high performance computing on graphics processing units: hgpu.org

hgpu.org » Sequence alignment

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

Alberto Zeni, Giulia Guidi, Marquita Ellis, Nan Ding, Marco D. Santambrogio, Steven Hofmeyr, Aydın Buluç, Leonid Oliker, Katherine Yelick

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Tags: Bioinformatics, Biology, CUDA, nVidia, Sequence alignment, Tesla V100

February 16, 2020 by hgpu

SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs

Mohammed Alser, Taha Shahroodi, Juan Gomez-Luna, Can Alkan, Onur Mutlu

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Tags: Algorithms, Computer science, CUDA, FPGA, nVidia, Package, Sequence alignment

October 27, 2019 by hgpu

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

Huihui Zou, Shanjiang Tang, Ce Yu, Hao Fu, Yusen Li, Wenjie Tang

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Tags: Algorithms, APU, Bioinformatics, Biology, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 750, OpenCL, Sequence alignment, Smith-Waterman algorithm

December 9, 2018 by hgpu

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

Face.evoLVe: High-Performance Face Recognition Library based on PyTorch & PaddlePaddle

Face.evoLVe: A High-Performance Face Recognition Library

OpenCLDecompiler

A method for decompilation of AMD GCN kernels to OpenCL

PULP: a (Parallel Ultra-Low-Power) open-source multi-core computing platform

DNN is not all you need: Parallelizing Non-Neural ML Algorithms on Ultra-Low-Power IoT Processors

StreamBlocks: A compiler for heterogeneous dataflow computing

APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores

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high performance computing on graphics processing units: hgpu.org

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

SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs

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

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

Recent source codes

Face.evoLVe: High-Performance Face Recognition Library based on PyTorch & PaddlePaddle

OpenCLDecompiler

PULP: a (Parallel Ultra-Low-Power) open-source multi-core computing platform

StreamBlocks: A compiler for heterogeneous dataflow computing

Bempp-cl: a boundary element method library

futhark-stencil-benchmarks

POCL: Portable Computing Language

K-FAC: PyTorch Distributed K-FAC Preconditioner

pystencils

APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores

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Featured events

9th International Workshop on OpenCL and SYCL, 2021

The 18th International Conference on High Performance Computing & Simulation (HPCS), 2020

The Fifth International Workshop on GPU Computing and AI (GCA), 2020

2nd International Conference on Frontiers of Intelligent Manufacturing and Automation (CFIMA’21), 2021

International Conference on Cyber Physical Systems and IoT (CPSIOT’20), 2020