high performance computing on graphics processing units: hgpu.org

Applications

Nengo: a Python tool for building large-scale functional brain models

Trevor Bekolay, James Bergstra, Eric Hunsberger, Travis DeWolf, Terrence C. Stewart, Daniel Rasmussen, Xuan Choo, Aaron Russell Voelker, Chris Eliasmith

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Tags: ATI, ATI Radeon HD 7970, Biology, Neuroscience, nVidia, nVidia GeForce GTX 280, OpenCL, Package, PyOpenCL, Python

January 9, 2014 by hgpu

Multicore Computing: Algorithms, Architectures, and Applications

Sanguthevar Rajasekaran, Lance Fiondella, Mohamed Ahmed, Reda A. Ammar

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Tags: Algorithms, Biology, Book, Computer science, CUDA, Linear Algebra, nVidia, String matching, Tesla

January 6, 2014 by hgpu

HUGO: Hierarchical mUlti-reference Genome cOmpression for aligned reads

Pinghao Li, Xiaoqian Jiang, Shuang Wang, Jihoon Kim, Hongkai Xiong, Lucila Ohno-Machado

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Tags: Algorithms, Bioinformatics, Biology, Clustering, Compression, CUDA, nVidia, Package, Sequence alignment, Tesla M2090

December 27, 2013 by hgpu

Accelerating Pairwise DNA Sequence Alignment using the CUDA Compatible GPU

H. Khaled, R. El Gohary, N. L. Badr, H. M. Faheem

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GT 540 M, Sequence alignment, Smith-Waterman algorithm

December 22, 2013 by hgpu

Efficient Multi-GPU Computation of All-Pairs Shortest Paths

Hristo Djidjev, Sunil Thulasidasan, Guillaume Chapuis, Rumen Andonov, Dominique Lavenier

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Tags: Algorithms, Bioinformatics, Biology, CUDA, GPU cluster, nVidia, Tesla C2090

December 18, 2013 by hgpu

Exploiting parallel features of modern computer architectures in bioinformatics

Guillaume Chapuis

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Tags: Algorithms, Bioinformatics, Biology, CUDA, Genetics, nVidia, Path problems, Tesla C2050, Tesla C2090, Thesis

December 16, 2013 by hgpu

Neurokernel: An Open Scalable Software Framework for Emulation and Validation of Drosophila Brain Models on Multiple GPUs

Lev E. Givon, Aurel A. Lazar

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Tags: Biology, CUDA, Neuroscience, nVidia, Package, Python

November 18, 2013 by hgpu

Graphics Processing Unit-Accelerated Quantitative Trait Loci Detection

Guillaume Chapuis, Olivier Filangi, Dominique Lavenier, Jean Michel J. M. Elsen, Pascale Leroy

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Tags: Bioinformatics, Biology, CUDA, nVidia, Tesla C2050

November 16, 2013 by hgpu

Accelerating calculations of RNA secondary structure partition functions using GPUs

Harry A Stern, David H Mathews

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Tags: Bioinformatics, Biology, CUDA, nVidia, Package, RNA, Tesla M2050

November 11, 2013 by hgpu

Computational kinetics of a large scale biological process on GPU workstations: DNA bending

Arnold Peter Ruymgaart

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Tags: Biology, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 480, Thesis

November 8, 2013 by hgpu

Communication Optimization for Multi GPU Implementation of Smith-Waterman Algorithm

Sampath Kumar, P. K. Baruah

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Tags: Algorithms, Biology, Computational biology, CUDA, Databases, nVidia, Sequence matching, Smith-Waterman algorithm, Tesla M2050

November 2, 2013 by hgpu

gEMpicker: A Highly Parallel GPU-Accelerated Particle Picking Tool for Cryo-Electron Microscopy

Thai V. Hoang, Xavier Cavin, Patrick Schultz, David W. Ritchie

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Tags: Algorithms, Biology, Cryo-EM, CUDA, Electron microscopy, FFT, Macromolecule, Microscopy, nVidia, Tesla C1060, Tesla C2075

October 24, 2013 by hgpu

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Nengo: a Python tool for building large-scale functional brain models

Multicore Computing: Algorithms, Architectures, and Applications

HUGO: Hierarchical mUlti-reference Genome cOmpression for aligned reads

Accelerating Pairwise DNA Sequence Alignment using the CUDA Compatible GPU

Efficient Multi-GPU Computation of All-Pairs Shortest Paths

Exploiting parallel features of modern computer architectures in bioinformatics

Neurokernel: An Open Scalable Software Framework for Emulation and Validation of Drosophila Brain Models on Multiple GPUs

Graphics Processing Unit-Accelerated Quantitative Trait Loci Detection

Accelerating calculations of RNA secondary structure partition functions using GPUs

Computational kinetics of a large scale biological process on GPU workstations: DNA bending

Communication Optimization for Multi GPU Implementation of Smith-Waterman Algorithm

gEMpicker: A Highly Parallel GPU-Accelerated Particle Picking Tool for Cryo-Electron Microscopy

Recent source codes

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

Most viewed papers (last 30 days)