cublas

NumPy & SciPy for GPU

Python interface to GPU-powered libraries

Deep Learning library using GPU(CUDA/cuBLAS)

Parallel Computing Library for Linux and macOS & NVIDIA CUDA Wrapper

Safe rust wrapper around CUDA toolkit

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

code for benchmarking GPU performance based on cublasSgemm and cublasHgemm

Matrix multiplication on GPUs for matrices stored on a CPU. Similar to cublasXt, but ported to both NVIDIA and AMD GPUs.

Examples showing how to utilize the NVML library for GPU monitoring

Hooked CUDA-related dynamic libraries by using automated code generation tools.

Tools to run and parse MKL verbose mode

Escoin: Efficient Sparse Convolutional Neural Network Inference on GPUs

Real-time GPU Beamformer for DSA110 written in C/CUDA

Lab exercise of Parallel Processing course in NTUA regarding CUDA programming

Basel morphable face model mesh and texture generator using GPU.

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。

A multilayer perceptron (for simple image classification), accelerated with CUDA

HSD: Hierarchical Spherical Deformation for Cortical Surface Registration

Python interface to the NVIDIA CublasXt API