Work

PolyBench/Python: benchmarking Python environments with polyhedral optimizations

Year: 2021

Type: conference-paper

Abstract: Python has become one of the most used and taught languages nowadays. Its expressiveness, cross-compatibility and ease of use have made it popular in areas as diverse as finance, bioinformatics or machine learning. However, Python programs are often significantly slower to execute than an equivalent native C implementation, especially for computation-intensive numerical kernels.

Source: ACM SIGPLAN International Conference on Compiler Construction (CC)

DOI: 10.1145/3446804.3446842

Authors: Miguel Á. Abella-González, Pedro Carollo-Fernández, Louis-Noël Pouchet, Fabrice Rastello, Gabriel Rodríguez

Institutions: Universidade da Coruña, Colorado State University, Institut national de recherche en sciences et technologies du numérique

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Ministerio de Ciencia e Innovación, Xunta de Galicia

Awards: G3549333136, G6056837937, G726424357, CAREER: Staging Compilers for Heterogeneous Platforms