high performance computing on graphics processing units: hgpu.org

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Source-to-Source Optimization of CUDA C for GPU Accelerated Cardiac Cell Modeling

Fred V. Lionetti, Andrew D. McCulloch, Scott B. Baden

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Tags: Computational biology, Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 295, Optimization

December 29, 2010 by hgpu

CUDASW++ 2.0: enhanced Smith-Waterman protein database search on CUDA-enabled GPUs based on SIMT and virtualized SIMD abstractions

Yongchao Liu, Bertil Schmidt, Douglas Maskell

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Tags: Bioinformatics, Biology, Computational biology, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 295, Package, Smith-Waterman algorithm

November 6, 2010 by hgpu

CUDASW++: optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units

Yongchao Liu, Douglas Maskell, Bertil Schmidt

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Tags: Bioinformatics, Biology, Computational biology, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 295, Package, Smith-Waterman algorithm

November 4, 2010 by hgpu

GPU-ClustalW: Using Graphics Hardware to Accelerate Multiple Sequence Alignment

Weiguo Liu, Bertil Schmidt, Gerrit Voss, Wolfgang Müller-Wittig

Tags: Bioinformatics, Biology, Computational biology, Computer science, Sequence alignment

November 1, 2010 by hgpu

GPU Accelerated Smith-Waterman

Yang Liu, Wayne Huang, John Johnson, Sheila Vaidya

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Tags: Bioinformatics, Biology, Computational biology, Computer science, GLSL, nVidia, nVidia GeForce 7800 GTX, OpenGL, Smith-Waterman algorithm

November 1, 2010 by hgpu

High-throughput sequence alignment using Graphics Processing Units

Michael Schatz, Cole Trapnell, Arthur Delcher, Amitabh Varshney

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

November 1, 2010 by hgpu

Bio-sequence database scanning on a GPU

Weiguo Liu, B. Schmidt, G. Voss, A. Schroder, W. Muller-Wittig

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Tags: Biology, Computational biology, GLSL, nVidia, nVidia GeForce 6800 GTO, nVidia GeForce 7800 GTX, OpenGL, Smith-Waterman algorithm

October 30, 2010 by hgpu

Optimizing data intensive GPGPU computations for DNA sequence alignment

Cole Trapnell, Michael C. Schatz

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Tags: Bioinformatics, Biology, Computational biology, CUDA, nVidia, nVidia GeForce 8800 GTX, Package, Sequence alignment, Short read mapping, Suffix trees

October 30, 2010 by hgpu

Exploiting graphics processing units for computational biology and bioinformatics

Joshua L. Payne, Nicholas A. Sinnott-Armstrong, Jason H. Moore

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Tags: All-Pairs Distance, Bioinformatics, Biology, Computational biology, CUDA, nVidia, nVidia GeForce 9600 M, Package

October 30, 2010 by hgpu

CUSA and CUDE: GPU-accelerated methods for estimating solvent accessible surface area and desolvation

David Dynerman, Erick Butzlaff, Julie C. Mitchell

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Tags: Bioinformatics, Biology, Biophysics, Chemistry, Computational biology, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 280, Solvent effects

October 29, 2010 by hgpu

An efficient implementation of Smith Waterman algorithm on GPU using CUDA, for massively parallel scanning of sequence databases

Lukasz Ligowski, Witold Rudnicki

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Tags: Bioinformatics, Biology, Computational biology, Computer science, CUDA, nVidia, nVidia GeForce 9800 GX2, Sequence alignment, Smith-Waterman algorithm

October 28, 2010 by hgpu

CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment

Svetlin Manavski, Giorgio Valle

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

October 28, 2010 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

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

BoltzGen:Toward Universal Binder Design

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

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

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high performance computing on graphics processing units: hgpu.org

Source-to-Source Optimization of CUDA C for GPU Accelerated Cardiac Cell Modeling

CUDASW++ 2.0: enhanced Smith-Waterman protein database search on CUDA-enabled GPUs based on SIMT and virtualized SIMD abstractions

CUDASW++: optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units

GPU-ClustalW: Using Graphics Hardware to Accelerate Multiple Sequence Alignment

GPU Accelerated Smith-Waterman

High-throughput sequence alignment using Graphics Processing Units

Bio-sequence database scanning on a GPU

Optimizing data intensive GPGPU computations for DNA sequence alignment

Exploiting graphics processing units for computational biology and bioinformatics

CUSA and CUDE: GPU-accelerated methods for estimating solvent accessible surface area and desolvation

An efficient implementation of Smith Waterman algorithm on GPU using CUDA, for massively parallel scanning of sequence databases

CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

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

BoltzGen:Toward Universal Binder Design

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

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

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