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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

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

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

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

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high performance computing on graphics processing units: hgpu.org

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

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

Recent source codes

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

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

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