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A parameterisable and scalable Smith-Waterman algorithm implementation on CUDA-compatible GPUs

Cheng Ling, Khaled Benkrid, Tsuyoshi Hamada

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Tags: Bioinformatics, Biology, Computational biology, CUDA, nVidia, nVidia GeForce 8800 GTX, Sequence alignment, Smith-Waterman algorithm

January 4, 2011 by hgpu

Speeding up subset seed algorithm for intensive protein sequence comparison

Van Hoa Nguyen, Dominique Lavenier

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce 8800 GTX, Sequence alignment

January 4, 2011 by hgpu

Population Parallel GP on the G80 GPU

Denis Robilliard, Virginie Marion-Poty, Cyril Fonlupt

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Tags: Bioinformatics, Biology, CUDA, Genetics, nVidia, nVidia GeForce 8800 GTX, Package

December 29, 2010 by hgpu

A SIMD Interpreter for Genetic Programming on GPU Graphics Cards

William B. Langdon, Wolfgang Banzhaf

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Tags: Bioinformatics, Biology, DirectX, Genetics, nVidia, nVidia GeForce 8800 GTX, OpenGL

December 29, 2010 by hgpu

The GPU on biomedical image processing for color and phenotype analysis

Antonio Ruiz, Manuel Ujaldon, Jose Antonio Andrade

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Tags: Bioinformatics, Image processing, Medicine, nVidia, nVidia GeForce 6800 Ultra, nVidia GeForce 7800 GTX, OpenGL

December 24, 2010 by hgpu

Design and implementation of the Smith-Waterman algorithm on the CUDA-compatible GPU

Yuma Munekawa, Fumihiko Ino, Kenichi Hagihara

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce 8800 GTX, Smith-Waterman algorithm

December 22, 2010 by hgpu

Long time-scale simulations of in vivo diffusion using GPU hardware

Elijah Roberts, John E. Stone, Leonardo Sepulveda, Wen-Mei W. Hwu, Zaida Luthey-Schulten

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Tags: Bioinformatics, Biology, CUDA, Distributed computing, nVidia, nVidia GeForce GTX 280, nVidia Quadro FX 5600

December 18, 2010 by hgpu

GPU Accelerated RNA Folding Algorithm

Guillaume Rizk, Dominique Lavenier

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 280, Tesla C870

December 17, 2010 by hgpu

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

The impact of accelerator processors for high-throughput molecular modeling and simulation

G. Giupponi, M. Harvey, G. Defabritiis

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Tags: Bioinformatics, Cell processor, Chemistry, Medicine, Molecular modeling, nVidia, Review

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

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

A parameterisable and scalable Smith-Waterman algorithm implementation on CUDA-compatible GPUs

Speeding up subset seed algorithm for intensive protein sequence comparison

Population Parallel GP on the G80 GPU

A SIMD Interpreter for Genetic Programming on GPU Graphics Cards

The GPU on biomedical image processing for color and phenotype analysis

Design and implementation of the Smith-Waterman algorithm on the CUDA-compatible GPU

Long time-scale simulations of in vivo diffusion using GPU hardware

GPU Accelerated RNA Folding Algorithm

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

The impact of accelerator processors for high-throughput molecular modeling and simulation

Initial Experiences Porting a Bioinformatics Application to a Graphics Processor

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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