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A Novel Graphical Processing Unit Method for Power Systems Security Analysis

Laurie Elizabeth Miller

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 470, Security, Sparse matrix, Thesis

January 30, 2014 by hgpu

Hardware Acceleration Technologies in Computer Algebra: Challenges and Impact

Sardar Anisul Haque

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Sorting, Sparse matrix, Tesla C2050, Tesla M2050, Thesis

December 23, 2013 by hgpu

Algorithm for Sparse Approximate Inverse Preconditioners in the Conjugate Gradient Method

Ilya B. Labutin, Irina V. Surodina

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Tags: Algorithms, Computer science, CUDA, nVidia, Sparse matrix, Tesla M2090

December 9, 2013 by hgpu

Locality Analysis for Characterizing Applications Based on Sparse Matrices

Noboru Tanabe, Sonoko Tomimori, Masami Takata, Kazuki Joe

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Tags: Computer science, CUDA, nVidia, Sparse matrix, Tesla C2050

December 9, 2013 by hgpu

Architecture-and Workload-Aware Heterogeneous Algorithms for Sparse Matrix Vector Multiplication

Sivaramakrishna Bharadwaj Indarapu, Manoj Maramreddy, Kishore Kothapalli

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX 480, Sparse matrix

October 21, 2013 by hgpu

Sparse Matrix Algorithms Using GPGPU

Kaupo Kuresson

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Computer science, nVidia, OpenCL, Package, Sparse matrix, Tesla S1070, Tesla T10, Thesis

September 18, 2013 by hgpu

Efficient CUDA polynomial preconditioned Conjugate Gradient solver for Finite Element computation of elasticity problems

Jianfei Zhang, Lei Zhang

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Tags: Algorithms, Computer science, Conjugate gradient solver, CUDA, Mixed precision, nVidia, nVidia GeForce GT 430, Sparse matrix

September 14, 2013 by hgpu

Efficient Sparse Matrix-Vector Multiplication on x86-Based Many-Core Processors

Xing Liu, Mikhail Smelyanskiy, Edmond Chow, Pradeep Dubey

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Tags: Algorithms, Computer science, CUDA, Intel Phi, nVidia, Performance, Sparse matrix, Tesla K20

August 29, 2013 by hgpu

GPU Acceleration of Graph Matching, Clustering, and Partitioning

Bastiaan Onne Fagginger Auer

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Tags: Algorithms, Clustering, Computer science, CUDA, Graph theory, nVidia, Sparse matrix, Tesla C2075, Thesis

August 6, 2013 by hgpu

A unified sparse matrix data format for modern processors with wide SIMD units

Moritz Kreutzer, Georg Hager, Gerhard Wellein, Holger Fehske, Alan R. Bishop

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Intel Phi, Mathematical Software, nVidia, Sparse matrix, Tesla K20

July 25, 2013 by hgpu

FastSpMM: An Efficient Library for Sparse Matrix Matrix Product on GPUs

Gloria Ortega, Francisco Vazquez, Inmaculada Garcia, Ester M. Garzon

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Tags: Computer science, Matrix multiplication, nVidia, nVidia GeForce GTX 480, Package, Sparse matrix, Tesla C2050

June 12, 2013 by hgpu

A Performance Modeling and Optimization Analysis Tool for Sparse Matrix-Vector Multiplication on GPUs

Ping Guo, Liqiang Wang, Po Chen

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Tags: Algorithms, Computer science, CUDA, nVidia, Sparse matrix, Tesla C2050

May 27, 2013 by hgpu

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

Shamrock: Multi-GPU hydrodynamics for astrophysics

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

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high performance computing on graphics processing units: hgpu.org

A Novel Graphical Processing Unit Method for Power Systems Security Analysis

Hardware Acceleration Technologies in Computer Algebra: Challenges and Impact

Algorithm for Sparse Approximate Inverse Preconditioners in the Conjugate Gradient Method

Locality Analysis for Characterizing Applications Based on Sparse Matrices

Architecture-and Workload-Aware Heterogeneous Algorithms for Sparse Matrix Vector Multiplication

Sparse Matrix Algorithms Using GPGPU

Efficient CUDA polynomial preconditioned Conjugate Gradient solver for Finite Element computation of elasticity problems

Efficient Sparse Matrix-Vector Multiplication on x86-Based Many-Core Processors

GPU Acceleration of Graph Matching, Clustering, and Partitioning

A unified sparse matrix data format for modern processors with wide SIMD units

FastSpMM: An Efficient Library for Sparse Matrix Matrix Product on GPUs

A Performance Modeling and Optimization Analysis Tool for Sparse Matrix-Vector Multiplication on GPUs

Recent source codes

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

Celerity Runtime: High-level C++ for Accelerator Clusters

wgpy: WebGL accelerated numpy-compatible array library for web browser

Microbenchmarking OpenMP target offload with Catch2

SUperman: Highly Efficient Permanent Computation Library

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

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