high performance computing on graphics processing units: hgpu.org

hgpu.org » Algorithms

Large-Scale Stochastic Learning using GPUs

Thomas Parnell, Celestine Dunner, Kubilay Atasu, Manolis Sifalakis, Haris Pozidis

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX Titan X, nVidia Quadro M 4000

February 26, 2017 by hgpu

Blocking Self-avoiding Walks Stops Cyber-epidemics: A Scalable GPU-based Approach

Hung T. Nguyen, Alberto Cano, Tam Vu, Thang N. Dinh

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Tags: Algorithms, Computer science, CUDA, Network communications, nVidia, nVidia GeForce GTX Titan X

February 22, 2017 by hgpu

An Efficient Parallel Data Clustering Algorithm Using Isoperimetric Number of Trees

Ramin Javadi, Saleh Ashkboos

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Tags: Algorithms, Clustering, Computer science, CUDA, nVidia, nVidia GeForce GTX 850 M

February 18, 2017 by hgpu

Trie Compression for GPU Accelerated Multi-Pattern Matching

Xavier Bellekens, Amar Seeam, Christos Tachtatzis, Robert Atkinson

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Tags: Algorithms, Compression, Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 1080

February 18, 2017 by hgpu

MapSQ: A MapReduce-based Framework for SPARQL Queries on GPU

Jiaying Feng, Xiaowang Zhang, Zhiyong Feng

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Databases, MapReduce, nVidia, nVidia GeForce GTX 590

February 18, 2017 by hgpu

Improving the Performance of Fully Connected Neural Networks by Out-of-Place Matrix Transpose

Shaohuai Shi, Pengfei Xu, Xiaowen Chu

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Tags: Algorithms, BLAS, Caffe, Computer science, CUBLAS, CUDA, Deep learning, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 1080, Package, Performance

February 14, 2017 by hgpu

Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC

Eriko Nurvitadhi, David Sheffield, Jaewoong Sim, Asit Mishra, Ganesh Venkatesh, Debbie Marr

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Tags: Algorithms, ASIC, Computer science, CUDA, Deep learning, FPGA, Neural networks, nVidia, nVidia GeForce GTX Titan X, nVidia Tegra TX1, Performance

February 14, 2017 by hgpu

GPU-Accelerated SVM Training Algorithm Based on PC and Mobile Device

Yi-Yan Nan, Quan-Zhe Li, Jin-Chun Piao, Shin-Dug Kim

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Tags: Algorithms, AMD Radeon R9 M200X, Android, ATI, Computer science, Image processing, Matrix multiplication, OpenCL, Pattern recognition

February 10, 2017 by hgpu

gearshifft – The FFT Benchmark Suite for Heterogeneous Platforms

Peter Steinbach, Matthias Werner

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Tags: Algorithms, Benchmarking, Computer science, FFT, Heterogeneous systems, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, Tesla K20, Tesla K80, Tesla P100

February 10, 2017 by hgpu

Machines and Algorithms

Peter A Boyle

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Tags: Algorithms, Computational Physics, CUDA, HEP, High Energy Physics - Lattice, Intel Xeon Phi, nVidia, Physics, QCD, Review

February 7, 2017 by hgpu

Advanced Concurrency Control Algorithm Design and GPU System Support for High Performance In-Memory Data Management

Yuan Yuan

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 680, OpenCL, Thesis

February 7, 2017 by hgpu

Fast Fourier Transforms over Prime Fields of Large Characteristic and their Implementation on Graphics Processing Units

Davood Mohajerani

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Tags: Algorithms, Computer science, CUDA, FFT, nVidia, nVidia GeForce GTX 760 M, Security, Thesis

February 5, 2017 by hgpu

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with Protein Database Search

tdg-benchs: benchmarks used to test the performance of taskgraph

Leveraging the potential of task-based programming with OpenMP task graphs

LLOR: Automatic Repair of OpenMP Programs

LLOR: Automated Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

Unified schemes for directive-based GPU offloading

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

See all packages

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high performance computing on graphics processing units: hgpu.org

Large-Scale Stochastic Learning using GPUs

Blocking Self-avoiding Walks Stops Cyber-epidemics: A Scalable GPU-based Approach

An Efficient Parallel Data Clustering Algorithm Using Isoperimetric Number of Trees

Trie Compression for GPU Accelerated Multi-Pattern Matching

MapSQ: A MapReduce-based Framework for SPARQL Queries on GPU

Improving the Performance of Fully Connected Neural Networks by Out-of-Place Matrix Transpose

Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC

GPU-Accelerated SVM Training Algorithm Based on PC and Mobile Device

gearshifft – The FFT Benchmark Suite for Heterogeneous Platforms

Machines and Algorithms

Advanced Concurrency Control Algorithm Design and GPU System Support for High Performance In-Memory Data Management

Fast Fourier Transforms over Prime Fields of Large Characteristic and their Implementation on Graphics Processing Units

Recent source codes

Matrix multiplication using Tensor Cores in CUDA

CPP Joules: Energy Measurement tool for CPP/CUDA programs

HPC-Coder-v2

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

tdg-benchs: benchmarks used to test the performance of taskgraph

LLOR: Automatic Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

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