Advanced System Technology

Think JoinAn extension to Think for programming parallel

streaming applicationsFréderic Mayot, Matthieu Leclercq, Erdem Özcan

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Streaming applications on MPSoC

Functional considerations– Enabling more parallelism– Mastering the distribution– Supporting different execution models

Software engineering considerations– Easier programming models– Separation of concerns– Improved the reuse and mastered evolution– Management of software assets

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Streaming strategies

Dispatcher

Decoder I

Decoder P

Display

Encoded videaoStream

Decoded video stream

SynchronizationImage I

Image P

Decoded I image

Image Idécodée

Decoded P image

Programming flows: approaches– The “pull” way

Strong coupling Too many synchronizations for good parallelism

– The “push” way : Filters pipelining through buffers Weak coupling Difficult to deal with many asynchronous events.

Proposition– Mastering the asynchronous push model– Join Calculus based synchronization language

ImageI getImg() { return DecI.getImgDecI();}

ImageI getImgDecI() { return Dispatcher.getImgI();}

Buffer

void dispatch() { while (Buffer.NotFull) Buffer.push(imgI); }

void decodeP() { while (Buffer.NotEmpty) { imgI = Buffer.pull(); Buffer2.push(Dec(imgI)); }}

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Computation model Synchronization via join patterns

Set of messages received reaction invocation

Language ThinkJoin

ift1.pushImgI (img1) & itf2.pushImgP (img2) => itf3.decode (img1, img2);

ift1.pushImgI (img1) & itf4.synch () => itf5.display (img1);

Rule

Pattern

Reaction

Message

Décodeur I

Décodeur P

AfficheurDispatcher

void pushImgI(ImageI img) &void pushImgP(ImageP img) { /* decode */}

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Integration into Fractal ADL

Implémentation

Controler Functional

void pushImgI (img);

void pushImgP (img);

void decode (img1, img2);

ift1.pushImgI (img1) & itf2.pushImgP (img2) => itf3.decode (img1, img2);

Automatically generated by the compiler from the rule :

<definition name=“functional”> <interface name="itf3" role="server" /> <content class="reaction" language=“thinkMC" /> </functional>

ADL

<definition name=“controller”> <interface name="itf1" role="server" /> <interface name="itf2" role="server" /> <interface name="itf3" role="client" /> <content class="rules" language="join" /> </definition>

ADL

functional.c

controller.rules

<definition name="cmp"> <interface name="itf1" role="server" /> <interface name="itf2" role="server" /> <component name=“functional” … > </component> <component name=“controller” … > </component > <binding client="this.itf1" server="ctl.itf1" /> <binding client="this.itf2" server="ctl.itf2" /> <binding client=“ctrl.react" server=“fn.reeact" /></definition>

ADL

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BBA

DCDC F

FE

How does-it work ? FSM execution example

Think-v3 ADL compiler extension Computation model inspired by Join calculus (cf. Fournet et al.)

– Tightly modified to have FIFO order within bindings Implementation

– Formalization by Finite State Machines (cf. Maranget et al.)– Encoding and pattern matching with bit masks to have linear complexity – Using circular buffers and synchronization primitives

i1.A() & i3.E() => ir.r1();

i1.B() & i2.C() => ir.r2();

i2.D() & i4.F() => ir.r3();

i1

i4i3

i2

FSMA

public interface I2 { void C ( ); void D ( );}

public interface I3 { void E ( );}

public interface I4 { void F ( );}

public interface I1 { void A ( ); void B ( );}

i1 i2 i3 i4

BB

FF

E

DCDC

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Execution models

i1

i2

Réactions

i1.pushP(img1) & i2.pushI(img2) => i3.decode(img1, img2);

i3

Scheduler

register(FSM, {decode, 10})

execute({decode, 10})

decode(img1, img2)

FSM

7 {decode, 10}… …

10 {img1, img2}… …

match

FSM

pushP(img1)

pushI(img2)

Various execution types– Synchronous– Asynchronous– Threads pool

Only one parameter to switch Depends on each component

pushP(img1)

pushI(img2)

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Evaluation on a MPEG2 decoder

Over cost (FSM, scheduling, serialization)Objectives reached

ArchitectureCollaborationRepartition

Next: more formal language, better efficiency

Header Dec

Picture Dec

Framestore

MC Y

MC U

MC V

Exporter

IDCT

Adder

MotionComp.

idct_mb_type.intra_mb() &idct_mb.push_mb(mb_data)=>r.add(mb_data);

idct_mb_type.non_intra_mb() &idct_mb.push_mb(mb_data) &mc_y.prediction_finished() &mc_u.prediction_finished() &mc_v.prediction_finished()=>r.add(mb_data);

Threads

Perf.

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