high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Computer science

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

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

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

Experiences in Building a Composable and Functional API for Runtime SPIR-V Code Generation

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

TorchBench: Benchmarking PyTorch with High API Surface Coverage

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Descend: A Safe GPU Systems Programming Language

Redwood heterogeneous framework

Redwood: Flexible and Portable Heterogeneous Tree Traversal Workloads

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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

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

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

AutoDock for GPUs and other accelerators

spotPython: Sequential Parameter Optimization in Python

Test benchmarks for CELERITY

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Redwood heterogeneous framework

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