SpMVformat

<p>Data from Pichel and Pateiro-López (2018), which contains information on 8111 sparse matrices. Each matrix is described by a set of nine structural features, and the performance of the single-precision SpMV kernel was measured under three storage formats: compressed row storage (CSR), ELLPACK (ELL), and hybrid (HYB). For each matrix and format, performance is expressed as the average GFLOPS (billions of floating-point operations per second), over 1000 SpMV operations.</p>

datasets

A wrapper for machine learning (ML) methods to select among a portfolio of algorithms based on the value of a key performance indicator (KPI). A number of features is used to adjust a model to predict the value of the KPI for each algorithm, then, for a new value of the features the KPI is estimated and the algorithm with the best one is chosen. To learn it can use the regression methods in 'caret' package or a custom function defined by the user. Several graphics available to analyze the results obtained. This library has been used in Ghaddar et al. (2023) <doi:10.1287/ijoc.2022.0090>).

Brais González-Rodríguez

ASML

Algorithm Portfolio Selection with Machine Learning

Ignacio Gómez-Casares

Beatriz Pateiro-López

Julio González-Díaz

María Caseiro-Arias

Antonio Fariña-Elorza

Manuel Timiraos-López

SpMVformat function

<p>A list with x (features) and y (KPIs) data.frames.</p>

Format

Automatic selection of the most suitable storage format for sparse matrices on GPUs — SpMVformat

Automatic selection of the most suitable storage format for sparse matrices on GPUs

SpMVformat: Automatic selection of the most suitable storage format for sparse matrices on GPUs

Description

Usage

Arguments

Format