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MaxSSmap: A GPU program for short read mapping with the maximum scoring subsequence

Usman Roshan

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Tags: Bioinformatics, Biology, CUDA, nVidia, Short read mapping, Smith-Waterman algorithm, Tesla M2050

March 12, 2014 by hgpu

Size Matters: Space/Time Tradeoffs to Improve GPGPU Applications Performance

Abdullah Gharaibeh, Matei Ripeanu

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

February 27, 2011 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

DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications

OpenAdSCL

Scalar collapse in AdS with an OpenCL open source code

Rgtsvm: Support Vector Machines on a GPU in R

Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

gloo: Collective communications library with various primitives for multi-machine training

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU

CELES: CUDA-accelerated electromagnetic scattering by large ensembles of spheres

CELES: CUDA-accelerated simulation of electromagnetic scattering by large ensembles of spheres

Boda: A C++ Framework for Efficient Experiments in Computer Vision [WIP]

Implementing Efficient, Portable Computations for Machine Learning

Parallel and in-process compilation of individuals for genetic programming on GPU

ESPRESO: Highly Parallel Solvers for Engineering Applications

Intel Xeon Phi acceleration of Hybrid Total FETI solver

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high performance computing on graphics processing units: hgpu.org

MaxSSmap: A GPU program for short read mapping with the maximum scoring subsequence

Size Matters: Space/Time Tradeoffs to Improve GPGPU Applications Performance

Optimizing data intensive GPGPU computations for DNA sequence alignment

Recent source codes

DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications

OpenAdSCL

Rgtsvm: Support Vector Machines on a GPU in R

Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

gloo: Collective communications library with various primitives for multi-machine training

MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU

CELES: CUDA-accelerated electromagnetic scattering by large ensembles of spheres

Boda: A C++ Framework for Efficient Experiments in Computer Vision [WIP]

Parallel and in-process compilation of individuals for genetic programming on GPU

ESPRESO: Highly Parallel Solvers for Engineering Applications

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4th International Conference on Biomedical and Bioinformatics Engineering (ICBBE), 2017

9th International Conference on Signal Processing Systems (ICSPS), 2017

The 5th International conference on Control, Mechatronics and Automation (ICCMA), 2017

8th International Conference on Biology, Environment and Chemistry (ICBEC), 2017

2nd International Conference on Communication and Information Systems (ICCIS), 2017