high performance computing on graphics processing units: hgpu.org

hgpu.org » Sparse matrix

Auto-SpMV: Automated Optimizing SpMV Kernels on GPU

Mina Ashoury, Mohammad Loni, Farshad Khunjush, Masoud Daneshtalab

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Tags: Computer science, CUDA, Energy efficiency, Linear Algebra, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 1650, Sparse matrix

February 26, 2023 by hgpu

MSREP: A Fast yet Light Sparse Matrix Framework for Multi-GPU Systems

Jieyang Chen, Chenhao Xie, Jesun S Firoz, Jiajia Li, Shuaiwen Leon Song, Kevin Barker, Mark Raugas, Ang Li

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia DGX-1, OpenMP, Sparse matrix, Tesla V100

October 2, 2022 by hgpu

Efficient Quantized Sparse Matrix Operations on Tensor Cores

Shigang Li, Kazuki Osawa, Torsten Hoefler

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Tags: Computer science, CUDA, Deep learning, Linear Algebra, Mixed precision, nVidia, nVidia A100, Package, Sparse matrix, Tesla V100

October 2, 2022 by hgpu

Exploiting dynamic sparse matrices for performance portable linear algebra operations

Chris Stylianou, Michele Weiland

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Tags: Computer science, CUDA, Heterogeneous systems, HPC, Linear Algebra, MPI, nVidia, OpenMP, Package, Sparse matrix, Tesla V100

October 2, 2022 by hgpu

Sgap: Towards Efficient Sparse Tensor Algebra Compilation for GPU

Genghan Zhang, Yuetong Zhao, Yanting Tao, Zhongming Yu, Guohao Dai, Sitao Huang, Yuan Wen, Pavlos Petoumenos, Yu Wang

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce RTX 2080, nVidia GeForce RTX 3090, Package, Sparse matrix, Tesla V100

September 11, 2022 by hgpu

Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numerical Behaviors

Wei Sun, Ang Li, Tong Geng, Sander Stuijk, Henk Corporaal

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Tags: Benchmarking, Computer science, Matrix multiplication, nVidia, nVidia A100, nVidia GeForce RTX 2080, nVidia GeForce RTX 2080 Ti, Performance, PTX, Sparse matrix

June 12, 2022 by hgpu

Explicit caching HYB: a new high-performance SpMV framework on GPGPU

Chong Chen

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Tags: Computer science, CUDA, FEM, Finite element method, nVidia, Package, Sparse matrix, Tesla V100

April 17, 2022 by hgpu

Heuristic Adaptability to Input Dynamics for SpMM on GPUs

Guohao Dai, Guyue Huang, Shang Yang, Zhongming Yu, Hengrui Zhang, Yufei Ding, Yuan Xie, Huazhong Yang, Yu Wang

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Tags: Algorithms, CUDA, Matrix multiplication, Neural networks, nVidia, nVidia GeForce RTX 2080, nVidia GeForce RTX 3090, Package, Sparse matrix, Tesla V100

February 20, 2022 by hgpu

FSpGEMM: An OpenCL-based HPC Framework for Accelerating General Sparse Matrix-Matrix Multiplication on FPGAs

Erfan Bank Tavakoli, Michael Riera, Masudul Hassan Quraishi, Fengbo Ren

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Tags: Algorithms, Computer science, FPGA, HPC, Linear Algebra, Matrix multiplication, nVidia, nVidia GTX Titan X, OpenCL, Sparse matrix

December 26, 2021 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

Accelerating Sparse Approximate Matrix Multiplication on GPUs

Xiaoyan Liu, Yi Liu, Ming Dun, Bohong Yin, Hailong Yang, Zhongzhi Luan, Depei Qian

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, nVidia, Performance, Sparse matrix, Tesla V100

March 28, 2021 by hgpu

Porting a sparse linear algebra math library to Intel GPUs

Yuhsiang M. Tsai, Terry Cojean, Hartwig Anzt

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Tags: AMD Radeon VII, ATI, Benchmarking, Computer science, HIP, Linear Algebra, nVidia, Package, Performance, Sparse, Sparse matrix, Tesla V100

March 21, 2021 by hgpu

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Clava: C/C++ Source-to-Source Tool based on Clang

Challenges and Opportunities in C/C++ Source-To-Source Compilation

gpuLik

Statistical Computing With Graphics Processing Units

Octo-Tiger: Astrophysics program simulating the evolution of star systems based on the fast multipole method on adaptive Octrees

Stellar Mergers with HPX-Kokkos and SYCL: Methods of using an Asynchronous Many-Task Runtime System with SYCL

BAT: A standardized benchmark suite for auto-tuners

Towards a Benchmarking Suite for Kernel Tuners

BenchDirect: A Directed Language Model for Compiler Benchmarks

A Deep Learning Model for Loop Interchange

Bridging Control-Centric and Data-Centric Optimization

Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE

EvoTorch: Scalable Evolutionary Computation in Python

DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators

RTIndeX: Exploiting Hardware-Accelerated GPU Raytracing for Database Indexing

See all packages

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high performance computing on graphics processing units: hgpu.org

Auto-SpMV: Automated Optimizing SpMV Kernels on GPU

MSREP: A Fast yet Light Sparse Matrix Framework for Multi-GPU Systems

Efficient Quantized Sparse Matrix Operations on Tensor Cores

Exploiting dynamic sparse matrices for performance portable linear algebra operations

Sgap: Towards Efficient Sparse Tensor Algebra Compilation for GPU

Dissecting Tensor Cores via Microbenchmarks: Latency, Throughput and Numerical Behaviors

Explicit caching HYB: a new high-performance SpMV framework on GPGPU

Heuristic Adaptability to Input Dynamics for SpMM on GPUs

FSpGEMM: An OpenCL-based HPC Framework for Accelerating General Sparse Matrix-Matrix Multiplication on FPGAs

Irregularity Mitigation and Portability Abstractions for Accelerated Sparse Matrix Factorization

Accelerating Sparse Approximate Matrix Multiplication on GPUs

Porting a sparse linear algebra math library to Intel GPUs

Recent source codes

Clava: C/C++ Source-to-Source Tool based on Clang

gpuLik

Octo-Tiger: Astrophysics program simulating the evolution of star systems based on the fast multipole method on adaptive Octrees

BAT: A standardized benchmark suite for auto-tuners

BenchDirect: A Directed Language Model for Compiler Benchmarks

A Deep Learning Model for Loop Interchange

Bridging Control-Centric and Data-Centric Optimization

Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE

DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators

RTIndeX: Exploiting Hardware-Accelerated GPU Raytracing for Database Indexing

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