I see that you also avoid using cudaMallocPitch and cudaMemCpy2D to do the 2D matrix addition. :) I did also the same and now everything works fine. However it would be nice to know how to use these 2D functions
For this sample, let’s assume we want to use a 1D CUDA thread block with 256 threads. #include <cublasdx.hpp> using namespace cublasdx; using GEMM = decltype(Size<32, 32, 32>() + Precision<double>() + Type<type::real>() + Function<function::MM>() + Arrangement<cublasdx::row...
Moreover, we propose a unified cache hit rate computation approach to consistently investigate the caching behavior for different SpMV kernels, which may have different data deployment in the hierarchical memory space of CUDA-enabled GPUs. We have assessed LightSpMV using a set of sparse matrices ...
CUDA的计算速度与grid/block size有关,grid/block size越大则计算速度越快,但即使单grid单block计算速度也比Triton快(下表的grid/block size均设置成1024) size大小超过1048576(4MB)时,CUDA测试会core dump,测试机器free memory是6364.69MB(显卡3080),如果读者知道原因欢迎指出 Fused Softmax Triton实现Fused Softmax...
Introduction to Data Parallelism and CUDA C 3.7 Exercises 3.1. A matrix addition takes two input matrices B and C and produces one output matrix A. Each element of the output matrix A is the sum of the corresponding elements of the input matrices B and C, that is, A[i][j] == B[i...
First some hardware info: i5-4590 quadcore 3.30GHz, 64 bit(Win 7, Matlab 2016a); GeForce GT 640, 384 CUDA cores, ~1 GHz. When running the tests, I got some gains when multiplying 2 1024x1024 matrices. But when looping on 200x200 or 500x500 matrices multiplication is down for GPU ...
nvmath-python(Beta) is an open-source Python library, providing Python programmers with access to high-performance mathematical operations fromNVIDIA CUDA-X math libraries. nvmath-python provides both low-level bindings to the underlying libraries and higher-level Pythonic abstractions. It is interope...
My previous CUDA Fortran post covered the mechanics of using shared memory, including static and dynamic allocation. In this post I will show some of the…
NVIDIA A100-SXM4-80GB, CUDA 11.2, cuBLAS 11.4. 3.2. Wave Quantization While tile quantization means the problem size is quantized to the size of each tile, there is a second quantization effect where the total number of tiles is quantized to the number of multiprocessors on the GPU: Wave...
Run gst on Windows Prerequisites cuda installed msvc redistribution installed (maybe no need) gst.exe + pthreadVC3.dllAbout GPU Stress Test is a tool to stress the compute engine of NVIDIA Tesla GPU’s by running a BLAS matrix multiply using different data types. It can be compiled and run...