Accelerating computation in html 5Ashish ShahSAS R&D INDIA

Outline

• Multicore Computing • Problem statement• Demo • Introduction to OpenCL and WebCL• Conclusion• References

Multicore Computing

Problem statement

Layout algorithm for node-linked graphs

AlgorithmLayout

DEMO

Demo 1 – Serial versionDemo 2 - Parallel version with multi-core

CPUDemo 3 - Parallel version with many-core

GPU

Performance analysisT

ime

in m

s

Number of particles

Introduction to OpenCL

• Open Compute Language, C- like language.

• Framework for writing parallel algorithms• Heterogeneous platforms• Developed by Apple• Is an open standard and controlled by

Khronos group

Example of adding two vectors

_kernel add(a,b,c)

{

int i =get_global_id(); //get thread id

c[i]=a[i]+b[i];

}

For(i=1 to n)

c[i]= a[i]+b[i];

Serial version

Using OpenCL

OpenCL Architecture

1. Platform model2. Execution model3. Memory model4. Programming model

OpenCL -Platform

• Device• Host

Host

Intel CPUGPU 2

Compute Device 1 (GPU 1)

Compute unite (Cores)

OpenCL-Execution Model

1. Kernel

2. Work-items

3. Work group

4. ND-range

5. Program

6. Memory objects

7. Command queues

_kernel add(a,b,c)

{

int i =get_global_id();//get thread/workitem id c[i]=a[i]+b[i];

}

Memory Model in OpenCL

Compute Device

Compute unit 0 Compute unit 1 Compute unit 2

Global Memory -DRAM

Global constant memory-DRAM

Local memory/cache Local memory/cache Local memory/cache

Private register Private register Private register

Programming model

1. Data parallel-single function on multiple data

2. Task parallel-Multiple functions on single data

OpenCL Runtime

OpenCL Framework

OpenCL Stack

OpenCL Device (GPU/CPU hardware)

Device driver

Compiler

Applications

kernals

OpenCL-Api

HTML,.java,.NET,c,c++

String data

Java,c,.net,WebCL

contextMemory Api’s

Command queues, buffer objects, kernel execution

Essential Development Tasks

Parallelize Code Kernel

Initialize OpenCL

environment

Initiate kernels and

data

Execute kernel

Read back data to host

C-code with restrictions

Essential Development Tasks

Parallelize Code Kernel

Initialize OpenCL

environment

Initiate kernels and

data

Execute kernel

Read back data to host

• Query compute device• Create context• Compile kernels

Essential Development Tasks

Parallelize Code Kernel

Initialize OpenCL

environment

Initiate kernels and

data

Execute kernel

Read back data to host

• Create memory objects• Map data structures to OpenCL

supported data structures.• Initialize kernel parameters

Essential Development Tasks

Parallelize Code Kernel

Initialize OpenCL

environment

Initiate kernels and

data

Execute kernel

Read back data to host

• Specify number of threads to execute task

• Trigger the execution of kernel-sync or async

Essential Development Tasks

Parallelize Code Kernel

Initialize OpenCL

environment

Initiate kernels and

data

Execute kernel

Read back data to host

• Map to application datastructure

Introduction to WebCL

• Java Script bindings for OpenCL• First announced in March 2011 by

Khronos• API definition underway• Prototype plugin is available only for

Firefox browser

Binding OpenCL to WebCL

CPU

Host application JavaScript

OpenCL Framework

WebCL

OpenCL

compliant

device

Coding with WebCLplatforms = WebCL.getPlatformIDs();

context = WebCL.createContextFromType([WebCL.CL_CONTEXT_PLATFORM,

platforms[0]], WebCL.CL_DEVICE_TYPE_CPU);

devices = context .getContextInfo(WebCL.CL_CONTEXT_DEVICES);

program = context .createProgramWithSource(kernelSrc);

kernelfunction1 = program.createKernel(“function1");

buffparam = context.createBuffer(WebCL.CL_MEM_READ_WRITE, bufSize);

cmdQueue = context.createCommandQueue(devices[0], 0);

cmdQueue.enqueueWriteBuffer(buffparam , true, 0, bufSize, parameter, []);

kernelfunction1.setKernelArg(0, buffparam , WebCL.types.float2);

cmdQueue.enqueueNDRangeKernel(kernelfunction1 , 1, [], totalWorkitems,

totalWorkgroups, []);

cmdQueue.finish ();

cmdQueue.enqueueReadBuffer(‘xyz’, true, 0, bufSize, ‘xyzParam’, []);

Applications of OpenCL• Database mining • Neural networks• Physics based simulation,mechanics• Image processing• Speech processing• Weather forecasting and climate research• Bioinformatics

Conclusion

• Significant performance gains in using OpenCL for computations in client-side environments like HTML5

• Algorithms need to be ‘parallelizable’

• Further optimizations can be achieved by exploiting memory model

Software/Hardware used in demo application

HardwareIntel(R) Core(TM)2 Quad core CPU Q8400 @

2.66GHz

Nvidia 160m Quadro 8 cores @ 580 MHz

Software OpenCL runtime for CPU

http://software.intel.com/en-us/articles/vcsource-tools-opencl-sdk/

OpenCL runtime for GPUhttp://

www.nvidia.com/object/quadro_nvs_notebook.html

WebCL plugin for Firefoxhttp://webcl.nokiaresearch.com/

References

http://www.macresearch.org/openclhttp://en.wikipedia.org/wiki/GPGPUhttp://www.khronos.org/webcl/