high performance computing on graphics processing units: hgpu.org

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Reordering strategy for blocking optimization in sparse linear solvers

Gregoire Pichon, Mathieu Faverge, Pierre Ramet, Jean Roman

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Sparse direct solvers, Tesla M2070

October 15, 2016 by hgpu

Efficient CSR-Based Sparse Matrix-Vector Multiplication on GPU

Jiaquan Gao, Panpan Qi, Guixia He

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Sparse matrix, Tesla C2050, Tesla K20

October 4, 2016 by hgpu

Explicit Fourth-Order Runge-Kutta Method on Intel Xeon Phi Coprocessor

Beata Bylina, Joanna Potiopa

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Tags: Computer science, Intel Xeon Phi, Linear Algebra, OpenMP, Sparse matrix

October 4, 2016 by hgpu

Acceleration of Block-Aware Matrix Factorization on Heterogeneous Platforms

Gregory W. Somers

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Tags: ATI, ATI Radeon HD 7950, Computer science, Factorization, Heterogeneous systems, Linear Algebra, OpenCL, Thesis

September 20, 2016 by hgpu

Scheduling of Linear Algebra Kernels on Multiple Heterogeneous Resources

Olivier Beaumont, Terry Cojean, Lionel Eyraud-Dubois, Abdou Guermouche, Suraj Kumar

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Tags: Algorithms, Computer science, CUDA, Factorization, Heterogeneous systems, Linear Algebra, nVidia, Tesla K40

September 13, 2016 by hgpu

A New Architecture for Optimization Modeling Frameworks

Matt Wytock, Steven Diamond, Felix Heide, Stephen Boyd

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Tags: Algorithms, CUDA, Deep learning, Linear Algebra, Mathematics, nVidia, nVidia GeForce GTX Titan X, Package, Python

September 13, 2016 by hgpu

Matrix Computations and Optimization in Apache Spark

Reza Bosagh Zadeh, Xiangrui Meng, Alexander Ulanov, Burak Yavuz, Li Pu, Shivaram Venkataraman, Evan Sparks, Aaron Staple, Matei Zaharia

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, Package, Scala, Spark, Tesla M2050

September 3, 2016 by hgpu

MAGMA Batched: A Batched BLAS Approach for Small Matrix Factorizations and Applications on GPUs

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

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Tags: BLAS, Computer science, CUDA, Factorization, Fluid dynamics, Linear Algebra, nVidia, Tesla K40

August 23, 2016 by hgpu

Parallel experiments with RARE-BLAS

Chemseddine Chohra, Philippe Langlois, David Parello

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Tags: Algorithms, BLAS, Computer science, Intel Xeon Phi, Linear Algebra

August 4, 2016 by hgpu

A survey of sparse matrix-vector multiplication performance on large matrices

Max Grossman, Christopher Thiele, Mauricio Araya-Polo, Florian Frank, Faruk O. Alpak, Vivek Sarkar

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Tags: Computer science, CUDA, Linear Algebra, Numerical Analysis, nVidia, Performance, Sparse matrix, survey, Tesla M2070

August 4, 2016 by hgpu

Stability and Performance of Various Singular Value QR Implementations on Multicore CPU with a GPU

Ichitaro Yamazaki, Stanimire Tomov, Jack Dongarra

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Tags: BLAS, Computer science, CUDA, Linear Algebra, Matrix decomposition, Mixed precision, Numerical Analysis, nVidia, Performance, Tesla K20

July 16, 2016 by hgpu

LU, QR, and Cholesky factorizations: Programming Model, Performance Analysis and Optimization Techniques for the Intel Knights Landing Xeon Phi

Azzam Haidar, Stanimire Tomov, Konstantin Arturov, Murat Guney, Shane Story, Jack Dongarra

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Tags: Algorithms, Computer science, Factorization, Heterogeneous systems, Intel Xeon Phi, Linear Algebra, Performance, Task scheduling

July 13, 2016 by hgpu

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

EnergyUCB-Bandit

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

Effects of OpenCL-Based Parallelization Methods on Explicit Numerical Methods to Solve the Heat Equation

UVaFTLE: Lagrangian finite time Lyapunov exponent extraction for fluid dynamic applications

Challenging Portability Paradigms: FPGA Acceleration Using SYCL and OpenCL

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high performance computing on graphics processing units: hgpu.org

Reordering strategy for blocking optimization in sparse linear solvers

Efficient CSR-Based Sparse Matrix-Vector Multiplication on GPU

Explicit Fourth-Order Runge-Kutta Method on Intel Xeon Phi Coprocessor

Acceleration of Block-Aware Matrix Factorization on Heterogeneous Platforms

Scheduling of Linear Algebra Kernels on Multiple Heterogeneous Resources

A New Architecture for Optimization Modeling Frameworks

Matrix Computations and Optimization in Apache Spark

MAGMA Batched: A Batched BLAS Approach for Small Matrix Factorizations and Applications on GPUs

Parallel experiments with RARE-BLAS

A survey of sparse matrix-vector multiplication performance on large matrices

Stability and Performance of Various Singular Value QR Implementations on Multicore CPU with a GPU

LU, QR, and Cholesky factorizations: Programming Model, Performance Analysis and Optimization Techniques for the Intel Knights Landing Xeon Phi

Recent source codes

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

EnergyUCB-Bandit

Effects of OpenCL-Based Parallelization Methods on Explicit Numerical Methods to Solve the Heat Equation

Faial: finds bugs in CUDA kernels

Intel® SHMEM: Device initiated shared memory based communication library

miniLB: Lattice Botlzmann miniapp w/SYCL

AFOCL

2domination

MFC: Exascale simulation of multiphase/physics fluid dynamics

UVaFTLE: Lagrangian finite time Lyapunov exponent extraction for fluid dynamic applications

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