high performance computing on graphics processing units: hgpu.org

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Parallel Implementation of the Finite Element Method on Graphics Processors for the Solution of Incompressible Flows

Mahmut Murat Gocmen

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Tags: Algorithms, CUBLAS, CUDA, FEM, Finite element method, Fluid dynamics, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 280, Package, Tesla C2075, Thesis

January 16, 2015 by hgpu

Batched Matrix Computations on Hardware Accelerators Based on GPUs

Azzam Haidar, Tingxing Dong, Piotr Luszczek, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Factorization, nVidia, Tesla K40

January 15, 2015 by hgpu

Minerva: A Scalable and Highly Efficient Training Platform for Deep Learning

Minjie Wang, Tianjun Xiao, Jianpeng Li, Jiaxing Zhang, Chuntao Hong, Zheng Zhang

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Tags: Computer science, CUBLAS, CUDA, Machine learning, Neural networks, nVidia

December 15, 2014 by hgpu

KBLAS: An Optimized Library for Dense Matrix-Vector Multiplication on GPU Accelerators

Ahmad Abdelfattah, David Keyes, Hatem Ltaief

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Mathematical Software, nVidia, Package, Tesla K20

October 8, 2014 by hgpu

On the evaluation of matrix polynomials using several GPGPUs

Pedro Alonso, Murilo Boratto, J. Peinado, J. Ibanez, Jorge Sastre

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Heterogeneous systems, nVidia, Tesla K20, Thesis

September 20, 2014 by hgpu

Accelerating Krylov Subspace Solvers on Graphics Processing Units

Hartwig Anzt, Stanimire Tomov, Piotr Luszczek, Ichitaro Yamazaki, Jack Dongarra, William Sawyer

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Sparse matrix, Tesla K20

August 3, 2014 by hgpu

GiMMiK – Generating Bespoke Matrix Multiplication Kernels for Various Hardware Accelerators; Applications in High-Order Computational Fluid Dynamics

Bartosz D. Wozniak

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Tags: CUBLAS, CUDA, Fluid dynamics, Linear Algebra, Matrix multiplication, nVidia, OpenCL, Package, Python, Tesla K40, Thesis

July 7, 2014 by hgpu

Parallel technologies for solving system of the linear equations by the conjugate gradient method

Eduard Bondarenko

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Tags: Computer science, Conjugate gradient solver, CUBLAS, nVidia, OpenACC, OpenMP, Tesla M2070

June 14, 2014 by hgpu

Heterogeneous Computing for Solving System of the Linear Equations by the Conjugate Gradient Method

Eduard Bondarenko

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Tags: Computer science, Conjugate gradient solver, CUBLAS, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 650 Ti, OpenACC

May 29, 2014 by hgpu

Optimizing Krylov Subspace Solvers on Graphics Processing Units

Hartwig Anzt, Stanimire Tomov, Piotr Luszczek, Ichitaro Yamazaki, Jack Dongarra, William Sawyer

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Sparse matrix, Tesla K20

April 6, 2014 by hgpu

Optimized Deep Learning Architectures with Fast Matrix Operation Kernels on Parallel Platform

Ying Zhang, Saizheng Zhang

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Tags: Computer science, CUBLAS, CUDA, Machine learning, Neural networks, nVidia

February 2, 2014 by hgpu

Hybrid algorithms for efficient Cholesky decomposition and matrix inverse using multicore CPUs with GPU accelerators

Gary Macindoe

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, Machine learning, Matrix decomposition, nVidia, nVidia GeForce 9500 GT, nVidia GeForce GTX 285, Tesla S1070, Thesis

January 29, 2014 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

Parallel Implementation of the Finite Element Method on Graphics Processors for the Solution of Incompressible Flows

Batched Matrix Computations on Hardware Accelerators Based on GPUs

Minerva: A Scalable and Highly Efficient Training Platform for Deep Learning

KBLAS: An Optimized Library for Dense Matrix-Vector Multiplication on GPU Accelerators

On the evaluation of matrix polynomials using several GPGPUs

Accelerating Krylov Subspace Solvers on Graphics Processing Units

GiMMiK – Generating Bespoke Matrix Multiplication Kernels for Various Hardware Accelerators; Applications in High-Order Computational Fluid Dynamics

Parallel technologies for solving system of the linear equations by the conjugate gradient method

Heterogeneous Computing for Solving System of the Linear Equations by the Conjugate Gradient Method

Optimizing Krylov Subspace Solvers on Graphics Processing Units

Optimized Deep Learning Architectures with Fast Matrix Operation Kernels on Parallel Platform

Hybrid algorithms for efficient Cholesky decomposition and matrix inverse using multicore CPUs with GPU accelerators

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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