As the GPU landscape continues to evolve, your Jacket code will simply get faster without you having to do anything new. Our focus is to deliver the best GPU computing platform on the planet for engineers, scientists, analysts, and students, and we guarantee that Jacket customers will always have the very best in GPU hardware choices for your applications. We continue to watch the progress of OpenCL, ATI, and other GPU computing initiatives. This is our report of the current status of OpenCL relative to CUDA on the new NVIDIA hardware. Currently it is unknown whether the overhead is due to the time taken to launch a kernel in OpenCL or something else within the API. The results indicate that there is an overhead when using OpenCL with smaller data sizes, which seems to disappear at larger data sizes. All the tests were for single precision numbers. System: Ubuntu 18.04 - Linux Kernel Version: I tried 4.15 through 4.17 (both custom and standard repo kernels) - Nvidia Driver: 396 - Graphics Card (GPU): Nvidia GeForce 1080 - CPU: i7-8700K (Coffeelake) - Cuda Version: 9.1 - ocl-icd-libopencl1 Version: 2.2.11-1ubuntu1 - ocl-icd-libopencl1 Provides: libopencl-1.1-1, libopencl-1. NVidia: since CUDA v.9, OpenCL support has migrated from the CUDA SDK to the. We will also install the NVIDIA GPU computing toolkit or CUDA toolkit. All the vector operations are modified versions of the SDK examples provided by NVIDIA. The major change in OpenCL 3.0 versus previous versions is the adoption of. We will learn how to install the Intel OpenCL driver on Windows. Considering these handicaps, only a few matrix / vector operations were considered for this benchmark. OpenCL is notably more difficult to program and debug than CUDA since OpenCL documentation, tools, and scientific computation libraries are still very limited. The Tesla C2050 is an amazing beast of a card, providing upto 512 Gigaflops of double precision arithmetic (at peak).īefore we present the benchmarks, we should comment on the programmability of OpenCL versus CUDA. Given the new tools available and the new Fermi hardware, we ran some tests on the Tesla c2050 to compare OpenCL performance to CUDA performance. CUDA is, of course, specific to nVidia based hardware. For example, NVIDIA now provides an OpenCL driver, toolkit, programming guide, and SDK examples. Installing CUDA and OpenCL requires two main components: A toolkit and a graphics driver. Some things have changed since that original post. If you have not reviewed that blog post to gain some insight into our progress you can access it here –. In December of 2008, we did a blog post answering questions from customers and prospects about the use of OpenCL for Jacket.
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