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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

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures

Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures

Learnergy: a Python library for energy-based machine learning models

Learnergy: Energy-based Machine Learners

Towards automated kernel selection in machine learning systems: A SYCL case study

Towards automated kernel selection in machine learning systems: A SYCL case study

CLFuzz

Automated test generation for OpenCL kernels using fuzzing and constraint solving

High-Performance Graph Primitives on GPUs

Fast Gunrock Subgraph Matching (GSM) on GPUs

Inline Vector Compression for Computational Physics

Inline Vector Compression for Computational Physics

AvA: Automatic virtualization of (general) accelerators

AvA: Accelerated Virtualization of Accelerators

GPGPU Verification of Radix Sort

Let’s sort this out: GPGPU Verification of Radix Sort

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2020

April

27-29

Munich, Germany

8th International Workshop on OpenCL, including SYCLCon, 2019

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

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