Several optimization methods of half-precision general matrix multiplication (HGEMM) using tensor core with WMMA API and MMA PTX instruction.

code for benchmarking GPU performance based on cublasSgemm and cublasHgemm

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

Algorithms implemented in CUDA + resources about GPGPU

Code for testing the native float16 matrix multiplication performance on Tesla P100 and V100 GPU based on cublasHgemm

Several optimization methods of half-precision general matrix vector multiplication (HGEMV) using CUDA core.

bilibili视频【CUDA 12.x 并行编程入门(C++版)】配套代码

CUDA kernel functions

Use tensor core to calculate back-to-back HGEMM (half-precision general matrix multiplication) with MMA PTX instruction.

Harness the power of GPU acceleration for fusing visual odometry and IMU data with an advanced Unscented Kalman Filter (UKF) implementation. Developed in C++ and utilizing CUDA, cuBLAS, and cuSOLVER, this system offers unparalleled real-time performance in state and covariance estimation for robotics and autonomous system applications.

CuPy first example computing GEMM with cuBlas, with handwritten cuda kernel and with NumPy-blas

A MNIST handwritten digit classifier written from scratch in Cuda - C

Lab exercise of Parallel Processing course in NTUA regarding CUDA programming

Simple VGG16 implemented in CUDA

gpumm - matrix-matrix multiplication by using CUDA, cublas, cublasxt and OpenACC.

Matrix Exponential Approximation using CUDA

cuBLAS GEMM Example for FP32 MatMul

A CUBLAS‐CUDA Based Implementation of Multi-GPU Large Matrix Multiplication

cuBLAS for pycu_interface.