Data ow programming Generation of software and hardware codecompilfr.ens-lyon.fr/wp-content/uploads/2011/04/Yviquel.pdf · Introduction Data ow rogrammingp Code generation Conclusion

IntroductionData�ow programming

Code generationConclusion

Data�ow programming

Generation of software and hardware code

Hervé Yviquel

Directed by E. Casseau1 and M. Raulet2

1IRISA (CAIRN) - University of Rennes 1

2IETR (Image) - INSA of Rennes

28 avril 2011

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1 Introduction

2 Data�ow programmingPresentationModel of Computation

3 Code generationCompilation frameworkHardware backendSoftware backends

4 Conclusion

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Video back then...

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...and now !

1999

2003

Today

1994

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Video standards

A lot of innovations in few years

• Redundancy, prede�ned pro�les

• Platform-speci�c reference software5 / 31



Target platforms

Several platforms with di�erent properties :1 Processors

I General-purpose : CPUsI Special-purpose : DSPs, GPUs

2 Programmable logicI FPGA

3 System-On-Chips (SoCs)I Texas Instruments' OMAP

FPGA

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Recon�gurable Video Coding

RVC : a new MPEG standard

• Allows recon�guration and reusability

• Provides platform-independent data�ow models

Objective : Shows the validity of the data�ow approach

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Recon�gurable Video Coding

RVC : a new MPEG standard

• Allows recon�guration and reusability

• Provides platform-independent data�ow models

Objective : Shows the validity of the data�ow approach

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PresentationModel of Computation

1 Introduction



4 Conclusion

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1 Introduction



4 Conclusion

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De�nition

AC

B D

E

A directed graph = Network

• Vertex = Processes (Actors)

• Edges = Communication channels (Unidirectional FIFOs)

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Advantages

• No architectural considerations inthe algorithm

I Target can be any platformI Abstraction is preserved

• Explicit parallelismI Between actors on parallel pathsI Pipeline

AC

B D

E

time

A

B

C

A

B

C

A

D

E

D

E

B

C

D

E

...

...

...

...

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1 Introduction



4 Conclusion

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Data�ow Process Networks (DPNs)

Generalization of Kahn Process Networks (KPNs)

• Dynamic data�owI Data Dependant behavior

• Execution of an actor = Sequence of actor �ringsI Data readingI Computational procedureI Data writing

• Unblocking semantic of FIFOs

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Writing DPN actors

• RVC-CAL : Subset of Cal Actor Language (CAL) [Eker andJanneck, 2003]

actor Clip () int(size=10) I, bool SIGNED ==> int(size=9) OUT :

int(size=7) count := -1; bool sflag;

read_signed: action SIGNED:[s] ==> guard

count < 0 do sflag := s; count := 63; end

limit: action I:[i] ==> OUT:[if i > 255 then 255 else i end] var int min = if sflag then -255 else 0 end do count := count - 1; end

priority read_signed > limit; end

end

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Analysis problems

• Most expressive data�ow modelI Nondeterminism

• Many analysis problems are undecidableI Proof of terminationI Computation of a static scheduling

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Finding static behavior

• Classi�cation of actors (SDF, CSDF, etc) [Wipliez and Raulet - DASIP10]

• Merging of actorsI Increase performance (Remove FIFOs)I What conditions ?

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Compilation frameworkHardware backendSoftware backends

1 Introduction



4 Conclusion

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1 Introduction



4 Conclusion

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Open RVC-CAL Compiler (Orcc 1)

1. Orcc is available at http ://orcc.sf.net19 / 31




1 Introduction



4 Conclusion

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VHDL backend

• Natural implementation (1 actor = 1 entity)

• Not �nished but validate on subnetworks of MPEG-4 SP(IDCT2D)

[Siret and Nezan - DASIP10]

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1 Introduction



4 Conclusion

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Scheduling on Software platform

AC

B D

E

Core1

Core2

Number of processor cores < Number of actors

• Run-time scheduling

• User-level scheduler (No need of threads)

• Dynamic scheduling strategies (round-robin, ...)

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Results from C backend

• MPEG-4 Simple Pro�le = 42 actors

• MPEG-4 Advanced Video Coding = 131 actors

Decoder Core QCIF CIF 720p Speedup

MPEG-4 SP 1 - 144 15.6 1

2 - 265 26.6 1.78

4 - 516 51.9 3.28

MPEG-4 AVC 1 169 40.6 - 1

2 294 71.4 - 1.74

4 473 97.1 - 2.59

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Limitation of static compilation

• No recon�guration and reusability

• Loosing platform-independent model

Compilation infrastructure

Source code (C, C++)

1010101…

Customer

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Dynamic recon�guration

• JIT compilerI Recon�guration

• Virtual machineI Keep platform-independent model

Customers

Virtual Machine

Configuration engine

Transmission

Coded video

Configuration

(Network)

$

(bytecode)

$

(Actors)

MPEG-RVC

Video Tool LibraryVideo Tool

Library

[Gorin, Wipliez, Raulet, etc - SiPS10]

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1 Introduction



4 Conclusion

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Summary

Data�ow programming

• can target software and hardware platform

• shows encouraging performances

• scales up well on multicore architectureI Thanks to explicit parallelism

• permits recon�guration and reuseability

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Perspectives

• Improve performancesI Find policy to merge static actorsI Faster scheduler (dynamic mapping)

• Target recon�gurable architectures

• etc.

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Perspectives



• etc.

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Perspectives



• etc.

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Documents

Data ow programming Generation of software and hardware codecompilfr.ens-lyon.fr/wp-content/uploads/2011/04/Yviquel.pdf · Introduction Data ow rogrammingp Code generation Conclusion