[ARCHIVED] Cooperative primitives for CUDA C++. See https://github.com/NVIDIA/cccl

NCCL Tests

Introduction to Parallel Programming class code

Training materials associated with NVIDIA's CUDA Training Series (www.olcf.ornl.gov/cuda-training-series/)

Examples demonstrating available options to program multiple GPUs in a single node or a cluster

Efficient Distributed GPU Programming for Exascale, an SC/ISC Tutorial

A CUDNN minimal deep learning training code sample using LeNet.

Efficient Top-K implementation on the GPU

Code samples for the CUDA tutorial "CUDA and Applications to Task-based Programming"

Tartan: Evaluating Modern GPU Interconnect via a Multi-GPU Benchmark Suite

Implementation and analysis of five different GPU based SPMV algorithms in CUDA

CUDA Dynamic Memory Allocator for SOA Data Layout

A Winograd Minimal Filter Implementation in CUDA

graph challenge 2021

Source code for the CPU-Free model - a fully autonomous execution model for multi-GPU applications that completely excludes the involvement of the CPU beyond the initial kernel launch.

This repository contains the source code for our ACM SIGMOD '22 paper (Evaluating Multi-GPU Sorting with Modern Interconnects)

try newly released `cudaLaunchCooperativeKernelMultiDevice()` in CUDA C++