![]() Therefore, most GPU programming is done on CUDA. NVIDIA clearly takes a notable lead in the current AI landscape as it primarily focuses on GPGPU Programming, whereas AMD focuses on gaming. The bottom lineĬonsidering the above discussion, the conclusion isn’t all too surprising. However, the real battleground is on compilers and Nvidia devoted significant attention to them from the very beginning. So much work is being put into handcrafting optimizations for large-scale ML deployments that are hardware specific at the moment. Furthermore, since Switching costs for researchers and developers are not insignificant, that is an additional barrier they need to break through. So now, not only does AMD need to work on R&D to build better (or at least on par) products, but it also needs to drive the adoption of its ecosystem. Late in the gameĪMD has evidently been behind in this race for almost a decade-thereby, leading to a much wider adoption of the CUDA ecosystem. GUI-based software applications are currently not supported. The ROCm open software platform is a compute stack for system deployments. On the other hand, ROCm ( Radeon Open Compute) doesn’t work on Radeon cards ( RDNA) or Windows. One can also distribute it to other users. Users can write and run CUDA code if they buy an Nvidia GPU. AMD’s FidelityFX Super Resolution offers similar features and works on almost any GPU but has no solid answer to tensor cores.Īnother reason AMD is so far behind is its lack of support for its own platforms. įurthermore, NVIDIA cards now have tensor cores that can run faster for training and inference on AI models. A study comparing CUDA programmes with OpenCL on NVIDIA GPUs showed that CUDA was 30% faster than OpenCL. Despite the claims that it has an API that can match CUDA, it isn’t easy to use. But in comparison, CUDA is more stable and modern and has better compatibility. The closest alternative to it is OpenCL (Open Computing Language). Most of the progress in AI in the past decade has been made using CUDA libraries, majorly because AMD didn’t have a functional alternative. It surprises me that having NUM_TD_PER_THREAD and _cuda.Your newsletter subscriptions are subject to AIM Privacy Policy and Terms and Conditions. NUM_TD_PER_THREAD does not influence the number of blocks/threads launched Printing the number of blocks used to launch the kernel the only parameter able to do this is _cuda.posesPerWI parameter. Because of this I am expecting an equivalent reduction in the in the number of blocks or total threads in the kernel launching. In the sense that clearly NUM_TD_PER_THREAD indicate the factor of work each thread take in the kernel. Maybe I missed something, but also the logic of the code does not add-up to me. I do not put in doubt that the author of miniBUDE could have said that NUM_TD_PER_THREAD is the only parameter you have to tune, but in addition to the wrong results, I was also cheking the code. Up to now I tried any deck, any GPU, either 65536 either 131072, and I did not see one case where using NUM_TD_PER_THREAD different from _cuda.posesPerWI produces "Largest difference was " near to zero. For correct result I do not refer to the performance, but I refer to the correctness of the result. I make sure that shared is used on both cases and NUM_TD_PER_THREAD=4 Hipcc -O3 -march=native -std=c++14 -ffast-math -mcpu=gfx900:xnack-DUSE_SHARED bude.cpp -o bude I attach the CUDA version converted to HIP because is condensated everything in one file can be compiled easily with It seems that OpenCL has the tendency to create a kernel that use much more register (with less occupancy) than the one with HIP. (-ffast-math to emulate the OpenCL options) Activate shared memory (because the OpenCL version use shared memory). converted manually to hip (just have to change some cuda function into hip). Than I also tried to take the CUDA version. ![]() I tried to run that benchmark on my Vega 64. Is a benchmark that came out of ISC 2021 as best paper award. I was trying to asses the performance of HIP vs OpenCL First I wanted to thanks for creating the HIP interface.
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