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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

First Experiences Optimizing Smith-Waterman on Intel’s Knights Landing Processor

Enzo Rucci, Carlos Garcia, Guillermo Botella, Armando De Giusti, Marcelo Naiouf, Manuel Prieto-Matias

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Tags: Bioinformatics, Biology, Computer science, Intel Xeon Phi, Sequence alignment, Smith-Waterman algorithm

February 26, 2017 by hgpu

Analysis and implementation of a BLAST-Like algorithm for MIC architectures

Felipe Sulser Larraz

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Tags: Benchmarking, Bioinformatics, Biology, Computer science, Databases, Filtering, Intel Xeon Phi, Sequence alignment, Smith-Waterman algorithm, Thesis

February 2, 2017 by hgpu

Molecular Activity Prediction using Deep Learning Software Library

Yoshiki Kato, Shinji Hamada, Hitoshi Goto

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Tags: Bioinformatics, Chemistry, Computer science, CUDA, Deep learning, nVidia, Package, Python, Tesla K20

November 5, 2016 by hgpu

Ultra-Fast Detection of Higher-Order Epistatic Interactions on GPUs

Daniel Junger, Christian Hundt, Jorge Gonzalez-Dominguez, Bertil Schmidt

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Tags: Algorithms, Bioinformatics, Clustering, Computer science, CUDA, Filtering, nVidia, nVidia GeForce GTX Titan X

September 3, 2016 by hgpu

Iterative Hard Thresholding for Model Selection in Genome-Wide Association Studies

Kevin L. Keys, Gary K. Chen, Kenneth Lange

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Tags: Bioinformatics, Genetics, Machine learning, nVidia, OpenCL, Package, Statistics, Tesla C2050

August 8, 2016 by hgpu

Drug Drug Interaction Extraction from Biomedical Literature Using Syntax Convolutional Neural Network

Zhehuan Zhao, Zhihao Yang, Ling Luo, Hongfei Lin, Jian Wang

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Tags: Bioinformatics, Biology, CUDA, Deep learning, Neural networks, NLP, nVidia, Package, Python, Tesla K20, Text mining

August 1, 2016 by hgpu

Gerbil: A Fast and Memory-Efficient k-mer Counter with GPU-Support

Marius Erbert, Steffen Rechner, Matthias Muller-Hannemann

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Tags: Bioinformatics, Biology, Computer science, CUDA, nVidia, nVidia GeForce GTX 970, nVidia GeForce GTX Titan, Package

July 26, 2016 by hgpu

Parallel Pairwise Correlation Computation On Intel Xeon Phi Clusters

Yongchao Liu, Tony Pan, Srinivas Aluru

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Tags: Algorithms, Bioinformatics, Computer science, Intel Xeon Phi, OpenMP

May 7, 2016 by hgpu

A Massively Parallel Algorithm for Cell Classification Using CUDA

Samuel Schmidt

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Tags: Bioinformatics, Biology, CUDA, Machine learning, nVidia, nVidia GeForce GTX 770, RNA, Thesis

March 20, 2016 by hgpu

Pairwise Sequence Alignment with Gaps with GPU

Thomas C. Carroll, Jude-Thaddeus Ojiaku, Prudence W.H. Wong

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 650, OpenCL, Sequence alignment

October 27, 2015 by hgpu

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high performance computing on graphics processing units: hgpu.org

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

First Experiences Optimizing Smith-Waterman on Intel’s Knights Landing Processor

Analysis and implementation of a BLAST-Like algorithm for MIC architectures

Molecular Activity Prediction using Deep Learning Software Library

Ultra-Fast Detection of Higher-Order Epistatic Interactions on GPUs

Iterative Hard Thresholding for Model Selection in Genome-Wide Association Studies

Drug Drug Interaction Extraction from Biomedical Literature Using Syntax Convolutional Neural Network

Gerbil: A Fast and Memory-Efficient k-mer Counter with GPU-Support

Parallel Pairwise Correlation Computation On Intel Xeon Phi Clusters

A Massively Parallel Algorithm for Cell Classification Using CUDA

Pairwise Sequence Alignment with Gaps with GPU

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