high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

Understanding the efficiency of GPU algorithms for matrix-matrix multiplication

K. Fatahalian,J. Sugerman,P. Hanrahan

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Tags: Algorithms, ATI, ATI Radeon 9800 XT, Computer science, Linear Algebra, nVidia, nVidia GeForce 6800 Ultra, nVidia GeForce FX 5900 Ultra

October 27, 2010 by hgpu

GPU Based Acceleration of Telegraph Equation

Vaclav Simek, Michal Kraus, Kunovsky Jiri, Jiri Petrek

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Tags: Algorithms, Computer science, CUDA, nVidia

October 27, 2010 by hgpu

Importance of Explicit Vectorization for CPU and GPU Software Performance

Neil G. Dickson, Kamran Karimi, Firas Hamze

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Tags: Computer science, CUDA, Monte Carlo simulation, nVidia, nVidia GeForce GTX 285, Physics

October 27, 2010 by hgpu

GPU computing for systems biology

Lorenzo Dematte, Davide Prandi

Tags: Biology

October 27, 2010 by hgpu

On the limits of GPU acceleration

Richard Vuduc, Aparna Chandramowlishwaran, Jee Choi, Murat Guney, Aashay Shringarpure

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Tags: Computer science, nVidia, nVidia GeForce GTX 285, Performance, Tesla C1060, Tesla S1070

October 27, 2010 by hgpu

GPU sample sort

Nikolaj Leischner, Vitaly Osipov, Peter Sanders

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Tags: Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 285, Sorting, Tesla C1060

October 27, 2010 by hgpu

Accelerating video decoding using GPU

Guobin Shen,Lihua Zhu,Shipeng Li,Heung-Yeung Shum,Ya-Qin Zhang

Tags: Computer science, nVidia, Video decoding

October 27, 2010 by hgpu

GPU implementation of neural networks

K. Oh

Tags: ATI, ATI Radeon 9700 Pro, Computer science, Image processing, Neural networks, Text detection

October 27, 2010 by hgpu

The GPU Computing Era

John Nickolls,William J. Dally

Tags: Computer science, Review

October 27, 2010 by hgpu

GPU Computing

John D. Owens,Mike Houston,David Luebke,Simon Green,John E. Stone,James C. Phillips

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Tags: Biophysics, Computer science, Game physics, Review

October 27, 2010 by hgpu

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice

HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments

BAT: A standardized benchmark suite for auto-tuners

BAT: A Benchmark suite for AutoTuners

The Darmstadt Automotive Parallel HeterogeNEous (DAPHNE) Benchmark-Suite

Programming Heterogeneous Systems with General and Domain-Specific Frameworks

General purpose lattice QCD code set Bridge++ 2.0 for high performance computing

MQBench: Towards Reproducible and Deployable Model Quantization Benchmark

Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py

iGUARD: In-GPU Advanced Race Detection

TaPaSCo: The Task Parallel System Composer

Optimizing a Hardware Network Stack to Realize an In-Network ML Inference Application

Tapenade Automatic Differentiation Engine

Source-to-Source Automatic Differentiation of OpenMP Parallel Loops

Colossal-AI: an integrated large-scale model training system with efficient parallelization techniques

Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training

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high performance computing on graphics processing units: hgpu.org

Applications

Understanding the efficiency of GPU algorithms for matrix-matrix multiplication

GPU Based Acceleration of Telegraph Equation

Importance of Explicit Vectorization for CPU and GPU Software Performance

GPU computing for systems biology

On the limits of GPU acceleration

GPU sample sort

Accelerating video decoding using GPU

GPU implementation of neural networks

The GPU Computing Era

GPU Computing

Recent source codes

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice

BAT: A standardized benchmark suite for auto-tuners

The Darmstadt Automotive Parallel HeterogeNEous (DAPHNE) Benchmark-Suite

General purpose lattice QCD code set Bridge++ 2.0 for high performance computing

MQBench: Towards Reproducible and Deployable Model Quantization Benchmark

Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py

iGUARD: In-GPU Advanced Race Detection

TaPaSCo: The Task Parallel System Composer

Tapenade Automatic Differentiation Engine

Colossal-AI: an integrated large-scale model training system with efficient parallelization techniques

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