Amanote Research

Amanote Research

    RegisterSign In

Model-Driven Autotuning of Sparse Matrix-Vector Multiply on GPUs

doi 10.1145/1693453.1693471
Full Text
Open PDF
Abstract

Available in full text

Date

January 1, 2010

Authors
Jee W. ChoiAmik SinghRichard W. Vuduc
Publisher

ACM Press


Related search

Data-Driven Mixed Precision Sparse Matrix Vector Multiplication for GPUs

Transactions on Architecture and Code Optimization
HardwareInformation SystemsArchitectureSoftware
2020English

Register-Based Implementation of the Sparse General Matrix-Matrix Multiplication on GPUs

2018English

Sparse Matrix-Vector Multiplication on GPGPUs

ACM Transactions on Mathematical Software
Applied MathematicsSoftware
2017English

Locality-Aware Parallel Sparse Matrix-Vector and Matrix-Transpose-Vector Multiplication on Many-Core Processors

IEEE Transactions on Parallel and Distributed Systems
HardwareComputational TheorySignal ProcessingArchitectureMathematics
2016English

Strassen's Matrix Multiplication on GPUs

2011English

Performance Modeling and Optimization of Sparse Matrix-Vector Multiplication on NVIDIA CUDA Platform

Journal of Supercomputing
HardwareInformation SystemsTheoretical Computer ScienceArchitectureSoftware
2011English

Perfomance Models for Blocked Sparse Matrix-Vector Multiplication Kernels

2009English

Parallel Multicore CSB Format and Its Sparse Matrix Vector Multiplication

Advances in Linear Algebra & Matrix Theory
2014English

Efficient Sparse Matrix Multiple-Vector Multiplication Using a Bitmapped Format

2013English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2025 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy