high performance computing on graphics processing units: hgpu.org

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Sequence alignment with GPU: Performance and design challenges

Gregory M. Striemer, Ali Akoglu

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Tags: Bioinformatics, Biology, Computational biology, Computer science, CUDA, nVidia, Package, Sequence alignment, Smith-Waterman algorithm, Tesla C870

October 27, 2010 by hgpu

GPU-Quicksort: A practical Quicksort algorithm for graphics processors

Daniel Cederman, Philippas Tsigas

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce 8600 GTS, nVidia GeForce 8800 GTX, Package, Sorting

October 27, 2010 by hgpu

ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale

M. J. Harvey, G. Giupponi, G. De Fabritiis

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Tags: Biological Physics, Chemistry, Computational Physics, CUDA, GPU cluster, Molecular dynamics, nVidia, nVidia GeForce GTX 280, Package, Physics, Tesla C1060

October 27, 2010 by hgpu

Fast fluid dynamics simulation on the GPU

Mark Harris

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Tags: Cg, Computer science, Fluid dynamics, Navier-Stokes equations, NSEs, nVidia, Package, Programming techniques

October 27, 2010 by hgpu

Simulating spin models on GPU

Martin Weigel

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Tags: CUDA, Ising model, Monte Carlo simulation, nVidia, Package, Physics, Statistical Mechanics, Tesla C1060

October 27, 2010 by hgpu

Monte Carlo integration on GPU

J. Kanzaki

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Tags: Computational Physics, CUDA, High Energy Physics - Experiment, High Energy Physics - Phenomenology, Monte Carlo integration, nVidia, nVidia GeForce GTX 285, Package, Physics

October 27, 2010 by hgpu

Multi-GPU accelerated multi-spin Monte Carlo simulations of the 2D Ising model

Benjamin Block,Peter Virnau,Tobias Preis

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Tags: CUDA, GPU cluster, Ising model, Monte Carlo simulation, nVidia, Package, Physics, Tesla C1060

October 27, 2010 by hgpu

PyCUDA: GPU Run-Time Code Generation for High-Performance Computing

Andreas Klockner,Nicolas Pinto,Yunsup Lee,Bryan Catanzaro,Paul Ivanov,Ahmed Fasih

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Tags: Computer science, CUDA, High-level Languages, nVidia, Package

October 27, 2010 by hgpu

Efficient GPU-Based Texture Interpolation using Uniform B-Splines

Daniel Ruijters,Bart ter Haar Romeny,Paul Suetens

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Tags: Computer science, CUDA, Image processing, nVidia, nVidia GeForce 9800 GTX, Package

October 27, 2010 by hgpu

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A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusters

ARMCL-PipeALL: an integrated high-throughput CPU-GPU CNN inference pipeline design for ARM-based Heterogeneous MPSoCs

CPU-GPU Layer-Switched Low Latency CNN Inference

EngineCL for Stratix10 MX

FPGA Accelerators on Heterogeneous Systems: An Approach Using High Level Synthesis

Theseus: A Library for Differentiable Nonlinear Optimization

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high performance computing on graphics processing units: hgpu.org

Packages

Sequence alignment with GPU: Performance and design challenges

GPU-Quicksort: A practical Quicksort algorithm for graphics processors

ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale

Fast fluid dynamics simulation on the GPU

Simulating spin models on GPU

Monte Carlo integration on GPU

Multi-GPU accelerated multi-spin Monte Carlo simulations of the 2D Ising model

PyCUDA: GPU Run-Time Code Generation for High-Performance Computing

Efficient GPU-Based Texture Interpolation using Uniform B-Splines

Recent source codes

A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusters

ARMCL-PipeALL: an integrated high-throughput CPU-GPU CNN inference pipeline design for ARM-based Heterogeneous MPSoCs

EngineCL for Stratix10 MX

Theseus: A Library for Differentiable Nonlinear Optimization

OmpCluster

PipeCNN: An OpenCL-based FPGA Accelerator for Convolutional Neural Networks

hls4ml: Machine learning on FPGAs using HLS

MocCUDA: Prototype to run Pytorch/CUDA on Fugaku

Rodinia Benchmarks for DPC++

DCA++: a state of the art implementation of the dynamical cluster approximation

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