high performance computing on graphics processing units: hgpu.org

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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

Parallelizing Exact and Approximate String Matching via Inclusive Scan on a GPU

Yasuaki Mitani, Fumihiko Ino, Kenichi Hagihara

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, Package, String matching

January 19, 2017 by hgpu

Function Call Re-Vectorization

Rubens E. A. Moreira, Sylvain Collange, Fernando Magno Quintao Pereira

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, Package, String matching

December 26, 2016 by hgpu

High performance pattern matching and data remanence on graphics processing units

X. Bellekens

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Tags: Aho Corasick, Algorithms, amazon, BMH, CUDA, Digital Forensic, forensic, GPU, pattern matching, PFAC, remanence, Sequence matching, String matching, Suffix Tree, Tesla K20, Tesla K20c

November 22, 2016 by xhgpu

Using multiple GPUs to accelerate string searching for digital forensic analysis

Ethan Bayne, R. Ian Ferguson, Adam T. Sampson, John Isaacs

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 970 M, nVidia GeForce GTX 980, OpenCL, String matching

November 10, 2016 by hgpu

GPU-Acceleration of In-Memory Data Analytics

Evangelia Sitaridi

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, String matching, Tesla C2070, Tesla K40, Thesis

August 18, 2016 by hgpu

Multiple String Matching on a GPU using CUDAs

Charalampos S. Kouzinopoulos, Panagiotis D. Michailidis, Konstantinos G. Margaritis

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Tags: Algorithms, Biology, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, String matching

July 10, 2015 by hgpu

To Use or Not to Use: Graphics Processing Units for Pattern Matching Algorithms

Vajira Thambawita, Roshan Ragel, Dhammika Elkaduwe

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Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GT 740 M, Performance, String matching

December 30, 2014 by hgpu

Performance Efficient DNA Sequence Detection on GPU Using Parallel Pattern Matching Approach

Rahul Shirude, Valmik B. Nikam, B.B. Meshram

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX Titan, RNA, Sequence alignment, String matching, Tesla K20

July 26, 2014 by hgpu

Various String Matching Algorithms for DNA Sequences to Detect Breast Cancer using CUDA Processors

Nagaveni V, G T Raju

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Tags: Algorithms, Bioinformatics, Biology, CUDA, Databases, Medicine, nVidia, String matching, Tesla C2070

June 28, 2014 by hgpu

Parallel Approaches to Edit Distance and Approximate String Matching

Cary Yang, Kevin Zhang

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, String matching

May 20, 2014 by hgpu

Multicore Computing: Algorithms, Architectures, and Applications

Sanguthevar Rajasekaran, Lance Fiondella, Mohamed Ahmed, Reda A. Ammar

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Tags: Algorithms, Biology, Book, Computer science, CUDA, Linear Algebra, nVidia, String matching, Tesla

January 6, 2014 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

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high performance computing on graphics processing units: hgpu.org

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

Parallelizing Exact and Approximate String Matching via Inclusive Scan on a GPU

Function Call Re-Vectorization

High performance pattern matching and data remanence on graphics processing units

Using multiple GPUs to accelerate string searching for digital forensic analysis

GPU-Acceleration of In-Memory Data Analytics

Multiple String Matching on a GPU using CUDAs

To Use or Not to Use: Graphics Processing Units for Pattern Matching Algorithms

Performance Efficient DNA Sequence Detection on GPU Using Parallel Pattern Matching Approach

Various String Matching Algorithms for DNA Sequences to Detect Breast Cancer using CUDA Processors

Parallel Approaches to Edit Distance and Approximate String Matching

Multicore Computing: Algorithms, Architectures, and Applications

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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