cublas

Python interface to GPU-powered libraries

Deep Learning library using GPU(CUDA/cuBLAS)

Parallel Computing Library for Linux and macOS & NVIDIA CUDA Wrapper

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

Algorithms implemented in CUDA + resources about GPGPU

code for benchmarking GPU performance based on cublasSgemm and cublasHgemm

Escoin: Efficient Sparse Convolutional Neural Network Inference on GPUs

Tools to run and parse MKL verbose mode

Lab exercise of Parallel Processing course in NTUA regarding CUDA programming

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。

Examples showing how to utilize the NVML library for GPU monitoring

Python interface to the NVIDIA CublasXt API

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

HSD: Hierarchical Spherical Defomration for Cortical Surface Registration

Safe CUDA cuBLAS wrapper for the Rust language.

Matrix multiplication example performed with OpenMP, OpenACC, BLAS, cuBLABS, and CUDA

C++ CUDA-compatible template class that provides an interface for generic purpose matrix related algorithms and computations. Includes Matlab-like functions. This is mainly an example of how to use CUDA code with C++. Don't expect such high performances.

A comparison between single thread program against multi-threading and CUDA approaches

Basel morphable face model mesh and texture generator using GPU.

🐮 Harness the power of the GPU with '((((((cuBLAS in Common Lisp

Generalized Orthogonal Least-Squares in CUDA

CUDA kernel functions

GPGPU Inverse Distance Weighting using matrix vector multiplication

Cuda NN inference

Library for profiling the execution time of CUDA official library functions

A neat C++ custom Matrix class to perform super-fast GPU (or CPU) powered Matrix/Vector computations with minimal code, leveraging the power of cuBLAS where applicable.

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

A general k-means algorithm with L2 distance using pyCUDA