My attempt of making a GEMM kernel...
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Updated
Jun 16, 2023 - Cuda
My attempt of making a GEMM kernel...
Manually optimize the GEMM (GEneral Matrix Multiply) operation. There is a long way to go.
Case Studies for using Accera - the open source cross-platform compiler from Microsoft Research - to create high performance deep learning computations (i.e. GEMM, Convolution, etc.)
Implementations of SGEMM algorithm on Nvidia GPU using different tricks to optimize the performance.
Fast SpMM implementation on GPUs for GNN (IPDPS'23)
My experiments with convolution
Implementations of DGEMM algorithm using different tricks to optimize the performance.
phiGEMM: CPU-GPU hybrid matrix-matrix multiplication library
Fast Matrix Multiplication Implementation in C programming language. This matrix multiplication algorithm is similar to what Numpy uses to compute dot products.
ConvLIB is a library of convolution kernels for multicore processors with ARM (NEON) or RISC-V architecture
The repository targets the OpenCL gemm function performance optimization. It compares several libraries clBLAS, clBLAST, MIOpenGemm, Intel MKL(CPU) and cuBLAS(CUDA) on different matrix sizes/vendor's hardwares/OS. Out-of-the-box easy as MSVC, MinGW, Linux(CentOS) x86_64 binary provided. 在不同矩阵大小/硬件/操作系统下比较几个BLAS库的sgemm函数性能,提供binary,开盒即用。
row-major matmul optimization
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