high performance computing on graphics processing units: hgpu.org

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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

SW# – GPU enabled exact alignments on genome scale

Matija Korpar, Mile Sikic

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Tags: Bioinformatics, Biology, Computer science, CUDA, nVidia, nVidia GeForce GTX 560, nVidia GeForce GTX 570, nVidia GeForce GTX 690, Package, Sequence alignment, Smith-Waterman algorithm

April 23, 2013 by hgpu

CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions

Yongchao Liu, Adrianto Wirawan, Bertil Schmidt

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 690, Package, PTX, Sequence alignment, Smith-Waterman algorithm

April 10, 2013 by hgpu

CMCpy: Genetic Code-Message Coevolution Models in Python

Peter J. Becich, Brian P. Stark, Harish S. Bhat, David H. Ardell

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 460, Package, Python

March 31, 2013 by hgpu

SOAP3-dp: Fast, Accurate and Sensitive GPU-based Short Read Aligner

Ruibang Luo, Thomas Wong, Jianqiao Zhu, Chi-Man Liu, Edward Wu, Lap-Kei Lee, Haoxiang Lin, Wenjuan Zhu, David W. Cheung, Hing-Fung Ting, Siu-Ming Yiu, Chang Yu, Yingrui Li, Ruiqiang Li, Tak-Wah Lam

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Tags: Algorithms, Bioinformatics, Biology, CUDA, Next-Generation sequencing, nVidia, Package, Tesla C2070, Tesla M2050

February 25, 2013 by hgpu

ClusCo: clustering and comparison of protein models

Jamroz Michal, Kolinski Andrzej

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Tags: Biology, Biomolecules, Clustering, Computational Engineering, CUDA, Finance, nVidia, nVidia GeForce GTX 470, Package

February 20, 2013 by hgpu

Binary Interval Search: a scalable algorithm for counting interval intersections

Ryan M. Layer, Kevin Skadron, Gabriel Robins, Ira M. Hall, Aaron R. Quinlan

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Tags: Algorithms, Bioinformatics, Biology, Computer science, CUDA, Monte Carlo simulation, nVidia, Package, Sequence alignment

February 6, 2013 by hgpu

A Comparison of CPU and OpenCL Parallelization Methods for Correlation and Graph Layout Algorithms used in the Network Analysis of High Dimensional Data

Athanasios Theocharidis, Gibran Hemani, Michael Kargas, Tom C. Freeman

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Tags: ATI, ATI Radeon HD 5870, ATI Radeon HD 6970, Biology, Graph layout, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 580, OpenCL, OpenGL, Package, Visualization

February 5, 2013 by hgpu

Parallel GPGPU Evaluation of Small Angle X-ray Scattering Profiles in a Markov Chain Monte Carlo Framework

Lubomir D. Antonov, Christian Andreetta, Thomas Hamelryck

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Tags: Algorithms, Biology, Medicine, nVidia, nVidia GeForce GTX 560 Ti, OpenCL, Package

January 30, 2013 by hgpu

Efficient Implementation of MrBayes on multi-GPU

Jie Bao, Hongju Xia, Jianfu Zhou, Xiaoguang Liu, Gang Wang

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Tags: Bayesian, Biology, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 480, Package, Task scheduling

January 28, 2013 by hgpu

Long Timestep Molecular Dynamics on the Graphical Processing Unit

James C. Sweet, Ronald J. Nowling, Trevor Cickovski, Christopher R. Sweet, Vijay S. Pande, Jesus A. Izaguirre

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Tags: Biology, Chemistry, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 580, OpenCL, Package

January 4, 2013 by hgpu

Efficient reconstruction of biological networks via transitive reduction on general purpose graphics processors

Dragan Bosnacki, Maximilian R Odenbrett, Anton Wijs, Willem Ligtenberg, Peter Hilbers

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 480, Package

November 23, 2012 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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high performance computing on graphics processing units: hgpu.org

Packages

GPU Multiple Sequence Alignment Fourier-Space Cross-Correlation Alignment

SW# – GPU enabled exact alignments on genome scale

CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions

CMCpy: Genetic Code-Message Coevolution Models in Python

SOAP3-dp: Fast, Accurate and Sensitive GPU-based Short Read Aligner

ClusCo: clustering and comparison of protein models

Binary Interval Search: a scalable algorithm for counting interval intersections

A Comparison of CPU and OpenCL Parallelization Methods for Correlation and Graph Layout Algorithms used in the Network Analysis of High Dimensional Data

Parallel GPGPU Evaluation of Small Angle X-ray Scattering Profiles in a Markov Chain Monte Carlo Framework

Efficient Implementation of MrBayes on multi-GPU

Long Timestep Molecular Dynamics on the Graphical Processing Unit

Efficient reconstruction of biological networks via transitive reduction on general purpose graphics processors

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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