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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

High performance cellular level agent-based simulation with FLAME for the GPU

Paul Richmond, Dawn Walker, Simon Coakley, Daniela Romano

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Tags: Bioinformatics, Cellular modelling, Computational modelling, Computer science, CUDA, nVidia, Package

October 28, 2010 by hgpu

Sequence alignment with GPU: Performance and design challenges

Gregory M. Striemer, Ali Akoglu

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Tags: Bioinformatics, Biology, Computational biology, Computer science, CUDA, nVidia, Package, Sequence alignment, Smith-Waterman algorithm, Tesla C870

October 27, 2010 by hgpu

GPU acceleration of a production molecular docking code

Bharat Sukhwani, Martin C. Herbordt

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Tags: Bioinformatics, Biophysics, Chemistry, FPGA, Molecular dynamics, nVidia, Tesla C1060

October 27, 2010 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

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

High performance cellular level agent-based simulation with FLAME for the GPU

Sequence alignment with GPU: Performance and design challenges

GPU acceleration of a production molecular docking code

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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