CUDA vs OpenCL
- 1. ArrayFire Webinar: OpenCL and CUDA Trade-Offs and Comparisons
- 2. GPU Software Features Programmability Portability Scalability Performance Community
- 3. Leading GPU Computing Platforms Programmability Portability Scalability Performance Community
- 4. Performance • Both CUDA and OpenCL are fast • Both can fully utilize the hardware • Devil in the details: – Hardware type, algorithm type, code quality
- 5. Performance • ArrayFire results at end of webinar
- 6. Scalability • Laptops --> Single GPU Machine – Both CUDA and OpenCL scale, no code change • Single GPU Machine --> Multi-GPU Machine – User managed, low-level synchronization • Multi-GPU Machine --> Cluster – MPI
- 7. Scalability Interesting developments: • Memory Transfer Optimizations – CUDA GPUDirect technology • Mobile GPU Computing – OpenCL available on ARM, Imgtec, Freescale, …
- 8. Scalability • Laptops --> Single GPU Machine – ArrayFire’s JIT optimizes for GPU type • Single GPU Machine --> Multi-GPU Machine – ArrayFire’s deviceset() function is super easy • Multi-GPU Machine --> Cluster – MPI
- 9. Portability • CUDA is NVIDIA-only – Open source announcement – Does not provide CPU fallback • OpenCL is the open industry standard – Runs on AMD, Intel, and NVIDIA – Provides CPU fallback
- 10. Portability • ArrayFire is fully portable – Same ArrayFire code runs on CUDA or OpenCL – Simply select the right library
- 11. Community • NVIDIA CUDA Forums – 26,893 topics • AMD OpenCL Forums – 4,038 topics • Stackoverflow CUDA Tag – 1,709 tags • Stackoverflow OpenCL Tag – 564 tags
- 12. Community • AccelerEyes GPU Forums – 1,435 topics – Largest GPU forums by a software company • Next largest – PGI GPU Forums – 485 topics
- 13. Programmability • Both CUDA and OpenCL are low-level – Time consuming kernel development – Data-parallel algorithm design • Focus on programmability interfaces
- 14. Programmability Faster SSE or Slower AVX Time-consuming Easy-to-use
- 15. Programmability Writing Faster Kernels SSE or Slower AVX Time-consuming Easy-to-use
- 16. Programmability Writing Faster Kernels SSE or Compiler Slower AVX Directives Time-consuming Easy-to-use
- 17. Programmability Writing Using Faster Kernels Libraries SSE or Compiler Slower AVX Directives Time-consuming Easy-to-use
- 18. Libraries Make the Difference Raw math libraries in NVIDIA CUDA • CUBLAS, CUFFT, CULA, Magma – Provides all BLAS, LAPACK, and FFT routines necessary for most dense matrix operations • CUSPARSE – A good start for sparse linear algebra
- 19. Libraries Make the Difference Raw math libraries in AMD OpenCL • clAmdBlas, clAmdFft – Provides most important Blas routines – Provides radix 2, 3, and 5 FFT routines – No LAPACK support – No sparse data support
- 20. Libraries Make the Difference Raw math libraries in AMD OpenCL • clAmdBlas, clAmdFft – Provides most important Blas routines – Provides radix 2, 3, and 5 FFT routines – No LAPACK support – No sparse data support – Runs on any OpenCL-compliant device
- 21. ArrayFire Code and Benchmarks