In the past years, e-Science applications have evolved from large-scale simulations executed in a single cluster to more complex workflows where these simulations are combined with Artificial Intelligence (AI) and High-Performance Data Analytics …
Issued by 6th BSC Severo Ochoa Doctoral Symposium
Title Orchestration of Software Packages in Data Science Workflows
Issued by MS12 - Task-based Programming for Scientific Computing: Runtime Support - Part I of II
SIAM - CSE19
Title Automatic Task-based Parallelization of Python Codes
The Distributed Stream Library enables hybrid Task-based Workflows and Dataflows.
Issued by SuperComputing 2018 - SC18
Barcelona Supercomputing Center - Booth #2038
Title Demo on the use of PyCOMPSs in PyMDSetup
Issued by 8th Workshop on Python for High-Performance and Scientific Computing
Title AutoParallel: A Python Module for Automatic Parallelization and Distributed Execution of Affine Loop Nests
The last improvements in programming languages, programming models, and frameworks have focused on abstracting the users from many programming issues. Among others, recent programming frameworks include simpler syntax, automatic memory management and …
This paper describes the success story of the adaptation of the NMMB-MONARCH online multi-scale atmospheric dust model to PyCOMPSs in order to exploit its inherent parallelism with the minimal developer effort. The paper also includes an evaluation …
Python has been adopted as programming language by a large number of scientific communities. Additionally to the easy programming interface, the large number of libraries and modules that have been made available by a large number of contributors, …
AutoParallel provides an automatic parallelization of affine loop-nests of Python applications.