1. By Arun Bhandari Course: HPC Date: 01/28/12

2. GPU (Graphics Processing Unit) High performance many core processors Only used to accelerate certain parts of the graphics pipeline. Performance pushed by game industry

3. Introduction to GPGPU  Stands for General Purpose Graphics Processing Unit  Also called GPU computing  Use of GPU to do general purpose computing  Heterogeneous co-processing computing model

4. Why GPU for computing? GPU is fast Massively parallel High memory Bandwidth Programmable NVIDIA CUDA, OpenCL Inexpensive desktop supercomputing NVIDIA Tesla C1060 : ~1 TFLOPS @ $1000

5. GPU vs CPU (Computation)

6. GPU vs CPU (Bandwidth)

7. Applications of GPGPU MATLAB Statistical physics Audio Signal processing Speech processing Digital image processing Video processing Geometric computing Weather forecasting Climate research Bioinformatics Medical imaging Database operations Molecular modeling Control engineering Electronic design automation And many more……

8. Programming Models Data-parallel processing High arithmetic intensity Coherent data access GPU programming languages NVIDIA CUDA OpenCL

9. CUDA vs OpenCL Conceptually identical work-item = thread work-group = block Similar memory model Global, local, shared memory Kernel, host program CUDA, highly optimized for NVIDIA GPUs OpenCL can be widely used for any GPUs/ CPUs.

10. GPU Optimization Maximize parallel execution Use large inputs Minimize device to host memory overhead Avoid shared memory bank conflict Use less expensive operators Division : 32 cycles, multiplication : 4 cycles *0.5 instead of /2.0

11. Conclusions GPU computing delivers high performance Many scientific computing problems are parallelizable Future of today’s technology Issues Every problem is not suitable for GPUs Unclear future performance growth of GPU hardware

12. Questions ???