using a csp based programming model for reconfigurable processor arrays

Using a CSP based Programming Model for

Reconfigurable Processor ArraysBy: [email protected]

"Using a CSP based Programming Model for Reconfigurable Processor Arrays", Zain-ul-Abdin 2

Motivation• Emergence of new heterogeneous parallel

architectures– Increased Performance– Power Efficiency

• Traditional methods– Automatic parallelization by compilers– Use of Thread model of computation

• Highly non-deterministic• Use of Concurrent Programming Model

– Expresses computations in a productive manner by matching it to target hardware

– Supported by a compiler for allowing portability


Array of Processors• Consists of heterogenous processors with

specialized interconnection netrworks• Improved performance by exploiting paralellism

rather than scaling clock frequency• Flexible due to dynamically reconfigurable

interconnection network• Energy Efficient

– Individual brics can be switched off when not in use– The Clock frequency of brics can be optimized


Ambric Programming Model

• Design consists of:– Objects: defines the

functionality in either java subset or assembly.

– Structured composition described in aStruct


Ambric-Simple ExampleDesign Toplevel

design SimpleDesigntop { Root_IF root_Inst;}

interface Root_IF {}

binding CompRoot implements Root_IF {

simpledesign process1;

Vio inOut = {NumSources = 1, NumSinks = 1};

channel c0 = {inOut.out[0], process1.in};channel c1 = {process1.out, inOut.in[0]};}

Object Structure

interface simpledesign { inbound in; outbound out;}

binding Javasimpledesign implements simpledesign {

implementation "simpledesign.java";}

Object Implementation

import ajava.io.InputStream;import ajava.io.OutputStream;

public class simpledesign {public void

run(InputStream<Integer> in, OutputStream<Integer> out) {

while (true) { out.writeInt(in.readInt()); } }}


Why use Occam-pi?

• Language level support for concurrency

• Provides higher order combinators for facilitating composition of re-targetable data parallel descriptions

• Sematically transparent PAR/SEQ style• Explicit control of graularity of parallelism

and data locality


Occam-pi Language

• Based on ideas of CSP with pi-calculus• Abstractions for underlying hardware

– Processes– Channels (Unbuffered message passing)

• Rendezvous behavior of channels– Receiver blocks until the sender wrote the value – Sender continues after the receiver read the value


Occam-pi Language• Primitive actions

– Variable assignment– Channel output !– Channel input ?– PAR– SEQ

• Variables can only be written by one process in parallel– Likewise, only a single process can read from a channel, and

another single process can write to the channel

PROC SimpleEx() INT x,y:

CHAN OF INT c,d:PAR SEQ

c ! 117d ? x

SEQc ? yd ! 118

:


Compilation Methodology

• Implemented a Backend for Ambric in Tock(Translator of Occam to C by Kent)

• Staged compilation• Native SOPL code generation for Ambric

– Use of concurrency of Occam-pi– Reduced memory footprint


Occam-Ambric Compilation


Ambric-related Transformations

• Introduction of Channel-end Specifiers• Enables use of flat data parallelism• Replicators transformations:

– SEQ Replicators to For loops– PAR Replicators unrolled to multiple PROCs

• Emission of aStruct structural interface and binding code for each PROC

• Emission of aJava class code corresponding to each PROC


1D-Discrete Cosine Transform


Performance Results

• 8-point DCT ImplementationsNP CLFO Throughput

Serial DCT 1 220 TS

Coarse-grained Parallelized DCT

4 51 3TS

Fine-grained Parallelized DCT

34 33 27TS


Conclusions

• Proposed the use of Occam-pi for programming a coarse-grained processor architecture

• Raises the abstraction level while not compromising the efficiency

• To extend the compiler for supporting mobility features of Occam-pi for reconfigurable logic

using a csp based programming model for reconfigurable processor arrays

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