high performance computing on graphics processing units: hgpu.org

hgpu.org » Graph theory

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

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

View

Download (PDF)

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

July 21, 2018 by hgpu

Multicore architecture and cache optimization techniques for solving graph problems

Alvaro Tzul

View

Download (PDF)

Tags: Computer science, CUDA, Graph theory, nVidia

July 15, 2018 by hgpu

Application of Deep-Learning to Compiler-Based Graphs

Tristan Vanderbruggen

View

Download (PDF)

Tags: Compilers, Computer science, Deep learning, Graph theory, Machine learning, Neural networks, nVidia, nVidia GRID K520, OpenCL, Thesis

July 7, 2018 by hgpu

An Efficient Dispatcher for Large Scale GraphProcessing on OpenCL-based FPGAs

Chengbo Yang

View

Download (PDF)

Tags: Computer science, FPGA, Graph theory, OpenCL, Task scheduling

July 7, 2018 by hgpu

A Comparative Study on Exact Triangle Counting Algorithms on the GPU

Leyuan Wang, Yangzihao Wang, Carl Yang, John D. Owens

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Filtering, Graph theory, nVidia, Sparse matrix, Tesla K40

April 28, 2018 by hgpu

Implementing Push-Pull Efficiently in GraphBLAS

Carl Yang, Aydin Buluc, John D. Owens

View

Download (PDF)

Tags: Algorithm optimization, Algorithms, BLAS, Computer science, CUDA, Graph theory, Linear Algebra, nVidia, Sparse matrix, Tesla K40

April 15, 2018 by hgpu

Graph Processing on GPUs: A Survey

Xuanhua Shi, Zhigao Zheng, Yongluan Zhou, Hai Jin, Ligang He, Bo Liu, Qiang-Sheng Hua

View

Download (PDF)

Tags: Computer science, CUDA, Graph theory, nVidia, OpenCL, survey

January 13, 2018 by hgpu

A Dynamic Hash Table for the GPU

Saman Ashkiani, Martin Farach-Colton, John D. Owens

View

Download (PDF)

Tags: Computer science, CUDA, Graph theory, Hashing, nVidia, Tesla K40

November 5, 2017 by hgpu

Computing Treewidth on the GPU

Tom C. van der Zanden, Hans L. Bodlaender

View

Download (PDF)

Source codes

Tags: Algorithms, Bloom filter, Computer science, Graph theory, nVidia, nVidia GeForce GTX 1060, OpenCL, Package

October 3, 2017 by hgpu

Performance Characterization of Multi-threaded Graph Processing Applications on Intel Many-Integrated-Core Architecture

Xu Liu, Langshi Chen, Jesun S. Firoz, Judy Qiu, Lei Jiang

View

Download (PDF)

Tags: Computer science, CUDA, Graph theory, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1070

August 17, 2017 by hgpu

GARDENIA: A Domain-specific Benchmark Suite for Next-generation Accelerators

Zhen Xu, Xuhao Chen, Jie Shen, Yang Zhang, Cheng Chen, Canqun Yang

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, Graph theory, Machine learning, nVidia, Package, Tesla K40

August 17, 2017 by hgpu

Using Graph Properties to Speed-up GPU-based Graph Traversal: A Model-driven Approach

Merijn Verstraaten, Ana Lucia Varbanescu, Cees de Laat

View

Download (PDF)

Source codes

Tags: Algorithms, Benchmarking, Computer science, CUDA, Graph theory, Machine learning, nVidia, nVidia GeForce GTX Titan X, Package

August 8, 2017 by hgpu

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Anari-visionaray

Data Parallel Visualization and Rendering on the RAMSES Supercomputer with ANARI

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads

* * *

high performance computing on graphics processing units: hgpu.org

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

Multicore architecture and cache optimization techniques for solving graph problems

Application of Deep-Learning to Compiler-Based Graphs

An Efficient Dispatcher for Large Scale GraphProcessing on OpenCL-based FPGAs

A Comparative Study on Exact Triangle Counting Algorithms on the GPU

Implementing Push-Pull Efficiently in GraphBLAS

Graph Processing on GPUs: A Survey

A Dynamic Hash Table for the GPU

Computing Treewidth on the GPU

Performance Characterization of Multi-threaded Graph Processing Applications on Intel Many-Integrated-Core Architecture

GARDENIA: A Domain-specific Benchmark Suite for Next-generation Accelerators

Using Graph Properties to Speed-up GPU-based Graph Traversal: A Model-driven Approach

Recent source codes

SCALE-Ahead-Of-Time Compilation of CUDA for AMD GPUs

LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations

Anari-visionaray

PROM: an open-source software framework to enhance the robustness and performance of predictive models against such changes during deployment

DeepSpeed: a deep learning optimization library that makes distributed training and inference easy, efficient, and effective

HPX: a C++ Standard Library for Concurrency and Parallelism

TorchQC: Quantum Dynamics and Machine Learning

Matrix multiplication using Tensor Cores in CUDA

CPP Joules: Energy Measurement tool for CPP/CUDA programs

HPC-Coder-v2

Most viewed papers (last 30 days)