r-barnes / pytorch_cmake_example
☆31Updated 3 years ago
Alternatives and similar repositories for pytorch_cmake_example:
Users that are interested in pytorch_cmake_example are comparing it to the libraries listed below
- Matrix Multiply-Accumulate with CUDA and WMMA( Tensor Core)☆130Updated 4 years ago
- A library of GPU kernels for sparse matrix operations.☆262Updated 4 years ago
- ☆95Updated last year
- An extension library of WMMA API (Tensor Core API)☆96Updated 9 months ago
- Step-by-step optimization of CUDA SGEMM☆310Updated 3 years ago
- CUDA Matrix Multiplication Optimization☆179Updated 9 months ago
- High Performance Grouped GEMM in PyTorch☆29Updated 2 years ago
- Dissecting NVIDIA GPU Architecture☆91Updated 2 years ago
- Training material for Nsight developer tools☆156Updated 8 months ago
- Optimizing SGEMM kernel functions on NVIDIA GPUs to a close-to-cuBLAS performance.☆337Updated 3 months ago
- ☆198Updated 9 months ago
- CUDA templates for tile-sparse matrix multiplication based on CUTLASS.☆51Updated 7 years ago
- We invite you to visit and follow our new repository at https://github.com/microsoft/TileFusion. TiledCUDA is a highly efficient kernel …☆181Updated 2 months ago
- Magicube is a high-performance library for quantized sparse matrix operations (SpMM and SDDMM) of deep learning on Tensor Cores.☆86Updated 2 years ago
- A simple high performance CUDA GEMM implementation.☆361Updated last year
- PyTorch-Based Fast and Efficient Processing for Various Machine Learning Applications with Diverse Sparsity☆108Updated 3 weeks ago
- Examples and exercises from the book Programming Massively Parallel Processors - A Hands-on Approach. David B. Kirk and Wen-mei W. Hwu (T…☆67Updated 4 years ago
- TileFusion is an experimental C++ macro kernel template library that elevates the abstraction level in CUDA C for tile processing.☆81Updated 2 weeks ago
- A Vectorized N:M Format for Unleashing the Power of Sparse Tensor Cores☆51Updated last year
- Code samples for the CUDA tutorial "CUDA and Applications to Task-based Programming"☆88Updated last year
- SparseTIR: Sparse Tensor Compiler for Deep Learning☆135Updated 2 years ago
- ☆101Updated last month
- A Easy-to-understand TensorOp Matmul Tutorial☆342Updated 7 months ago
- ☆106Updated 3 years ago
- Efficient SpGEMM on GPU using CUDA and CSR☆52Updated last year
- ☆109Updated last year
- End to End steps for adding custom ops in PyTorch.☆21Updated 4 years ago
- CUTLASS and CuTe Examples☆48Updated 3 months ago
- PyTorch extension for emulating FP8 data formats on standard FP32 Xeon/GPU hardware.☆108Updated 4 months ago
- Benchmark code for the "Online normalizer calculation for softmax" paper☆91Updated 6 years ago