high performance computing on graphics processing units: hgpu.org

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Fast Knowledge Graph Completion using Graphics Processing Units

Chun-Hee Lee, Dong-oh Kang, Hwa Jeon Song

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Tags: AI, Algorithms, Computer science, CUDA, Databases, Graph theory, nVidia, nVidia A100

July 30, 2023 by hgpu

Improving the scalability of modern applications by parallel multi-core and many-core programming

Alessandro Borione

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, nVidia GeForce RTX 3070, OpenCL, Thesis

January 15, 2023 by hgpu

Experience of Migrating a Parallel Graph Coloring Program from CUDA to SYCL

Zheming Jin

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Tags: Benchmarking, Computer science, CUDA, Graph theory, nVidia, OpenCL, Package, Performance, SYCL

May 8, 2022 by hgpu

Learning Massive Graph Embeddings on a Single Machine

Jason Mohoney, Roger Waleffe, Yiheng Xu, Theodoros Rekatsinas, Shivaram Venkataraman

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Tags: Computer science, CUDA, Databases, Graph theory, Machine learning, nVidia, Tesla V100

January 24, 2021 by hgpu

Fast Gunrock Subgraph Matching (GSM) on GPUs

Leyuan Wang, John D. Owens

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Tags: Computer science, Graph theory, nVidia, nVidia GeForce GTX Titan V, nVidia GeForce GTX Titan XP, Package

March 8, 2020 by hgpu

Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU

Xuhao Chen, Roshan Dathathri, Gurbinder Gill, Keshav Pingali

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Tags: Computer science, CUDA, Graph theory, nVidia, nVidia GeForce GTX 1080 Ti, Tesla V100

November 24, 2019 by hgpu

On-The-Fly Parallel Data Shuffling for Graph Processing on OpenCL-based FPGAs

Xinyu Chen, Ronak Bajaj, Yao Chen, Jiong He, Bingsheng He, Weng-Fai Wong, Deming Chen

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Tags: Computer science, FPGA, Graph theory, OpenCL

August 25, 2019 by hgpu

HashGraph – Scalable Hash Tables Using A Sparse Graph Data Structure

Oded Green

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Tags: Algorithms, Computer science, CUDA, Databases, Graph theory, nVidia, nVidia Quadro GV100

July 14, 2019 by hgpu

GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding

Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang

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Tags: Computer science, Graph theory, Machine learning, nVidia, nVidia GeForce GTX 1080, Tesla P100

March 10, 2019 by hgpu

SIMD-X: Programming and Processing of Graph Algorithms on GPUs

Hang Liu, H. Howie Huang

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Tags: Computer science, CUDA, Graph theory, nVidia, Tesla K20, Tesla K40, Tesla P100

December 16, 2018 by hgpu

Towards Efficient Large-Scale Graph Neural Network Computing

Lingxiao Ma, Zhi Yang, Youshan Miao, Jilong Xue, Ming Wu, Lidong Zhou, Yafei Dai

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Tags: Computer science, CUDA, Deep learning, Graph theory, Machine learning, Neural networks, nVidia, Tesla P100

October 28, 2018 by hgpu

Abelian: A Compiler for Graph Analytics on Distributed, Heterogeneous Platforms

Gurbinder Gill, Roshan Dathathri, Loc Hoang, Andrew Lenharth, Keshav Pingali

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Tags: Computer science, CUDA, Graph theory, Intel Xeon Phi, nVidia, Tesla K80

July 21, 2018 by hgpu

HPCToolkit

Refining HPCToolkit for application performance analysis at exascale

Open Catalog of Best Practices for Modernization and Optimization

Optimizing the Weather Research and Forecasting Model with OpenMP Offload and Codee

Owl: Differential-based Side-Channel Leakage Detection for CUDA Applications

pSTL-Bench: Micro-benchmark suite designed to evaluate the scalability and efficiency of parallel C++ STL implementations

Exploring Scalability in C++ Parallel STL Implementations

GPU-Smash: Compression abstraction library

A Parallel Compression Pipeline for Improving GPU Virtualization Data Transfers

Noarr Structures

Abstractions for C++ code optimizations in parallel high-performance applications

$3D-fractal-generators$

HiCCL: A hierarchical collective communications library with portable optimizations

HiCCL: A Hierarchical Collective Communication Library

See all packages

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high performance computing on graphics processing units: hgpu.org

Fast Knowledge Graph Completion using Graphics Processing Units

Improving the scalability of modern applications by parallel multi-core and many-core programming

Experience of Migrating a Parallel Graph Coloring Program from CUDA to SYCL

Learning Massive Graph Embeddings on a Single Machine

Fast Gunrock Subgraph Matching (GSM) on GPUs

Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU

On-The-Fly Parallel Data Shuffling for Graph Processing on OpenCL-based FPGAs

HashGraph – Scalable Hash Tables Using A Sparse Graph Data Structure

GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding

SIMD-X: Programming and Processing of Graph Algorithms on GPUs

Towards Efficient Large-Scale Graph Neural Network Computing

Abelian: A Compiler for Graph Analytics on Distributed, Heterogeneous Platforms

Recent source codes

HPCToolkit

Open Catalog of Best Practices for Modernization and Optimization

Owl: Differential-based Side-Channel Leakage Detection for CUDA Applications

pSTL-Bench: Micro-benchmark suite designed to evaluate the scalability and efficiency of parallel C++ STL implementations

GPU-Smash: Compression abstraction library

Noarr Structures

3D-fractal-generators

SyncPerformance

VerCors: a verification toolset for verifying parallel and concurrent software

HiCCL: A hierarchical collective communications library with portable optimizations

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