Data-Driven Mixed Precision Sparse Matrix Vector Multiplication for GPUs

Transactions on Architecture and Code Optimization - United States
doi 10.1145/3371275
Full Text
Abstract

Available in full text

Date
Authors
Publisher

Association for Computing Machinery (ACM)

Related search

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

2010English

Sparse Matrix-Vector Multiplication on GPGPUs

ACM Transactions on Mathematical Software
Applied MathematicsSoftware
2017English

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

2018English

Perfomance Models for Blocked Sparse Matrix-Vector Multiplication Kernels

2009English

Strassen's Matrix Multiplication on GPUs

2011English

Efficient Sparse Matrix Multiple-Vector Multiplication Using a Bitmapped Format

2013English

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

Parallel Multicore CSB Format and Its Sparse Matrix Vector Multiplication

Advances in Linear Algebra & Matrix Theory
2014English

Fast Sparse Matrix Multiplication

Lecture Notes in Computer Science
Computer ScienceTheoretical Computer Science
2004English

Amanote Research

Note-taking for researchers

Follow Amanote

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

Privacy PolicyRefund Policy