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Assessing opportunities of SYCL for biological sequence alignment on GPU-based systems

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

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Tags: AMD Radeon RX 6700 XT, AMD Radeon RX Vega 6, ATI, Bioinformatics, Biology, Computational biology, CUDA, Heterogeneous systems, Intel Arc A770, Intel UHD 630, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, nVidia GeForce RTX 2070, nVidia GeForce RTX 3090, nVidia V100, oneAPI, Package, Sequence alignment, SYCL

February 25, 2024 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

Performance Assessment of using OpenCL on FPGA Systems for ODE Solvers

Patrick Tobias Haft

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Tags: Code generation, Computational biology, Computer science, Differential equations, FPGA, nVidia, nVidia GeForce GTX 980 Ti, ODEs, OpenCL, Optimization, Ordinary differential equations, Tesla K20, Thesis

May 16, 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

Parallel Tree Traversal for Nearest Neighbor Query on the GPU

Moohyeon Nam, Jinwoong Kim, Beomseok Nam

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Tags: Algorithms, Biology, Computational biology, Computer science, CUDA, Information Retrieval, Nearest neighbour, nVidia, Tesla K40

September 5, 2016 by hgpu

PROJECTION Algorithm for Motif Finding on GPUs

Jhoirene B. Clemente, Francis George C. Cabarle, Henry N. Adorna

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Tags: Algorithms, Biology, Computational biology, CUDA, nVidia

May 26, 2016 by hgpu

On Longest Repeat Queries Using GPU

Yun Tian, Bojian Xu

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Tags: Biology, Computational biology, Computer science, CUDA, nVidia, nVidia GeForce GTX 660 Ti

January 28, 2015 by hgpu

Hybrid CPU-GPU Pipeline Framework

Fahad Khalid, Frank Feinbube, Andreas Polze

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Tags: Biology, Computational biology, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 680

November 18, 2014 by hgpu

High-accuracy Optimization by Parallel Iterative Discrete Approximation and Multi-GPU Computing

Di Zhao

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Tags: Biology, Computational biology, Computational finance, CUDA, Finance, nVidia, nVidia GeForce GTX 660, Thesis

November 13, 2014 by hgpu

Finding Longest Common Subsequences by GPU-Based Parallel Ant Colony Optimization

David Markvica

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Tags: Algorithms, Biology, Computational biology, Computer science, Heterogeneous systems, Metaheuristics, nVidia, nVidia GeForce GTX 560 Ti, OpenCL, Optimization, Pattern recognition, Thesis

October 27, 2014 by hgpu

On the Efficiency of CPU and Hybrid CPU-GPU Systems in Computational Biology Tasks

Nikolay Pydiura, Pavel Karpov, Yaroslav Blume

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Tags: AMD Radeon R9 280, ATI, Biology, Computational biology, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 480, OpenCL

October 25, 2014 by hgpu

Massive exploration of perturbed conditions of the blood coagulation cascade through GPU parallelization

Paolo Cazzaniga, Marco S. Nobile, Daniela Besozzi, Matteo Bellini, Giancarlo Mauri

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Tags: Bioinformatics, Biology, Computational biology, CUDA, Differential equations, nVidia, ODEs, Ordinary differential equations, Tesla K20

May 30, 2014 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

MSCCL++: A GPU-driven communication stack for scalable AI applications

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

Data-efficient LLM Fine-tuning for Code Generation

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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