are farthest apart. A small diameter corresponds to low latency.

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Bus Network

Bus topology is the original coaxial cable-based LocalAreaNetwork (LAN) topology in which the medium forms asingle bus to which all stations are attached.

The positive aspects

It is also a mature technology that is well known and reliable.

The cost is also very low. simple to construct.

The negativeaspects

limited data

transmissionrate.

not scalable interms ofperformance.

Example: SGIPower Challenge.55


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Cross-Bar Switch Network

A cross-bar switch is a network that works through a switchingmechanism to access shared memory. it scales better than the bus network but it costs significantly

more.

The telephone system uses this type of network. An example ofa computer with this type of network is the HP V-Class.

Here is a diagram

of a cross-barswitch networkwhich shows theprocessors talkingthrough theswitchboxes tostore or retrievedata in memory.

There are multiplepaths for aprocessor tocommunicate witha certain memory.

The switches56


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Cross-Bar Switch Network

In a hypercube network, the processors areconnected as if they were corners of amultidimensional cube. Each node in an Ndimensional cube is directly connected to N othernodes.

The fact that the number ofdirectly connected, "nearestneighbor", nodes increaseswith the total size of thenetwork is also highlydesirable for a parallel

computer. The degree of a hypercube

network is log n and thediameter is log n, where n isthe number of processors.

Examples of computers with57


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Tree Network

The processors are the bottom nodes of the tree. Fora processor to retrieve data, it must go up in thenetwork and then go back down.

This is useful for decision making applications thatcan be mapped as trees.

The degree of a tree network is 1. The diameter of thenetwork is 2 log (n+1)-2 where n is the number ofprocessors.

The Thinking Machines CM-5 is an example of a parallelcomputer with this type of

network. Tree networks are very

suitable for databaseapplications because itallows multiple searches

through the database at atime.58


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Interconnected Networks

Torus Network: A mesh with wrap-aroundconnections in both the x and y directions.

Multistage Network: A network with more thanone networking unit.

Fully Connected Network: A network where everyprocessor is connected to every other processor.

Hypercube Network: Processors are connectedas if they were corners of a multidimensionalcube.

Mesh Network: A network where each interiorprocessor is connected to its four nearestneighbors.

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Interconnected Networks

Bus Based Network: Coaxial cable based LANtopology in which the medium forms a single bus

to which all stations are attached.

Cross-bar Switch Network: A network that works

through a switching mechanism to access sharedmemory.

Tree Network: The processors are the bottom

nodes of the tree.

Ring Network: Each processor is connected to

two others and the line of connections forms a

circle.

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Summary of Parallel Computer


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Characteristics

How many processors does the computer have? 10s?

100s?

1000s?

How powerful are the processors?

what's the MHz rate

what's the MIPS rate

What's the instruction set architecture?

RISC

CISC

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Characteristics

How much memory is available? total memory

memory per processor

What kind of memory?

distributed memory shared memory

distributed shared memory

What type of flow of control?

SIMD MIMD

SPMD

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Characteristics

What is the interconnection network? Bus

Crossbar

Hypercube

Tree Torus

Multistage

Fully Connected

Mesh

Ring

Hybrid

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Design decisions made by some of


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Design decisions made by some of

the major parallel computer vendors

Computer

Programmin

g

Style

OS Processors MemoryFlow of

ControlNetwork

SGI

Origin2000

OpenMP

MPI

IRIXMIPS RISC

R10000

DSM MIMD

Crossbar

Hypercub

e

HP V-

Class

OpenMP

MPIHP-UX HP PA 8200 DSM MIMD

Crossbar

Ring

Cray T3E SHMEM UnicosCompaq

Alpha

Distribute

dMIMD Torus

IBM SP MPI AIX IBM Power3Distributed

MIMDIBMSwitch

Workstatio

n

Clusters

MPI LinuxIntel Pentium

III

Distribute

dMIMD

Myrinet

Tree

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Summary

This completes our introduction to parallel computing. You have learned about parallelism in computer

programs, and also about parallelism in the hardwarecomponents of parallel computers.

In addition, you have learned about the commonly

used parallel computers, and how these computerscompare to each other.

There are many good texts which provide anintroductory treatment of parallel computing. Here aretwo useful references:

Highly Parallel Computing, Second EditionGeorge S. Almasi and Allan GottliebBenjamin/Cummings Publishers, 1994

Parallel Computing Theory and Practice

01 01 parallel computing explained

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