high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Biology

Efficient bayesian multi-view deconvolution

Stephan Preibisch, Fernando Amat, Evangelia Stamataki, Mihail Sarov, Eugene Myers, Pavel Tomancak

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Tags: Bayesian, Biology, CUDA, Microscopy, nVidia, nVidia Quadro 4000, Package

August 6, 2013 by hgpu

Parallel birth and death process for cell nuclei extraction in histopathology images

Christophe Avenel, Pierre Fortin, Dominique Bereziat

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Tags: Biology, CUDA, Medicine, nVidia, Tesla C2070

July 25, 2013 by hgpu

CFMDS: CUDA-based fast multidimensional scaling for genome-scale data

Sungin Park, Soo-Yong Shin, Kyu-Baek Hwang

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Tags: Bioinformatics, Biology, CUDA, Genetics, Multidimensional scaling, nVidia, Package

June 21, 2013 by hgpu

Performance of a GPU-based Direct Summation Algorithm for Computation of Small Angle Scattering Profile

Konstantin Berlin, Nail A. Gumerov, Ramani Duraiswami, David Fushman

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Tags: Algorithms, Biology, Biomolecules, Fast multipole method, Macromolecule, nVidia, nVidia GeForce GT 330 M, nVidia GeForce GTX 295, OpenCL, RNA

June 12, 2013 by hgpu

GPU Multiple Sequence Alignment Fourier-Space Cross-Correlation Alignment

Matthias Alexander Lee

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Tags: Bioinformatics, Biology, Computational Complexity, CUDA, nVidia, Package, Python, RNA, Sequence alignment, Signal processing

May 31, 2013 by hgpu

Improved Performance of CaFE and IRIS Model Fitting Using CUDA

Dylan Jackson

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Tags: Biology, Biomolecules, CUDA, nVidia, nVidia GeForce GTX 680, Sensing, Thesis

May 31, 2013 by hgpu

Rapid Computation of Sodium Bioscales Using GPU-Accelerated Image Reconstruction

Ian C. Atkinson, Geng Liu, Nady Obeid, Keith R. Thulborn, Wen-mei Hwu

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Tags: Algorithms, Biology, CUDA, FFT, Image reconstruction, Magnetic resonance imaging, MRI, nVidia, Tesla C2050

May 27, 2013 by hgpu

GPU-accelerated protein family identification for metagenomics

Changjun Wu, Ananth Kalyanaraman

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Tags: Biology, Clustering, CUDA, Graph theory, nVidia, Tesla K20

May 25, 2013 by hgpu

A Distributed CPU-GPU Framework for Pairwise Alignments on Large-Scale Sequence Datasets

Da Li, Kittisak Sajjapongse, Huan Truong, Gavin Conant, Michela Becchi

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Tags: Bioinformatics, Biology, Computational biology, CUDA, GPU cluster, Heterogeneous systems, MPI, nVidia, nVidia Quadro FX 2000, Sequence alignment, Tesla K20

May 11, 2013 by hgpu

The GPU-based High-performance Pattern-matching Algorithm for Intrusion Detection

Dongliang Xu, Hongli Zhang, Yujian Fan

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Tags: Biology, Computer science, CUDA, Information Retrieval, nVidia, nVidia GeForce 9800 GTS, nVidia GeForce GTX 570, Security, String matching

May 11, 2013 by hgpu

Simulation of Biological Tissue using Mass-Spring-Damper Models

Emil Eriksson

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Tags: ATI, ATI Radeon HD 7970, Biology, OpenCL, Surgical simulation, Thesis, Visualization

May 6, 2013 by hgpu

GPU-based Steady-State Solution of the Chemical Master Equation

Marco Maggioni, Tanya Berger-Wolf, Jie Liang

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Tags: Biology, CUDA, Macromolecule, nVidia, nVidia GeForce GTX 580, Probability, Sparse matrix

May 1, 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

Efficient bayesian multi-view deconvolution

Parallel birth and death process for cell nuclei extraction in histopathology images

CFMDS: CUDA-based fast multidimensional scaling for genome-scale data

Performance of a GPU-based Direct Summation Algorithm for Computation of Small Angle Scattering Profile

GPU Multiple Sequence Alignment Fourier-Space Cross-Correlation Alignment

Improved Performance of CaFE and IRIS Model Fitting Using CUDA

Rapid Computation of Sodium Bioscales Using GPU-Accelerated Image Reconstruction

GPU-accelerated protein family identification for metagenomics

A Distributed CPU-GPU Framework for Pairwise Alignments on Large-Scale Sequence Datasets

The GPU-based High-performance Pattern-matching Algorithm for Intrusion Detection

Simulation of Biological Tissue using Mass-Spring-Damper Models

GPU-based Steady-State Solution of the Chemical Master Equation

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

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