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I'm honestly much more worried about NVIDIA's overwhelming success in buying their way into the entire scientific community. CUDA is proprietary, not standardi

by qayxc 7y ago

I'm honestly much more worried about NVIDIA's overwhelming success in buying their way into the entire scientific community. CUDA is proprietary, not standardised, and tied to a single hardware vendor. Technical issues aside, I applaud any effort to break the dominance of CUDA. CUDA is a pest that needs to either open up or be replaced. HPC is a very heterogeneous environment (on the whole) and making it easier to use the full potential of the hardware is understandable. Also keep in mind that it's a research project and very early in its development.

adev_ 7y ago
> CUDA is a pest that needs to either open up or be replaced. My main problem with cuda is not that it is un-free, it is that the source code is unavailable and consequently you have to deal with a badly packaged, badly supported blob that turn only on a restricted set of compilers. The consequences of that is that every software depending on cuda is a pain in the butt to distribute. Even major software like Pytorch or tensorflow need to have separated released due to cuda. It is particularly sad that Nvidia in 2020 did not understand what Mellanox understood 10 years ago: that successful leadership in hardware is compatible OSS with an software stack.
pjmlp 7y ago
Thank Khronos for being too focused on C and expecting partners to develop any kind of tooling or SDKs. It took the CUDA bulldozer for them to consider more C++ love and introduction of bytecode formats that allow more languages to easily target Khronos standards. Incidentally that were the biggest complains about Vulkan as well, on the last LunarG survey, tooling and documentation.
queensnake 7y ago
Intel’s OneAPI promises to translate away from CUDA: https://software.intel.com/oneapi https://software.intel.com/oneapi
fluffything 7y ago
> I'm honestly much more worried about NVIDIA's overwhelming success in buying their way into the entire scientific community. They bought their way by providing software along with the hardware, and by paying developers and researchers to work full-time in improving the performance of user applications on their hardware. If you are a medium-ly important HPC application, the chances that there is an NVIDIA engineer working full-time on making it perform good on GPUs is quite large. What's worrying is that Intel and AMD haven't realized, 20 years later, that hardware without software is useless. Even if they were to make GPUs that outperform NVIDIA's hardware in raw numbers by a 10x factor, NVIDIA's GPUs would still probably be the better product to buy because of the software and support you get.

by qayxc 7y ago

adev_ 7y ago

> CUDA is a pest that needs to either open up or be replaced. My main problem with cuda is not that it is un-free, it is that the source code is unavailable and consequently you have to deal with a badly packaged, badly supported blob that turn only on a restricted set of compilers. The consequences of that is that every software depending on cuda is a pain in the butt to distribute. Even major software like Pytorch or tensorflow need to have separated released due to cuda. It is particularly sad that Nvidia in 2020 did not understand what Mellanox understood 10 years ago: that successful leadership in hardware is compatible OSS with an software stack.

pjmlp 7y ago

Thank Khronos for being too focused on C and expecting partners to develop any kind of tooling or SDKs. It took the CUDA bulldozer for them to consider more C++ love and introduction of bytecode formats that allow more languages to easily target Khronos standards. Incidentally that were the biggest complains about Vulkan as well, on the last LunarG survey, tooling and documentation.

queensnake 7y ago

Intel’s OneAPI promises to translate away from CUDA: https://software.intel.com/oneapi https://software.intel.com/oneapi

fluffything 7y ago

> I'm honestly much more worried about NVIDIA's overwhelming success in buying their way into the entire scientific community. They bought their way by providing software along with the hardware, and by paying developers and researchers to work full-time in improving the performance of user applications on their hardware. If you are a medium-ly important HPC application, the chances that there is an NVIDIA engineer working full-time on making it perform good on GPUs is quite large. What's worrying is that Intel and AMD haven't realized, 20 years later, that hardware without software is useless. Even if they were to make GPUs that outperform NVIDIA's hardware in raw numbers by a 10x factor, NVIDIA's GPUs would still probably be the better product to buy because of the software and support you get.