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Improving Performance of Hardware Accelerators by Optimizing Data Movement: A Bioinformatics Case Study

Peter Knoben, Nikolaos Alachiotis

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Tags: Bioinformatics, Biology, Computer science, FPGA, OpenCL, Package

February 12, 2023 by hgpu

SaLoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs

Seongyeon Park, Hajin Kim, Tanveer Ahmad, Nauman Ahmed, Zaid Al-Ars, H. Peter Hofstee, Youngsok Kim, Jinho Lee

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Tags: Bioinformatics, Biology, CUDA, Databases, nVidia, nVidia GeForce GTX 1650, nVidia GeForce RTX 3090, Sequence alignment, Smith-Waterman algorithm, String matching

January 29, 2023 by hgpu

PySAGES: flexible, advanced sampling methods accelerated with GPUs

Pablo F. Zubieta Rico, Ludwig Schneider, Gustavo Perez-Lemus, Riccardo Alessandri, Siva Dasetty, Cintia A. Menéndez, Yiheng Wu, Yezhi Jin, Trung Nguyen, John Parker, Andrew L. Ferguson, Juan J. de Pablo

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Tags: Biology, Chemistry, Computational Physics, CUDA, Molecular dynamics, nVidia, Package, Physics, Python, Tesla V100

January 22, 2023 by hgpu

Assessing Opportunities of SYCL and Intel oneAPI for Biological Sequence Alignment

Manuel Costanzo, Enzo Rucci, Carlos García Sánchez, Marcelo Naiouf, Manuel Prieto-Matías

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Tags: Bioinformatics, Biology, Computational biology, Computer science, CUDA, Heterogeneous systems, Intel, Intel UHD 630, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, nVidia GeForce RTX 2070, nVidia GeForce RTX 3090, OpenCL, Sequence alignment, SYCL, Tesla V100

November 27, 2022 by hgpu

Understanding the Power of Evolutionary Computation for GPU Code Optimization

Jhe-Yu Liou, Muaaz Awan, Steven Hofmeyr, Stephanie Forrest, Carole-Jean Wu

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Tags: Bioinformatics, Biology, Computer science, CUDA, Evolutionary Computations, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 1080 Ti, Package, performance portability, Sequence alignment, Tesla P100, Tesla V100

September 4, 2022 by hgpu

AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs

André Müller, Bertil Schmidt, Richard Membarth, Roland Leißa, Sebastian Hack

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Tags: AMD Radeon Instinct MI100, ATI, Bioinformatics, Biology, CUDA, Next-Generation sequencing, nVidia, nVidia GeForce RTX 3090, OpenCL, Package, Sequence alignment, Tesla A100

May 22, 2022 by hgpu

Migrating CUDA to oneAPI: A Smith-Waterman Case Study

Manuel Costanzo, Enzo Rucci, Carlos Garcia Sanchez, Marcelo Naiouf, Manuel Prieto-Matias

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Tags: Bioinformatics, Biology, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce RTX 2070, Sequence alignment, Smith-Waterman algorithm, SYCL

March 27, 2022 by hgpu

FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours

Shenggan Cheng, Ruidong Wu, Zhongming Yu, Binrui Li, Xiwen Zhang, Jian Peng, Yang You

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Tags: Biology, Computer science, CUDA, Machine learning, nVidia, nVidia A100, Package

March 6, 2022 by hgpu

NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics

Raimondas Galvelis, Alejandro Varela-Rial, Stefan Doerr, Roberto Fino, Peter Eastman, Thomas E. Markland, John D. Chodera, Gianni De Fabritiis

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Tags: Biology, Chemistry, CUDA, GeForce RTX 2080 Ti, Machine learning, Molecular dynamics, Molecular simulation, Neural networks, nVidia, Package

January 23, 2022 by hgpu

linus: Conveniently explore, share, and present large-scale biological trajectory data from a web browser

Johannes Waschke, Mario Hlawitschka, Kerim Anlas, Vikas Trivedi, Ingo Roeder, Jan Huisken, Nico Scherf

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Tags: Biology, OpenCL, Package, PyOpenCL, Python

January 10, 2021 by hgpu

GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research

Scott LeGrand, Aaron Scheinberg, Andreas F. Tillack, Mathialakan Thavappiragasam, Josh V. Vermaas, Rupesh Agarwal, Jeff Larkin, Duncan Poole, Diogo Santos-Martins, Leonardo Solis-Vasquez, Andreas Koch, Stefano Forli, Oscar Hernandez, Jeremy C. Smith, Ada Sedova

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Tags: Biology, Biomolecules, Computational biology, CUDA, molecular docking, nVidia, nVidia DGX-2, nVidia GeForce RTX 2080 Ti, OpenCL, supercomputer, Tesla V100

July 12, 2020 by hgpu

Evaluating the Energy Efficiency of OpenCL-accelerated AutoDock Molecular Docking

Leonardo Solis-Vasquez, Diogo Santos-Martins, Andreas Koch, Stefano Forli

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Tags: AMD Radeon RX Vega 56, ATI, Biology, Chemistry, Energy-efficient computing, Heterogeneous systems, molecular docking, nVidia, OpenCL, Tesla V100

March 1, 2020 by hgpu

high performance computing on graphics processing units: hgpu.org

Improving Performance of Hardware Accelerators by Optimizing Data Movement: A Bioinformatics Case Study

SaLoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs

PySAGES: flexible, advanced sampling methods accelerated with GPUs

Assessing Opportunities of SYCL and Intel oneAPI for Biological Sequence Alignment

Understanding the Power of Evolutionary Computation for GPU Code Optimization

AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs

Migrating CUDA to oneAPI: A Smith-Waterman Case Study

FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours

NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics

linus: Conveniently explore, share, and present large-scale biological trajectory data from a web browser

GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research

Evaluating the Energy Efficiency of OpenCL-accelerated AutoDock Molecular Docking

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