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Automating Heterogeneous Parallelism in Numerical Differential Equations

Utkarsh

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Tags: AMD RX Vega 64, ATI, Computer science, CUDA, Differential equations, Factorization, Heterogeneous systems, Intel, Intel Arc A770, Julia, Numerical Analysis, nVidia, nVidia Quadro RTX 5000, Package, Tesla V100, Thesis

July 14, 2024 by hgpu

Improving Energy Efficiency of Basic Linear Algebra Routines on Heterogeneous Systems with Multiple GPUs

Hadi Zamani Sabzi

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Factorization, Heterogeneous systems, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 1660, nVidia GeForce GTX 980, Thesis

May 21, 2023 by hgpu

Optimizing High-Performance Linpack for Exascale Accelerated Architectures

Noel Chalmers, Jakub Kurzak, Damon McDougall, Paul T. Bauman

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Tags: AMD Radeon Instinct MI250X, ATI, Benchmarking, Computer science, Factorization, HIP, Linear Algebra, MPI, Package, Performance

April 23, 2023 by hgpu

Irregularity Mitigation and Portability Abstractions for Accelerated Sparse Matrix Factorization

Daniel Thürck

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Tags: Algorithms, Computer science, Factorization, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan V, OpenCL, Sparse matrix, Thesis

May 9, 2021 by hgpu

Performance Evaluation of Mixed Precision Algorithms for Solving Sparse Linear Systems

Mawussi Zounon, Nicholas J. Higham, Craig Lucas, Françoise Tisseur

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Tags: Algorithms, Computer science, CUDA, Factorization, Mixed precision, nVidia, Tesla P100, Tesla V100

September 27, 2020 by hgpu

Efficient Matrix Factorization on Heterogeneous CPU-GPU Systems

Yuanhang Yu, Dong Wen, Ying Zhang, Xiaoyang Wang, Wenjie Zhang, Xuemin Lin

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Tags: Algorithms, Computer science, CUDA, Data mining, Factorization, Heterogeneous systems, Machine learning, nVidia, nVidia Quadro P 4000

July 5, 2020 by hgpu

Synergistic CPU-FPGA Acceleration of Sparse Linear Algebra

Mohammadreza Soltaniyeh, Richard P. Martin, Santosh Nagarakatte

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Tags: Computer science, Factorization, FPGA, HLS, Linear Algebra, Matrix multiplication, OpenCL, Sparse

May 10, 2020 by hgpu

GLU3.0: Fast GPU-based Parallel Sparse LU Factorization for Circuit Simulation

Shaoyi Peng, Sheldon X.-D. Tan

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Tags: Algorithms, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX Titan X, Sparse matrix

August 5, 2019 by hgpu

Block based Singular Value Decomposition approach to matrix factorization for recommender systems

Prasad Bhavana, Vikas Kumar, Vineet Padmanabhan

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Tags: AI, Algorithms, Computer science, CUDA, Factorization, Machine learning, nVidia, Package, Tesla M40

July 21, 2019 by hgpu

Using SIMD and SIMT vectorization to evaluate sparse chemical kinetic Jacobian matrices and thermochemical source terms

Nicholas J. Curtis, Kyle E. Niemeyer, Chih-Jen Sung

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Tags: Chemical Physics, Code generation, Computational Physics, Factorization, nVidia, OpenCL, Package, Physics, Tesla C2075, Tesla K40

September 9, 2018 by hgpu

Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

Wei Tan, Shiyu Chang, Liana Fong, Cheng Li, Zijun Wang, Liangliang Cao

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Tags: Algorithms, Compression, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX Titan X, Package

August 19, 2018 by hgpu

clMF: A fine-grained and portable alternating least squares algorithm for parallel matrix factorization

Jing Chen, Jianbin Fanga, Weifeng Liub, Tao Tang, Canqun Yang

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Tags: AMD Radeon R9 Fury X, ATI, Computer science, Factorization, Linear Algebra, nVidia, OpenCL, Package, Tesla K20

June 2, 2018 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

Automating Heterogeneous Parallelism in Numerical Differential Equations

Improving Energy Efficiency of Basic Linear Algebra Routines on Heterogeneous Systems with Multiple GPUs

Optimizing High-Performance Linpack for Exascale Accelerated Architectures

Irregularity Mitigation and Portability Abstractions for Accelerated Sparse Matrix Factorization

Performance Evaluation of Mixed Precision Algorithms for Solving Sparse Linear Systems

Efficient Matrix Factorization on Heterogeneous CPU-GPU Systems

Synergistic CPU-FPGA Acceleration of Sparse Linear Algebra

GLU3.0: Fast GPU-based Parallel Sparse LU Factorization for Circuit Simulation

Block based Singular Value Decomposition approach to matrix factorization for recommender systems

Using SIMD and SIMT vectorization to evaluate sparse chemical kinetic Jacobian matrices and thermochemical source terms

Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

clMF: A fine-grained and portable alternating least squares algorithm for parallel matrix factorization

Recent source codes

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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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