Highly unlikely to be a good idea. The CUDA compiler is based on LLVM, an extremly powerful framework for code transformations, i.e. optimizations. If you run into the compiler optimizing away code that you don
A basic transformation kernel is really not any more difficult to write using a grid-stride paradigm, so various CUDA coders may choose to use it “always” or as a matter of course. As a replacement for 1. Nothing wrong with that. TBH, I don’t understand the need to look at item ...
but I want to mention two important fields here:majorandminor.These describe the compute capability of the device, which is typically given inmajor.minorformat and indicates the architecture generation. The first CUDA-capable device in the Tesla product line was the Tesla...
checkCuda( cudaMemset(d_a, 0.0, n * sizeof(T)) ); checkCuda( cudaEventRecord(startEvent,0) ); offset<<>>(d_a, i); checkCuda( cudaEventRecord(stopEvent,0) ); checkCuda( cudaEventSynchronize(stopEvent) ); checkCuda( cudaEventElapsedTime(&ms, startEvent, stopEvent) ); printf("%d...
Found 1 devices supporting CUDA. CUDA Device # 0 properties - CUDA device details: Name: GeForce 8800 GT Compute capability: 1.1 Total Video Memory: 1023MB CUDA driver version: 3010 CUDA Device # 0 supported. Completed shader test! Internal return value: 7 ...
64-bit Intel or AMD CPU with AVX2 support, or Windows 11 on Snapdragon compute platform, or ARM v8.1 64-bit CPU NVIDIA GPU with CUDA compute capability 5.0 or higher with 8 GB VRAM, or AMD "Navi" or "Vega" GPU or later with HIP capability and 8 GB VRAM or more ...
ps: a quick question, what GPU do you recommend to run the repo? Is one A100 40G enough or I need one A100 80G. [Bug]: When tensor_parallel_size>1, RuntimeError: Cannot re-initialize CUDA in forked subprocess.vllm-project/vllm#6152 ...
Moreover, CUDA toolkit was discontinued for MacOS, starting CUDA Toolkit 11. So, for such systems, I suppose one would have to try building from source for lower Compute Capability and CUDA Toolkit 10.x instead. 👍 1 Sign up for free to join this conversation on GitHub. Already have ...
Tensorflow can make use of NVIDIA GPUs with CUDA compute capabilities to speed up computations. To reserve NVIDIA GPUs, we edit the docker-compose.yaml that we defined previously and add the deploy property under thetrainingservice as follows: ...
On a fresh Ubuntu system, you need to first install the proprietary NVIDIA driver and CUDA. The latter ensures you get the OpenCL framework bundled with it. Finally, install theclinfoprogram to ensure you have OpenCL properly installed, showing you your NVIDIA GPU's OpenCL specifications in ...