high performance computing on graphics processing units: hgpu.org

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Scalable and highly parallel implementation of Smith-Waterman on graphics processing unit using CUDA

Ali Akoglu, Gregory Striemer

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

December 15, 2010 by hgpu

Real-world comparison of CPU and GPU implementations of SNPrank: a network analysis tool for GWAS

Nicholas A. Davis, Ahwan Pandey, B. A. McKinney

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Tags: Bioinformatics, Biology, CUDA, Genetics, Medicine, nVidia, Package, Tesla M1060

December 13, 2010 by hgpu

SWPS3 – fast multi-threaded vectorized Smith-Waterman for IBM Cell/B.E. and x86/SSE2

Adam Szalkowski, Christian Ledergerber, Philipp Kraehenbuehl, Christophe Dessimoz

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Tags: Biology, Cell processor, CUDA, nVidia, nVidia GeForce 8800 GTX, Optimization, Package, Sequence alignment, Smith-Waterman algorithm

December 12, 2010 by hgpu

Exploring Parallel Algorithms for Volumetric Mass-Spring-Damper Models in CUDA

Allan Rasmusson, Jesper Mosegaard, Thomas S. Sorensen

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Tags: Biology, CUDA, Medicine, nVidia, nVidia GeForce 8800 GTX

December 12, 2010 by hgpu

High performance computing for deformable image registration: Towards a new paradigm in adaptive radiotherapy

Sanjiv S. Samant, Junyi Xia, P, John D. Owens

Tags: Biology, Computed tomography, CT, CUDA, Image reconstruction, Image registration, Medicine, nVidia, nVidia GeForce 8800 GTX, Radiotherapy

December 11, 2010 by hgpu

Initial Experiences Porting a Bioinformatics Application to a Graphics Processor

Maria Charalambous, Pedro Trancoso, Alexandros Stamatakis

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Tags: Bioinformatics, Biology, Brook, nVidia, nVidia GeForce FX 5700

December 10, 2010 by hgpu

Performance evaluation of image processing algorithms on the GPU

Daniel Castano-Diez, Dominik Moser, Andreas Schoenegger, Sabine Pruggnaller, Achilleas S S. Frangakis

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Tags: Biology, CUDA, Image processing, Medicine, nVidia, nVidia Quadro FX 5600, Pattern recognition, Tomography

December 7, 2010 by hgpu

GPU-MEME: Using Graphics Hardware to Accelerate Motif Finding in DNA Sequences

Chen Chen, Bertil Schmidt, Liu Weiguo, Wolfgang Muller-Wittig

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Tags: Bioinformatics, Biology, GLSL, nVidia, nVidia GeForce 8800 GTX, OpenGL, Package, Pattern recognition

December 6, 2010 by hgpu

Implementation and performance evaluation of reconstruction algorithms on graphics processors

D. Castanodiez, H. Mueller, A. Frangakis

Tags: Biology, Electron microscopy, Image processing

December 6, 2010 by hgpu

Hierarchical clustering of gene expression profiles with graphics hardware acceleration

Q. Zhang, Y. Zhang

Tags: Biology, Genetics, Pattern recognition

December 4, 2010 by hgpu

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

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

Scalable and highly parallel implementation of Smith-Waterman on graphics processing unit using CUDA

Real-world comparison of CPU and GPU implementations of SNPrank: a network analysis tool for GWAS

SWPS3 – fast multi-threaded vectorized Smith-Waterman for IBM Cell/B.E. and x86/SSE2

Exploring Parallel Algorithms for Volumetric Mass-Spring-Damper Models in CUDA

High performance computing for deformable image registration: Towards a new paradigm in adaptive radiotherapy

Initial Experiences Porting a Bioinformatics Application to a Graphics Processor

Performance evaluation of image processing algorithms on the GPU

GPU-MEME: Using Graphics Hardware to Accelerate Motif Finding in DNA Sequences

Implementation and performance evaluation of reconstruction algorithms on graphics processors

Hierarchical clustering of gene expression profiles with graphics hardware acceleration

Locality and parallelism optimization for dynamic programming algorithm in bioinformatics

ClawHMMER: A Streaming HMMer-Search Implementation

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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