high performance computing on graphics processing units: hgpu.org

Tommaso Biagini, Giovanni Chillemi, Gianluigi Mazzoccoli, Alessandro Grottesi, Caterina Fusilli, Daniele Capocefalo, Stefano Castellana, Angelo Luigi Vescovi, Tommaso Mazza

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Tags: Benchmarking, Bioinformatics, Biology, Chemistry, CUDA, Genomics, Molecular dynamics, nVidia, OpenCL, Physics, Tesla C2070

December 19, 2017 by hgpu

HCudaBLAST: an implementation of BLAST on Hadoop and Cuda

Nilay Khare, Alind Khare and Farhan Khan

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Tags: Algorithms, Bioinformatics, Biology, BLAST, CUDA, Hadoop, nVidia, nVidia Quadro 2000, nVidia Quadro 600, Tesla C2075

December 3, 2017 by hgpu

A Hybrid-parallel Architecture for Applications in Bioinformatics

Jan Christian Kassens

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Tags: Algorithms, Bioinformatics, Biology, Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 780 Ti, Thesis

December 3, 2017 by hgpu

High Performance Streaming Smith-Waterman Implementation with Implicit Synchronization on Intel FPGA using OpenCL

Ernst Joachim Houtgast, Vlad-Mihai Sima, Zaid Al-Ars

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Tags: Algorithms, Bioinformatics, Biology, FPGA, OpenCL, Smith-Waterman algorithm

November 26, 2017 by hgpu

A Study of Time and Energy Efficient Algorithms for Parallel and Heterogeneous Computing

Jude-Thaddeus Ojiaku

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Tags: ATI, ATI Radeon HD 7750, ATI Radeon HD 7970, Bioinformatics, Computer science, Graph layout, Heterogeneous systems, nVidia, nVidia GefForce GTX 650, nVidia GefForce GTX 680, OpenCL, Thesis

October 29, 2017 by hgpu

Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis

Jesus Perez Serrano, Edans Flavius De Oliveira Sandes, Alba Cristina Magalhaes Alves de Melo, Manuel Ujaldon

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Tags: Bioinformatics, Biology, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 980, Performance, Smith-Waterman algorithm

June 10, 2017 by hgpu

Exploiting Multi-level Parallelism on a Many-core System for the Application of Hyperheuristics to a Molecular Docking Problem

Jose M. Cecilia, Jose-Matias, Cutillas-Lozano, Domingo Gimenez, Baldomero Imbernon

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Tags: Bioinformatics, Biology, Chemistry, Intel Xeon Phi, Metaheuristics, molecular docking

March 20, 2017 by hgpu

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high performance computing on graphics processing units: hgpu.org

WarpCore: A Library for fast Hash Tables on GPUs

LOGAN: High-Performance GPU-Based X-Drop Long-Read Alignment

ASW: Accelerating Smith-Waterman Algorithm on Coupled CPU-GPU Architecture

Fast and Flexible GPU Accelerated Binding Free Energy Calculations within the AMBER Molecular Dynamics Package

pyPaSWAS: Python-based multi-core CPU and GPU sequence alignment

Molecular dynamics recipes for genome research

HCudaBLAST: an implementation of BLAST on Hadoop and Cuda

A Hybrid-parallel Architecture for Applications in Bioinformatics

High Performance Streaming Smith-Waterman Implementation with Implicit Synchronization on Intel FPGA using OpenCL

A Study of Time and Energy Efficient Algorithms for Parallel and Heterogeneous Computing

Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis

Exploiting Multi-level Parallelism on a Many-core System for the Application of Hyperheuristics to a Molecular Docking Problem

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clRNG: an OpenCL based software library containing random number generation functions

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