![]() _global_ void kernelDefault(_grid_constant_ const param_t p. CUDA Toolkit 11.2 (TensorFlow) 11.6 (PyTorch). #define CONST_COPIED_PARAMS (TOTAL_PARAMS - KERNEL_PARAM_LIMIT) If your GPU is compatible, install NVIDIA drivers, Toolkit and models for TensorFlow and PyTorch. If we want to fully explore the function of the CUDA 11.2 toolkit, we can install PyTorch v1.9.0 in the developer mode. Would it be possible to make available a version of nvprof (or more generally, the CUDA toolkit) compatible with CUDA 11.2 I’d be happy to give feedback on any early beta testing if so. #define KERNEL_PARAM_LIMIT (1024) // ints CUDA Device Query (Runtime API) version (CUDART static linking) Detected 1 CUDA Capable device (s) Device 0: 'GeForce GTX 1060 6GB' CUDA Driver Version / Runtime Version 11.2 / 11.2 CUDA Capability Major/Minor version number: 6.1 Total amount of global memory: 6078 MBytes (6373310464 bytes) MapSMtoCores for SM 6. NVIDIA-SMI 460.20 Driver Version: 460.20 CUDA Version: 11.2 so the version of CUDA built for WSL2 is actually too new. Previously, passing kernel arguments exceeding 4,096 bytes required working around the kernel parameter limit by copying excess arguments into constant memory with cudaMemcpyToSymbol or cudaMemcpyToSymbolAsync, as shown in the snippet below. CUDA 12.1 increases this parameter limit from 4,096 bytes to 32,764 bytes on all device architectures including NVIDIA Volta and above. ![]() CUDA kernel function parameters are passed to the device through constant memory and have been limited to 4,096 bytes.
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