high performance computing on graphics processing units: hgpu.org

hgpu.org » Smith-Waterman algorithm

Sequence Homology Search using Fine-Grained Cycle Sharing of Idle GPUs

Fumihiko Ino, Yuma Munekawa, Kenichi Hagihara

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Tags: Algorithms, Computer science, CUDA, Distributed computing, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 285, Search, Smith-Waterman algorithm

December 4, 2011 by hgpu

Improving CUDASW++, a Parallelization of Smith-Waterman for CUDA Enabled Devices

Doug Hains, Zach Cashero, Mark Ottenberg, Wim Bohm, Sanjay Rajopadhye

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, Sequence alignment, Smith-Waterman algorithm, Tesla C1060, Tesla C2050

November 5, 2011 by hgpu

DOPA: GPU-based protein alignment using database and memory access optimizations

Laiq Hasan, Marijn Kentie, Zaid Al-Ars

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Source codes

Tags: Algorithms, Bioinformatics, Biology, Computational Complexity, CUDA, Databases, nVidia, nVidia GeForce GTX 275, Optimization, Package, Sequence alignment, Smith-Waterman algorithm

October 21, 2011 by hgpu

High performance technique for database applications using a hybrid GPU/CPU platform

M. Affan Zidan, Talal Bonny, Khaled N. Salama

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Tags: Algorithms, Computer science, Databases, Presentation, Programming techniques, Sequence matching, Smith-Waterman algorithm

September 11, 2011 by hgpu

A Coarse Grain Reconfigurable Architecture for sequence alignment problems in bio-informatics

Pei Liu, A. Hemani

Tags: Algorithms, Bioinformatics, Biology, FPGA, Sequence alignment, Smith-Waterman algorithm

July 25, 2011 by hgpu

On the Robust Mapping of Dynamic Programming onto a Graphics Processing Unit

Shucai Xiao, A.M. Aji, Wu-chun Feng

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Tags: Algorithms, Bioinformatics, Biology, Cell processor, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 280, Sequence alignment, Smith-Waterman algorithm

July 23, 2011 by hgpu

An Adaptive Hybrid Multiprocessor technique for bioinformatics sequence alignment

T. Bonny, M.A. Zidan, K.N. Salama

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia Quadro FX 4800, Sequence alignment, Smith-Waterman algorithm

July 7, 2011 by hgpu

Striped Smith-Waterman speeds database searches six times over other SIMD implementations

M. Farrar

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Tags: Algorithms, Bioinformatics, Biology, Sequence alignment, Smith-Waterman algorithm

July 6, 2011 by hgpu

Six-fold speed-up of Smith-Waterman sequence database searches using parallel processing on common microprocessors

Torbjorn Rognes, Erling Seeberg

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Tags: Algorithms, Bioinformatics, Biology, Sequence alignment, Smith-Waterman algorithm

July 6, 2011 by hgpu

Accelerating Smith-Waterman Local Sequence Alignment on GPU Cluster

Thuy T. Nguyen, Duc H. Nguyen, Phong H. Pham, Ngoc M. Ta, Tan N. Duong, Hung D. Le

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Tags: Bioinformatics, Biology, CUDA, GPU cluster, MPI, nVidia, nVidia GeForce GTX 295, Sequence alignment, Smith-Waterman algorithm, Tesla C1060

June 27, 2011 by hgpu

Accelerating Smith-Waterman on Heterogeneous CPU-GPU Systems

Jaideep Singh, Ipseeta Aruni

Tags: Bioinformatics, Biology, CUDA, Heterogeneous systems, nVidia, Smith-Waterman algorithm

June 24, 2011 by hgpu

Highly efficient mapping of the Smith-Waterman algorithm on CUDA-compatible GPUs

K. Dohi, K. Benkridt, C. Ling, T. Hamada, Y. Shibata

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

May 31, 2011 by hgpu

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

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

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high performance computing on graphics processing units: hgpu.org

Sequence Homology Search using Fine-Grained Cycle Sharing of Idle GPUs

Improving CUDASW++, a Parallelization of Smith-Waterman for CUDA Enabled Devices

DOPA: GPU-based protein alignment using database and memory access optimizations

High performance technique for database applications using a hybrid GPU/CPU platform

A Coarse Grain Reconfigurable Architecture for sequence alignment problems in bio-informatics

On the Robust Mapping of Dynamic Programming onto a Graphics Processing Unit

An Adaptive Hybrid Multiprocessor technique for bioinformatics sequence alignment

Striped Smith-Waterman speeds database searches six times over other SIMD implementations

Six-fold speed-up of Smith-Waterman sequence database searches using parallel processing on common microprocessors

Accelerating Smith-Waterman Local Sequence Alignment on GPU Cluster

Accelerating Smith-Waterman on Heterogeneous CPU-GPU Systems

Highly efficient mapping of the Smith-Waterman algorithm on CUDA-compatible GPUs

Recent source codes

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

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

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