high performance computing on graphics processing units: hgpu.org

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Automatic generation of warp-level primitives and atomic instructions for fast and portable parallel reduction on GPUs

Simon Garcia De Gonzalo, Sitao Huang, Juan Gomez-Luna, Simon Hammond, Onur Mutlu, Wen-mei Hwu

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 980, Performance, performance portability, Tesla K40, Tesla P100

May 26, 2019 by hgpu

Modeling and Evaluation of Synchronous Stochastic Gradient Descent in Distributed Deep Learning on Multiple GPUs

Shaohuai Shi, Qiang Wang, Xiaowen Chu, Bo Li

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Tags: Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, Package, Performance, Tesla K80

May 12, 2018 by hgpu

Portable Real-Time DCT Based Steganography Using OpenCL

Ante Poljicak, Guillermo Botella, Carlos Garcia, Luka Kedmenec, Manuel Prieto-Matias

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Tags: APU, ARM, ATI, ATI Radeon HD 6870, Image processing, nVidia, nVidia GeForce GTX 980, OpenCL, real time systems

March 10, 2018 by hgpu

Performance Evaluation of Deep Learning Tools in Docker Containers

Pengfei Xu, Shaohuai Shi, Xiaowen Chu

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Tags: Benchmarking, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, Package, Performance

November 12, 2017 by hgpu

Accelerating digital forensic searching through GPGPU parallel processing techniques

Ethan Bayne

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Tags: Computer science, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX 970 M, nVidia GeForce GTX 980, nVidia GeForce GTX 980 Ti, OpenCL, Thesis

August 14, 2017 by xhgpu

GPU Array Access Auto-Tuning

Nicolas Weber

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 440, nVidia GeForce GT 620, nVidia GeForce GT 730, nVidia GeForce GTX 1080, nVidia GeForce GTX 480, nVidia GeForce GTX 560 Ti, nVidia GeForce GTX 570, nVidia GeForce GTX 590, nVidia GeForce GTX 680, nVidia GeForce GTX 780, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, Performance, performance portability, Tesla C2070, Tesla K20, Thesis

August 8, 2017 by hgpu

Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis

Jesus Perez Serrano, Edans Flavius De Oliveira Sandes, Alba Cristina Magalhaes Alves de Melo, Manuel Ujaldon

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Tags: Bioinformatics, Biology, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 980, Performance, Smith-Waterman algorithm

June 10, 2017 by hgpu

GPUMap: A Transparently GPU-Accelerated Map Function

Ivan Pachev

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Tags: Computer science, CUDA, Distributed computing, Java, MapReduce, nVidia, nVidia GeForce GTX 980, Python, Thesis

May 22, 2017 by hgpu

Group Marching Tree: Sampling-Based Approximately Optimal Motion Planning on GPUs

Brian Ichter, Edward Schmerling, Marco Pavone

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 980, nVidia Tegra TX1, Robotics

May 18, 2017 by hgpu

A hybrid CPU-GPU parallelization scheme of variable neighborhood search for inventory optimization problems

Nikolaos Antoniadis, Angelo Sifaleras

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Tags: Computer science, Metaheuristics, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 960, nVidia GeForce GTX 980, OpenACC, OpenMP, Optimization

April 20, 2017 by hgpu

Machine Learning from Streaming Data in Heterogeneous Computing Environments

Yusuf Guven Toprakkiran

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Tags: Algorithms, AMD Radeon R9 Fury, ATI, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 980, OpenCL, Thesis

April 11, 2017 by hgpu

Energy conservation techniques for GPU computing

Xinxin Mei

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Tags: Computer science, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 780, nVidia GeForce GTX 980, Task scheduling, Thesis

March 28, 2017 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Automatic generation of warp-level primitives and atomic instructions for fast and portable parallel reduction on GPUs

Modeling and Evaluation of Synchronous Stochastic Gradient Descent in Distributed Deep Learning on Multiple GPUs

Portable Real-Time DCT Based Steganography Using OpenCL

Performance Evaluation of Deep Learning Tools in Docker Containers

Accelerating digital forensic searching through GPGPU parallel processing techniques

GPU Array Access Auto-Tuning

Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis

GPUMap: A Transparently GPU-Accelerated Map Function

Group Marching Tree: Sampling-Based Approximately Optimal Motion Planning on GPUs

A hybrid CPU-GPU parallelization scheme of variable neighborhood search for inventory optimization problems

Machine Learning from Streaming Data in Heterogeneous Computing Environments

Energy conservation techniques for GPU computing

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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