Introduction Array API Runtime Code Generation Preliminary Results Conclusions

A Composable Array Function Interface forHeterogeneous Computing in Java

Juan Jose Fumeroλ Michel Steuwerπ Christophe Dubachλ

λUniversity of Edinburgh, UK

πUniversity of Münster, Germany

ARRAY’14 , 13.06.2014

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

Programming for Heterogeneous Computing

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

Programming for Heterogeneous Computing

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

Programming for Heterogeneous Computing

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

Previous Work

Embedded DSL in High Level Languages:PyCUDA, PyOpenCL,...JOCL, JavaCL, JCuda, ...

Stream programming:IBM Liquid Metal: new operators for tasks and data parallelismSumatra: Stream API based on JDK 8 for GPU arrayprogramming

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

API DescriptionArray Programming Interface

Function<T,R>

ArrayFunction<T,R>

Map<I,T,R> Reduce<I,T,R> Zip<I,T,R>

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

Example - dotProduct

∑(n−1)0 ai ∗ bi

f = z i p ( ) . map( x −> x . _1 ∗ x . _2). r educe ( ( x , y ) −> x + y ) ;

F l o a t [ ] a = new F l o a t [N ] ;F l o a t [ ] b = new F l o a t [N ] ;

F l o a t [ ] r e s u l t = f . app l y (new Tuple ( a , b ) ) ;

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

Example - dotProduct

∑(n−1)0 ai ∗ bi

f = z i p ( ) . map( x −> x . _1 ∗ x . _2). r educe ( ( x , y ) −> x + y ) ;

F l o a t [ ] a = new F l o a t [N ] ;F l o a t [ ] b = new F l o a t [N ] ;

F l o a t [ ] r e s u l t = f . app l y (new Tuple ( a , b ) ) ;

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

Example - dotProduct

∑(n−1)0 ai ∗ bi

f = z i p ( ) . map( x −> x . _1 ∗ x . _2). r educe ( ( x , y ) −> x + y ) ;

F l o a t [ ] a = new F l o a t [N ] ;F l o a t [ ] b = new F l o a t [N ] ;

F l o a t [ ] r e s u l t = f . app l y (new Tuple ( a , b ) ) ;

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

Example - dotProduct

∑(n−1)0 ai ∗ bi

f = z i p ( ) . map( x −> x . _1 ∗ x . _2). r educe ( ( x , y ) −> x + y ) ;

F l o a t [ ] a = new F l o a t [N ] ;F l o a t [ ] b = new F l o a t [N ] ;

F l o a t [ ] r e s u l t = f . app l y (new Tuple ( a , b ) ) ;

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

Runtime Code Generation

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

Deoptimisation Process

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

Vision in the FutureOpportunities for Specialisation

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

SetupWorkstation with AMD SDK OCL Driver

Black-Scholes problemComparison with:

Java Sequential: primitivesdata typesJava Objects: using Floatand TuplesArray Function: our APIJava threadsOpenCL GPU

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

Sequential VersionBlack - Scholes (AMD version)

200

400

600

800

1000

512 1K 2K 4K 8K16K

32K65K

128K256K

500K 1M

Runtim

e in m

illis

econds

Input size

ArrayFunction API Sequential J. Objects Sequential J. Primitive

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

Parallel ExecutionsBlack-Scholes on AMD GPU and Intel 16 cores

0

10

20

30

40

50

512 1K 2K 4K 8K16K

32K65K

128K256K

500K 1M

Speedup

Input size

#32 Java Threads OpenCL GPU

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

GPU execution time breakdownBlack-Scholes on AMD Tahiti

1M elements Kernel execution workflow

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

GPU execution time breakdownBlack-Scholes on AMD Tahiti

1M elements Kernel execution workflow

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

GPU execution time breakdownBlack-Scholes on AMD Tahiti

1M elements Kernel execution workflow

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

GPU execution time breakdownBlack-Scholes on AMD Tahiti

1M elements Kernel execution workflow

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

GPU execution time breakdownBlack-Scholes on AMD Tahiti

1M elements Kernel execution workflow

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

.zip(Conclusions).map(Future)

PresentJava Array Programming API: very high level approach ofusing parallel patterns in heterogeneous systemsWe have presented an early prototype of Map/Reduce byusing Graal JDK8 and OpenCL

FutureRuntime scheduling (Where is the best place to run the code?)Code generation for multiple devicesSpecialised code generation at runtime can improveperformance and portability

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

.zip(Conclusions).map(Future)

PresentJava Array Programming API: very high level approach ofusing parallel patterns in heterogeneous systemsWe have presented an early prototype of Map/Reduce byusing Graal JDK8 and OpenCL

FutureRuntime scheduling (Where is the best place to run the code?)Code generation for multiple devicesSpecialised code generation at runtime can improveperformance and portability

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Introduction Array API Runtime Code Generation Preliminary Results Conclusions

Thanks so much for your attention

This work was supported by agrant from:

Juan José Fumerojuan.fumero@ed.ac.uk

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