high performance computing on graphics processing units: hgpu.org

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Locality and parallelism optimization for dynamic programming algorithm in bioinformatics

Guangming Tan, Shengzhong Feng, Ninghui Sun

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Tags: Algorithms, Bioinformatics, Biology, Programming techniques

December 2, 2010 by hgpu

ClawHMMER: A Streaming HMMer-Search Implementation

Daniel R. Horn, Mike Houston, Pat Hanrahan

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Tags: ATI, ATI Radeon X800, Bioinformatics, Biology, Brook, nVidia, nVidia GeForce 6800 Ultra, nVidia GeForce 7800 GTX, Pattern Search

November 30, 2010 by hgpu

GPU-BLAST: Using graphics processors to accelerate protein sequence alignment

Panagiotis D. Vouzis, Nikolaos V. Sahinidis

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Tags: Bioinformatics, Biology, BLAST, CUDA, nVidia, Package, Sequence alignment, Tesla C2050

November 30, 2010 by hgpu

Highly accelerated feature detection in proteomics data sets using modern graphics processing units

Rene Hussong, Barbara Gregorius, Andreas Tholey, Andreas Hildebrandt

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Tags: Bioinformatics, Biology, CUDA, nVidia, Package, Tesla C870

November 28, 2010 by hgpu

A Massively Parallel Architecture for Bioinformatics

Gerd Pfeiffer, Stefan Baumgart, Jan Schroder, Manfred Schimmler

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Tags: Algorithms, Bioinformatics, Biology, FPGA, MapReduce

November 27, 2010 by hgpu

Parallel reconstruction of neighbor-joining trees for large multiple sequence alignments using CUDA

Yongchao Liu, Bertil Schmidt, Douglas L. Maskell

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 280, Sequence alignment, Smith-Waterman algorithm

November 27, 2010 by hgpu

Performance Analysis of IBM Cell Broadband Engine on Sequence Alignment

Yang Song, Gregory M. Striemer, Ali Akoglu

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Tags: Bioinformatics, Biology, Cell processor, CUDA, nVidia, Sequence alignment, Smith-Waterman algorithm, Tesla C870

November 27, 2010 by hgpu

Many-core algorithms for statistical phylogenetics

Marc A. Suchard, Andrew Rambaut

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Tags: Bioinformatics, Biology, CUDA, Genetics, nVidia, nVidia GeForce GTX 280

November 23, 2010 by hgpu

MSA-CUDA: Multiple Sequence Alignment on Graphics Processing Units with CUDA

Yongchao Liu, Bertil Schmidt, Douglas L. Maskell

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 280, Sequence alignment

November 21, 2010 by hgpu

Ultra-fast FFT protein docking on graphics processors

David W. Ritchie, Vishwesh Venkatraman

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Tags: Bioinformatics, Biology, CUDA, FFT, nVidia, nVidia GeForce GTX 285

November 19, 2010 by hgpu

Acceleration of the Smith-Waterman Algorithm using Single and Multiple Graphics Processors

Ali Khajeh-Saeed, Stephen Poole, J. Blair Perot

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce 9800 GT, nVidia GeForce 9800 GTX, nVidia GeForce GTX 295, Sequence matching, Smith-Waterman algorithm

November 18, 2010 by hgpu

permGPU: Using graphics processing units in RNA microarray association studies

Ivo Shterev, Sin H. Jung, Stephen George, Kouros Owzar

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Tags: Bioinformatics, Biology, CUDA, Genetics, nVidia, nVidia GeForce GTX 280, Package, RNA

November 17, 2010 by hgpu

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

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

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

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

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high performance computing on graphics processing units: hgpu.org

Locality and parallelism optimization for dynamic programming algorithm in bioinformatics

ClawHMMER: A Streaming HMMer-Search Implementation

GPU-BLAST: Using graphics processors to accelerate protein sequence alignment

Highly accelerated feature detection in proteomics data sets using modern graphics processing units

A Massively Parallel Architecture for Bioinformatics

Parallel reconstruction of neighbor-joining trees for large multiple sequence alignments using CUDA

Performance Analysis of IBM Cell Broadband Engine on Sequence Alignment

Many-core algorithms for statistical phylogenetics

MSA-CUDA: Multiple Sequence Alignment on Graphics Processing Units with CUDA

Ultra-fast FFT protein docking on graphics processors

Acceleration of the Smith-Waterman Algorithm using Single and Multiple Graphics Processors

permGPU: Using graphics processing units in RNA microarray association studies

Recent source codes

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

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

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