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gSMat: A Scalable Sparse Matrix-based Join for SPARQL Query Processing

Xiaowang Zhang, Mingyue Zhang, Peng Peng, Jiaming Song, Zhiyong Feng, Lei Zou

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Tags: Computer science, CUDA, Databases, nVidia, Sparse matrix, Tesla M40

July 28, 2018 by hgpu

Revisiting Online Autotuning for Sparse-Matrix Vector Multiplication Kernels on High-Performance Accelerators

Simon Garcia De Gonzalo, Wen-Mei Hwu, Simon D. Hammond, Christian R. Trott

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Tags: Computer science, Intel Xeon Phi, nVidia, Performance, Sparse matrix, Tesla P100

July 15, 2018 by hgpu

Strategies for the Heterogeneous Execution of Large-Scale Simulations on Hybrid Supercomputers

Xavier Alvarez, Andrey Gorobets, F. Xavier Trias

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Tags: cfd, Computational Physics, Fluid dynamics, Heterogeneous systems, HPC, Hybrid computing, MPI, nVidia, OpenCL, Sparse matrix, Tesla K40

June 24, 2018 by hgpu

Optimizing Sparse Matrix-Vector Multiplication on Emerging Many-Core Architectures

Shizhao Chen, Jianbin Fang, Donglin Chen, Chuanfu Xu, Zheng Wang

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Tags: Computer science, Intel Xeon Phi, Machine learning, OpenMP, Performance, Sparse matrix

June 9, 2018 by hgpu

A Comparative Study on Exact Triangle Counting Algorithms on the GPU

Leyuan Wang, Yangzihao Wang, Carl Yang, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Filtering, Graph theory, nVidia, Sparse matrix, Tesla K40

April 28, 2018 by hgpu

Implementing Push-Pull Efficiently in GraphBLAS

Carl Yang, Aydin Buluc, John D. Owens

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Tags: Algorithm optimization, Algorithms, BLAS, Computer science, CUDA, Graph theory, Linear Algebra, nVidia, Sparse matrix, Tesla K40

April 15, 2018 by hgpu

High-performance sparse matrix-matrix products on Intel KNL and multicore architectures

Yusuke Nagasaka, Satoshi Matsuoka, Ariful Azad, Aydin Buluc

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Tags: Algorithms, Computer science, Intel Xeon Phi, Matrix multiplication, Sparse matrix

April 7, 2018 by hgpu

Sparse Matrix-Matrix Multiplication on Multilevel Memory Architectures : Algorithms and Experiments

Mehmet Deveci, Simon D. Hammond, Michael M. Wolf, Sivasankaran Rajamanickam

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, Sparse matrix, Tesla P100

April 7, 2018 by hgpu

Design Principles for Sparse Matrix Multiplication on the GPU

Carl Yang, Aydin Buluc, John D. Owens

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, Sparse matrix, Tesla K40

March 31, 2018 by hgpu

Revisiting Online Autotuning for Sparse-Matrix Vector Multiplication Kernels on Next-Generation Architectures

Simon Garcia De Gonzalo, Simon D. Hammond, Christian R. Trott, Wen-Mei Hwu

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Tags: Auto-Tuning, Computer science, CUDA, Intel Xeon Phi, nVidia, Sparse matrix, Tesla K20, Tesla K40, Tesla K80, Tesla P100

January 28, 2018 by hgpu

Accelerated Sparse Matrix Operations in Nonlinear Least Squares Solvers

Lukas Polok

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Tags: Algorithms, Computer science, Differential equations, Factorization, FEM, Finite element method, Linear Algebra, nVidia, nVidia GeForce GTX 680, OpenCL, Partial differential equations, PDEs, Sparse matrix, Tesla K40, Thesis

December 19, 2017 by hgpu

GPU Parallelization for Unstructured Sparse Matrix Problems with OpenMP 4.5 and OpenACC

Stefan Rosenberger, Gundolf Haase

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 1060, OpenACC, OpenMP, Sparse matrix

November 21, 2017 by hgpu

CLUEstering: a high-performance density-based clustering library for scientific computing

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high performance computing on graphics processing units: hgpu.org

gSMat: A Scalable Sparse Matrix-based Join for SPARQL Query Processing

Revisiting Online Autotuning for Sparse-Matrix Vector Multiplication Kernels on High-Performance Accelerators

Strategies for the Heterogeneous Execution of Large-Scale Simulations on Hybrid Supercomputers

Optimizing Sparse Matrix-Vector Multiplication on Emerging Many-Core Architectures

A Comparative Study on Exact Triangle Counting Algorithms on the GPU

Implementing Push-Pull Efficiently in GraphBLAS

High-performance sparse matrix-matrix products on Intel KNL and multicore architectures

Sparse Matrix-Matrix Multiplication on Multilevel Memory Architectures : Algorithms and Experiments

Design Principles for Sparse Matrix Multiplication on the GPU

Revisiting Online Autotuning for Sparse-Matrix Vector Multiplication Kernels on Next-Generation Architectures

Accelerated Sparse Matrix Operations in Nonlinear Least Squares Solvers

GPU Parallelization for Unstructured Sparse Matrix Problems with OpenMP 4.5 and OpenACC

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