high performance computing on graphics processing units: hgpu.org

Jairo Panetta, Thiago Teixeira, Paulo R. P. de Souza Filho, Carlos A. da Cunha Finho, David Sotelo, Fernando M. da Motta, Silvio S. Pinheiro, Ivan P. Junior, Andre L. Rosa, Luiz R. Monnerat, Leandro T. Carneiro, Carlos H. B. de Albrecht

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Tags: CUDA, Geoscience, GPU cluster, nVidia, Physics, Tesla C1060

October 28, 2010 by hgpu

The Heisenberg spin glass model on GPU: myths and actual facts

M. Bernaschi, G. Parisi, L. Parisi

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Tags: Condensed matter, CUDA, Disordered Systems and Neural Networks, nVidia, nVidia GeForce GTX 480, Package, Physics, Spin Systems, Tesla C1060, Tesla C2050

October 28, 2010 by hgpu

Accelerating astrophysical particle simulations with programmable hardware (FPGA and GPU)

R. Spurzem, P. Berczik, G. Marcus, A. Kugel, G. Lienhart, I. Berentzen, R. Manner, R. Klessen, R. Banerjee

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Tags: Astrophysics, FPGA, N-body simulation, nVidia, nVidia GeForce 8800 GTX, Particle simulation, Special purpose accelerators

October 28, 2010 by hgpu

Analyzing CUDA workloads using a detailed GPU simulator

Ali Bakhoda, George L. Yuan, Wilson W. L. Fung, Henry Wong, Tor M. Aamodt

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8600 GTS, PTX

October 28, 2010 by hgpu

CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment

Svetlin Manavski, Giorgio Valle

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Tags: Bioinformatics, Biology, Computational biology, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Package, Sequence alignment, Smith-Waterman algorithm

October 28, 2010 by hgpu

cuInspiral: prototype gravitational waves detection pipeline fully coded on GPU using CUDA

Leone B. Bosi

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Tags: Computational Physics, CUDA, FFT, General Relativity and Quantum Cosmology, nVidia, nVidia GeForce GTX 275, Physics

October 28, 2010 by hgpu

Matched Filter Computation on FPGA, Cell and GPU

Z. K. Baker, M. B. Gokhale, J. L. Tripp

Tags: Computer science, FPGA, nVidia, nVidia GeForce 7900 GTX

October 28, 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

Visualization and GPU-accelerated simulation of medical ultrasound from CT images

Jump flooding in GPU with applications to Voronoi diagram and distance transform

String Matching on a Multicore GPU Using CUDA

Compute Pairwise Manhattan Distance and Pearson Correlation Coefficient of Data Points with GPU

Motion Compensation and Reconstruction of H.264/AVC Video Bitstreams using the GPU

Accelerating Kirchhoff Migration by CPU and GPU Cooperation

The Heisenberg spin glass model on GPU: myths and actual facts

Accelerating astrophysical particle simulations with programmable hardware (FPGA and GPU)

Analyzing CUDA workloads using a detailed GPU simulator

CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment

cuInspiral: prototype gravitational waves detection pipeline fully coded on GPU using CUDA

Matched Filter Computation on FPGA, Cell and GPU

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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