high performance computing on graphics processing units: hgpu.org

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

Sven Warris, N. Roshan N. Timal, Marcel Kempenaar, Arne M. Poortinga, Henri van de Geest, Ana L. Varbanescu, Jan-Peter Nap

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 1070, OpenCL, Package, Python, Sequence alignment, Smith-Waterman algorithm

January 13, 2018 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

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

Accelerating Protein Structure Prediction using Particle Swarm Optimization on GPU

Hamed Khakzad, Yasaman Karami, Seyed Shahriar Arab

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, Particle swarm optimization, Sequence alignment, Tesla C1060

July 24, 2015 by hgpu

Improving the Mapping of Smith-Waterman Sequence Database Searches onto CUDA-Enabled GPUs

Liang-Tsung Huang, Chao-Chin Wu, Lien-Fu Lai, Yun-Ju Li

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Tags: Bioinformatics, Biology, CUDA, Databases, nVidia, Sequence alignment, Smith-Waterman algorithm, Tesla C1060, Tesla K20

June 30, 2015 by hgpu

Concurrent Task Execution on the Intel Xeon Phi

Yucheng Zhu

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Tags: Bioinformatics, Biology, Computer science, CUDA, Intel Xeon Phi, nVidia, OpenMP, Sequence alignment, Smith-Waterman algorithm, Tesla K20, Thesis

June 22, 2015 by hgpu

Comparative Study of the Parallelization of the Smith-Waterman Algorithm on OpenMP and Cuda C

Amadou Chaibou, Oumarou Sie

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 670, OpenMP, Sequence alignment, Smith-Waterman algorithm

June 22, 2015 by hgpu

Implementation of K-shortest Path Algorithm in GPU Using CUDA

Avadhesh Pratap Singh, Dhirendra Pratap Singh

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Tags: Bioinformatics, Biology, Computer science, CUDA, nVidia, Sequence alignment, Tesla C2075

June 7, 2015 by hgpu

Flexible, Fast and Accurate Sequence Alignment Profiling on GPGPU with PaSWAS

Sven Warris, Feyruz Yalcin, Katherine J. L. Jackson, Jan Peter Nap

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 295, Package, Sequence alignment, Smith-Waterman algorithm, Tesla C1060

April 7, 2015 by hgpu

Arioc: high-throughput read alignment with GPU-accelerated exploration of the seed-and-extend search space

Richard Wilton, Tamas Budavari, Ben Langmead, Sarah J. Wheelan, Steven L. Salzberg, Alexander S. Szalay

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Tags: Bioinformatics, Biology, CUDA, nVidia, Package, Sequence alignment, Tesla K20

March 28, 2015 by hgpu

Implementation of Smith-Waterman algorithm in OpenCL for GPUs

Dzmitry Razmyslovich, Guillermo Marcus, Markus Gipp, Marc Zapatka, Andreas Szillus

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Tags: Bioinformatics, Biology, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Sequence alignment, Smith-Waterman algorithm

February 27, 2015 by azhar ali

PAstudy

Double-precision FPUs in High-Performance Computing: an Embarrassment of Riches?

hpx: The C++ Standard Library for Parallelism and Concurrency

Integration of CUDA Processing within the C++ library for parallelism and concurrency (HPX)

OpenDwarfs: a benchmark suite consisting of different computation/communication idioms, i.e., dwarfs, for state-of-art multicore CPUs, GPUs, Intel MICs and Altera FPGAs

OpenCL Performance Prediction using Architecture-Independent Features

Rodinia Benchmark Suite ported for OpenCL-capable Intel FPGAs

High Performance Computing with FPGAs and OpenCL

The Ocean Tensor Package

Tornado

Using Compiler Snippets to Exploit Parallelism on Heterogeneous Hardware: A Java Reduction Case Study

pocl: Portable Computing Language

Exploiting Task Parallelism with OpenCL: A Case Study

TVM: Open deep learning compiler stack for cpu, gpu and specialized accelerators

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

MyCaffe: A complete deep learning platform written almost entirely in C# for Windows developers

MyCaffe: A Complete C# Re-Write of Caffe with Reinforcement Learning

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

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high performance computing on graphics processing units: hgpu.org

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

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

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

Pairwise Sequence Alignment with Gaps with GPU

Accelerating Protein Structure Prediction using Particle Swarm Optimization on GPU

Improving the Mapping of Smith-Waterman Sequence Database Searches onto CUDA-Enabled GPUs

Concurrent Task Execution on the Intel Xeon Phi

Comparative Study of the Parallelization of the Smith-Waterman Algorithm on OpenMP and Cuda C

Implementation of K-shortest Path Algorithm in GPU Using CUDA

Flexible, Fast and Accurate Sequence Alignment Profiling on GPGPU with PaSWAS

Arioc: high-throughput read alignment with GPU-accelerated exploration of the seed-and-extend search space

Implementation of Smith-Waterman algorithm in OpenCL for GPUs

Recent source codes

PAstudy

hpx: The C++ Standard Library for Parallelism and Concurrency

OpenDwarfs: a benchmark suite consisting of different computation/communication idioms, i.e., dwarfs, for state-of-art multicore CPUs, GPUs, Intel MICs and Altera FPGAs

Rodinia Benchmark Suite ported for OpenCL-capable Intel FPGAs

The Ocean Tensor Package

Tornado

pocl: Portable Computing Language

TVM: Open deep learning compiler stack for cpu, gpu and specialized accelerators

MyCaffe: A complete deep learning platform written almost entirely in C# for Windows developers

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

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International Conference on Image, Video and Signal Processing (IVSP), 2019

International Joint Conference on Signals, Systems and Computers (CSSC), 2018

International Joint Conference on Robotics, Automation and Mechatronics (JCRAM), 2018

International Joint Conference on Robotics and Artificial Intelligence (JCRAI), 2018

International Joint Conference on Computer Vision and Pattern Recognition (CCVPR), 2018