Alternatives and similar repositories for pytorch_cmake_example:

Users that are interested in pytorch_cmake_example are comparing it to the libraries listed below

• wzsh / wmma_tensorcore_sample
  Matrix Multiply-Accumulate with CUDA and WMMA( Tensor Core)
  ☆124Updated 4 years ago
• google-research / sputnik
  A library of GPU kernels for sparse matrix operations.
  ☆255Updated 4 years ago
• sunlex0717 / DissectingTensorCores
  ☆87Updated 10 months ago
• parasj / checkmate
  Training neural networks in TensorFlow 2.0 with 5x less memory
  ☆130Updated 2 years ago
• leimao / CUDA-GEMM-Optimization
  CUDA Matrix Multiplication Optimization
  ☆161Updated 7 months ago
• NVIDIA / nsight-training
  Training material for Nsight developer tools
  ☆148Updated 6 months ago
• GPUPeople / spECK
  Efficient SpGEMM on GPU using CUDA and CSR
  ☆51Updated last year
• dumerrill / merge-spmv
  ☆93Updated 8 years ago
• Cjkkkk / CUDA_gemm
  A simple high performance CUDA GEMM implementation.
  ☆346Updated last year
• wangzyon / NVIDIA_SGEMM_PRACTICE
  Step-by-step optimization of CUDA SGEMM
  ☆284Updated 2 years ago
• hgyhungry / ge-spmm
  ☆105Updated 3 years ago
• wmmae / wmma_extension
  An extension library of WMMA API (Tensor Core API)
  ☆88Updated 7 months ago
• ParCIS / Magicube
  Magicube is a high-performance library for quantized sparse matrix operations (SpMM and SDDMM) of deep learning on Tensor Cores.
  ☆85Updated 2 years ago
• cmpute / pytorch-cmake-example
  Example to build PyTorch CUDA extension using CMake (with pybind11 and scikit-build)
  ☆11Updated 4 years ago
• uwsampl / SparseTIR
  SparseTIR: Sparse Tensor Compiler for Deep Learning
  ☆134Updated last year
• cwpearson / nvidia-performance-tools
  Instructions, Docker images, and examples for Nsight Compute and Nsight Systems
  ☆130Updated 4 years ago
• YulhwaKim / cutlass_tilesparse
  CUDA templates for tile-sparse matrix multiplication based on CUTLASS.
  ☆49Updated 6 years ago
• nvixnu / pmpp__programming_massively_parallel_processors
  Examples and exercises from the book Programming Massively Parallel Processors - A Hands-on Approach. David B. Kirk and Wen-mei W. Hwu (T…
  ☆64Updated 4 years ago
• daadaada / turingas
  Assembler for NVIDIA Volta and Turing GPUs
  ☆212Updated 3 years ago
• Huanghongru / SGEMM-Implementation-and-Optimization
  Some source code about matrix multiplication implementation on CUDA
  ☆35Updated 6 years ago
• ColfaxResearch / cutlass-kernels
  ☆181Updated 7 months ago
• UDC-GAC / venom
  A Vectorized N:M Format for Unleashing the Power of Sparse Tensor Cores
  ☆48Updated last year
• pku-liang / FlexTensor
  Automatic Schedule Exploration and Optimization Framework for Tensor Computations
  ☆176Updated 2 years ago
• sjfeng1999 / gpu-arch-microbenchmark
  Dissecting NVIDIA GPU Architecture
  ☆88Updated 2 years ago
• IntelLabs / FP8-Emulation-Toolkit
  PyTorch extension for emulating FP8 data formats on standard FP32 Xeon/GPU hardware.
  ☆105Updated 2 months ago
• parasailteam / coconet
  ☆75Updated 2 years ago
• dgSPARSE / dgSPARSE-Lib
  PyTorch-Based Fast and Efficient Processing for Various Machine Learning Applications with Diverse Sparsity
  ☆104Updated this week
• yzhaiustc / Optimizing-SGEMM-on-NVIDIA-Turing-GPUs
  Optimizing SGEMM kernel functions on NVIDIA GPUs to a close-to-cuBLAS performance.
  ☆324Updated last month
• microsoft / microxcaling
  PyTorch emulation library for Microscaling (MX)-compatible data formats
  ☆199Updated 4 months ago
• KnowingNothing / MatmulTutorial
  A Easy-to-understand TensorOp Matmul Tutorial
  ☆316Updated 5 months ago