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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

Solving Linear Equations with Conjugate Gradient Method on OpenCL Platforms

Caner Sayin

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Tags: Computer science, Heterogeneous systems, Linear Algebra, nVidia, OpenCL, Sparse matrix, Tesla C2050, Tesla C2070, Thesis

May 20, 2013 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

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

Solving Linear Equations with Conjugate Gradient Method on OpenCL Platforms

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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