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I know CUDA is for only Nvidia GPUs. If the specifications for a Nvidia graphics card states that CUDA is supported, can we conclude that it also supports OpenCL? Can there be support/compatibility problems with different OpenCL versions? i.e. OpenCL 1.0 or 1.1?
Given that in version 4.1 of the OpenCL Programming Guide (PDF) Nvidia explicitly speaks about CUDA-Enabled GPUs (Especially in Appendix A), I think we can safely assume that those cards supporting CUDA also support OpenCL. Even more so since OpenCL on Nvidia hardware still makes use of the CUDA architecture.
Appendix C of the same document lists the support of various extensions given a specific compute capability.
According to Nvidia's OpenCL page (http://developer.nvidia.com/opencl), all CUDA devices support OpenCL. As far as OpenCL versioning, that page indicates that you just need to make sure you have the right driver version.
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What is the best way to do programming for GPU?
I know:
CUDA is very good, much developer support and very nice zo debug, but only on NVidia Hardware
OpenCL is very flexible, run on NVidia, AMD and Intel Hardware, run on Accellerators, GPU and CPU but as far as I know not supported anymore by NVidia.
Coriander (https://github.com/hughperkins/coriander) which converts CUDA to OpenCL
HIP https://github.com/ROCm-Developer-Tools/HIP is made by AMD to have a possibility to write in a way to convert to AMD and NVidia CUDA. It also can convert CUDA to HIP.
OpenCL would my prefered way, I want to be very flexible in hardware support. But if not longer supported by NVidia, it is a knockout.
HIP sounds then best to me with different released files. But how will be the support of Intels soon coming hardware?
Are there any other options?
Important is for me many supported hardeware, long term support, so that can be compiled in some years also and manufacture independant.
Additional: Should be able to use more than obe compiler, on Linux and Windows supported.
Nvidia won't cancel OpenCL support anytime soon.
A newly emerging approach for portable code on GPU is SYCL. It enables higher level programming from a single source file that is then compiled twice, once for the CPU and once for GPU. The GPU part then runs on GPU via either OpenCL, CUDA or some other backend.
As of right now however, the best supported GPU framework across plattforms is OpenCL 1.2, which is very well established at this point. With that your code runs on 10 year old GPUs, on the latest and fastest data-center GPUs, on gaming and workstation GPUs and even on CPUs if you need more memory. On Nvidia GPUs there is no performance/efficiency tradeoff at all compared to CUDA; it runs just as fast.
The porting tools like HIP are great if you already have a large code base, but performance could possibly suffer. My advice is to go for either one framework and stay fully committed to it, rather than using some tool to then generate a possibly poorly optimized port.
If you choose to start with OpenCL, have a look at this OpenCL-Wrapper. The native OpenCL C++ bindings are a bit cumbersome to use, and this lightweight wrapper simplifies learning a lot, while keeping functionality and full performance.
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Can I use nVidia CUDA on QNX(x86_64 or other) and what driver do I need for this?
I found nothing about this by link, and in #46 answer sound like "I don't know"
http://www.qnx.com/news/web_seminars/faq_multicore.html
But, QNX thinks to Include Support for NVIDIA Tegra Processor Family:
http://www.qnx.com/news/pr_5306_1.html
And nVidia thinks to add support CUDA and OpenCL to the nVidia Tegra5(Logan) ARM+GK1xx in next year:
http://en.wikipedia.org/wiki/Tegra#Logan
http://www.ubergizmo.com/2013/07/nvidia-tegra-5-release-date-specs-news/
And then can we use CUDA on nVidia Tegra5(ARM+GK1xx) on QNX(ARM), and what about QNX(x86)?
At this time, there's no support for CUDA on QNX.
The supported operating systems for CUDA are listed on the cuda download page as well as in section 1.4 of the release notes
Regarding Tegra, at this time there are no Tegra devices that support CUDA. The list of CUDA-enabled GPUs is here. Whether using an x86/x86_64 CPU or an ARM CPU, one of these CUDA GPUs is required for CUDA support.
Update: There are now tegra devices that support CUDA, including the widely available Tegra TK1 and recently announced TX1.
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In our application we have FFT part. We would like to port that part onto GPU. We have Tesla K20m GPU. Which version of cuFFT is optimized for K20m card.
There is not a specific version of the cufft library that is optimized for a specific card. Just use the standard cufft library that ships with cuda 5.0 (or cuda 5.5 RC, if you like).
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what is "SASS" short for ?
I know it is an asembly level native code ISA targeting specific hardware,
exits in between PTX code and binary code.
but anyone could kindly tell me what does each character stands for ?
all that i can find about Fermi hardware native instruction is in cuobjdump.pdf, but it only gives their names, where can I find more information such as their throughput or latency or IPC or CPI, undertaking units corresponding to each intruction, like SFU, FPU ?
Streaming ASSembler.... I should know since I invented the term, lead the core team G80 streaming process architecture team and developed first SASS assembler ;-)
Since there seems to be no information on this anywhere, I can only guess: Shader ASSembly language
SASS (as Ashwin points out probably "Shader ASSembly") is the binary code that runs on the metal of Fermi architecture devices. What cudaobjdump (and older third party tools like decuda and nv50dis) show is a direct disassembly of the cubin payload emitted by the ptxas assembler.
To the best of my knowledge there is no open instruction set documentation for any NVIDIA GPUs.
At some point during the CUDA 5 release cycle, NVIDIA began to provide a summary document which annotates the basic instruction set of suported GPUs (Fermi, Kepler, and Maxwell as of CUDA 7).
Streaming ASSembly? Since NVIDIA calls their cores, "Streaming Multiprocessors".
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I want to download the latest version of the GPU computing SDK which is compatible with the system that I work on. The CUDA driver and runtime version are 4.10, but I can not find the link. I can just find the CUDA Toolkit which is not what I want. Can anyone help me by sending a direct link for me? Thanks.....
CUDA 4.1 is the latest CUDA release. The GPU Computing SDK for this release can be found at the bottom of this page: http://developer.nvidia.com/cuda-toolkit-41
The GPU Computing SDK is supposed to be available at this page: http://developer.nvidia.com/gpu-computing-sdk
But, it looks like NVIDIA has messed up the webpages a bit, the CUDA Toolkit and the GPU Computing SDK pages point at each other, with neither offering the SDK.