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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

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

libhclooc: Software Library Facilitating Out-of-core Implementations of Accelerator Kernels on Hybrid Computing Platforms

Novel Data-Partitioning Algorithms for Performance and Energy Optimization of Data-Parallel Applications on Modern Heterogeneous HPC Platforms

CLTestCheck: Measuring Test Effectiveness for GPU Kernels

CLTestCheck: Measuring Test Effectiveness for GPU Kernels

tensorflow-solvers

TensorFlow Doing HPC

CU2CL: A prototype CUDA-to-OpenCL source-to-source translator

On the Portability of GPU-Accelerated Applications via Automated Source-to-Source Translation

EngineCL: Usability and Performance in Heterogeneous Computing

Cooperative CPU, GPU, and FPGA heterogeneous execution with EngineCL

cuSten: CUDA Finite Difference Library

cuSten – CUDA Finite Difference and Stencil Library

ZMCintegral: An easy way to use multi-GPUs to calculate multi-dimensional integration

A Package for Multi-Dimensional Monte Carlo Integration on Multi-GPUs

TensorFlow.js: A WebGL accelerated JavaScript library for training and deploying ML models

TensorFlow.js: Machine Learning for the Web and Beyond

Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications

Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications

SHA3-ON-GPU: SHA3 implementation on CPU & GPU

GPU Accelerated Keccak (SHA3) Algorithm

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2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

14

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019

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