glaf: a visual programming and auto- tuning framework for...

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GLAF: A Visual Programming and Auto-

Tuning Framework for Parallel Computing

Student: Konstantinos Krommydas

Collaborator: Dr. Ruchira Sasanka (Intel)

Advisor: Dr. Wu-chun Feng

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High-performance computing is crucial

in a broad range of scientific domains:

• Engineering, math, physics, biology, …

Parallel

programming

revolution has made

high-performance

computing

accessible to the

broader masses

Motivation

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Many different programming languages

Challenges

Many different computing platforms

Many different architectures

Many different optimization strategies

Domain experts should not (need to) know all these details

• but rather focus on their science

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Challenges

Domain experts need to collaborate with computer scientists

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pseudocode

unoptimized/naïve

serial code

highly optimized

parallel code

Innovation

slow-down

Limited access to

parallel computing

• Communication overhead & errors

• Need to exchange domain-

specific/programming knowledge

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Contributions

• Realize a programming abstraction & development

framework for domain experts to provide a balance

between performance and programmability

– i.e., obtain fast performance from algorithms that have been

programmed easily

5

*GL

AF

auto-parallelizable, optimizable, tunable

intuitive, familiar, minimalistic syntax

data-visual and interactive

able to integrate with existing legacy code

Desired features

*Grid-based Language and Auto-tuning Framework

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Programming Using GLAF: a Simple Example

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Graphical User InterfaceComments

(in step,

statements,

grids, …)

Module

name

Function

name

Step number

(within function)

Click-based

interface

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Grid-Based Data Structures

• GLAF variables are based on the concept of grids:

• familiar abstraction (e.g., images, matrices,

spreadsheets)

• regular format that facilitates code generation,

optimizations, and parallelism detection

• Grid-based programming puts the focus on the

relation, rather than the implementation details

• Example:

• Scalar variable: 0D grid

• 1D array: one-dimensional grid

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Data typeDimension title

Grid name

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Different data

types across

a dimension

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GLAF Infrastructure

Browser Web/Cloud

• GLAF Programming GUI

• Data visualization

• Fortran/C/JS/OpenCL code

generation

• Auto-parallelization

• Compilation/auto-tuning

script generation

• Data storage• Compilation

• Auto-tuning

• Execution

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Code Generation

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Code Generation

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Auto-TuningGenerates

platform-specific

binaries &

optimizationsSelects the

languages in

which to auto-

generate code Selects one or

more code “starting

points” for each

languageSelects

optimizations for

each combination

of language and

code “starting point”

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Auto-Tuning

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Multi-level

Auto-Tuning

Approach

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Fortran C OpenCL

Serial GLAF-Parallel Compiler Parallel

GLAF

program

Data-layout

transformsLoop collapse

transforms

Loop interchange

transforms…

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Visualization

• Data visualization facilitates:

– understanding the algorithm being developed

– revealing bugs at an early stage

“Show Data”

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Visualization




“Colorize”

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Visualization




“Image Map”

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0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

serial compiler-parallelized GLAF-parallelized(col(3)) GLAF-parallelized(col(1))

Speed-upoverserialfortran_C

PU

Implementa on

fortran_CPU

C_CPU_norestr

C_CPU_restr

fortran_XP

C_XP_norestr

C_XP_restr

Results: 3D finite difference algorithm

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0

5

10

15

20

25

serialSoA serialAoS compiler-parallelizedSoAcompiler-parallelizedAoS GLAF-parallelizedSoA GLAF-parallelizedAoS

Speed-upoverserialSoAfortran_CPU

Implementa on

fortran_CPU fortran_CPU_tmp C_CPU_norestr C_CPU_restr_tmp C_CPU_restr_tmp_powf

fortran_XP fortran_XP_tmp C_XP_norestr C_XP_restr_tmp C_XP_restr_tmp_powf

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Results: N-body algorithm

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Related Work

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Implicit Explicit

Parallelization

Compilers:

gcc, Intel,

Cray, PGI,

…

Extensions/

libraries:

Pthreads, OpenMP,

OpenACC, Chapel

…

+ Includes loop-level parallelism & data-level

parallelism (vectorization), and potential

optimizations

- Requires certain programming knowledge

- (Often) conservative nature

Problem

Solving

Environments

Domain-specific:

computational biology,

physics,

dense linear algebra,

Fast-fourier transforms,

…

+ High-performance auto-tuning

- Restrictive in nature

- Restrictive in terms of target

language and/or platform

Domain-

Specific

Languages

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Future Work

• Improve tool’s robustness

• Enabling more languages/extensions:

– OpenCL, OpenACC

– Support of distributed programming (MPI)

• Dynamic feedback/advice on parallelism issues

• Extend auto-tuning, auto-parallelization/auto-

vectorization capabilities

• Implement more dwarfs and provide back-end support

for common programming pitfalls in code generated

for supported languages

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Conclusion

GLAF targets domain experts and provides a fine balance between

performance and programmability

• auto-parallelization, optimization and auto-tuning

• helps avoid common programming pitfalls

GLAF allows systematic generation

of multiple starting points for

different

languages/platforms/optimizations

Different seeds in a state-

space search algorithm

Analogous to:In summary:

“GLAF: A Visual Programming and Auto-Tuning

Framework for Parallel Computing”Krommydas, Sasanka, Feng

Leads to overall better performance Global vs. local minimum

glaf: a visual programming and auto- tuning framework for...

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