The 7th Annual IndicThreads Pune Conference was held on 14-15 December 2012. http://pune12.indicthreads.com/

1. Accelerating computation in html 5
Ashish Shah
SAS R&D INDIA

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

3. Multicore Computing

4. Problem statement
 Layout algorithm for node-linked graphs
Layout Algorithm

5. DEMO
Demo 1 – Serial version
Demo 2 - Parallel version with multi-core CPU
Demo 3 - Parallel version with many-core GPU

6. Performance analysis
Time in ms
Number of particles

7. 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

8. Example of adding two vectors
Serial version
For(i=1 to n)
c[i]= a[i]+b[i];
Using OpenCL
_kernel add(a,b,c)
{
int i =get_global_id(); //get thread id
c[i]=a[i]+b[i];
}

9. OpenCL Architecture
1. Platform model
2. Execution model
3. Memory model
4. Programming model

10. OpenCL -Platform
• Device
• Host
Host
Intel CPU
GPU 2
Compute
Device 1 (GPU
1)
Compute unite (Cores)

11. OpenCL-Execution Model
_kernel add(a,b,c)
1. Kernel {
2. Work-items int i =get_global_id();//get thread/workitem id
c[i]=a[i]+b[i];
3. Work group }
4. ND-range
5. Program
6. Memory
objects
7. Command
queues

12. Memory Model in OpenCL
Compute Device
Private register Private register Private register
Compute unit 0 Compute unit 1 Compute unit 2
Local memory/cache Local memory/cache Local memory/cache
Global constant memory-DRAM
Global Memory -DRAM

13. Programming model
1. Data parallel-single function on multiple data
2. Task parallel-Multiple functions on single data

14. OpenCL Stack
Java,c,.net, HTML,.java,
WebCL Applications .NET,c,c++
kernals
OpenCL-Api Compiler String data
context
Memory Api’s
OpenCL Framework
OpenCL Runtime Command
queues, buffer
objects, kernel
Device driver execution
OpenCL Device (GPU/CPU
hardware)

15. Essential Development Tasks
C-code with restrictions
Initialize Initiate
Execute Read back
Parallelize Code Kernel OpenCL kernels and
kernel data to host
environment data

16. Essential Development Tasks
• Query compute device
• Create context
• Compile kernels
Initialize Initiate
Execute Read back
Parallelize Code Kernel OpenCL kernels and
kernel data to host
environment data

17. Essential Development Tasks
• Create memory objects
• Map data structures to OpenCL
supported data structures.
• Initialize kernel parameters
Initialize Initiate
Execute Read back
Parallelize Code Kernel OpenCL kernels and
kernel data to host
environment data

18. Essential Development Tasks
• Specify number of threads to
execute task
• Trigger the execution of kernel-
sync or async
Initialize Initiate
Execute Read back
Parallelize Code Kernel OpenCL kernels and
kernel data to host
environment data

19. Essential Development Tasks
• Map to application datastructure
Initialize Initiate
Execute Read back
Parallelize Code Kernel OpenCL kernels and
kernel data to host
environment data

20. 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

21. Binding OpenCL to WebCL
CPU
Host application JavaScript
WebCL
OpenCL
OpenCL Framework compliant
device

22. Coding with WebCL
platforms = 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’, []);

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

24. 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

25. Software/Hardware used in demo application
Hardware
Intel(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 GPU
http://www.nvidia.com/object/quadro_nvs_notebook.
html
WebCL plugin for Firefox
http://webcl.nokiaresearch.com/

26. References
http://www.macresearch.org/opencl
http://en.wikipedia.org/wiki/GPGPU
http://www.khronos.org/webcl/