PaPaS: A Portable, Lightweight, and Generic Framework for Parallel Parameter Studies

TL;DR

PaPaS is a versatile Python framework that simplifies the setup and execution of parallel parameter and performance studies across various computing environments, enhancing resource utilization.

Contribution

It introduces a portable, lightweight, and generic workflow framework that uses simple parameter files, supporting distributed parallelization in multi-node and multi-tenant systems.

Findings

Enables easy definition and management of parameter studies.

Supports distributed parallel execution via SSH, batch systems, and MPI.

Improves resource utilization in high-performance computing environments.

Abstract

The current landscape of scientific research is widely based on modeling and simulation, typically with complexity in the simulation's flow of execution and parameterization properties. Execution flows are not necessarily straightforward since they may need multiple processing tasks and iterations. Furthermore, parameter and performance studies are common approaches used to characterize a simulation, often requiring traversal of a large parameter space. High-performance computers offer practical resources at the expense of users handling the setup, submission, and management of jobs. This work presents the design of PaPaS, a portable, lightweight, and generic workflow framework for conducting parallel parameter and performance studies. Workflows are defined using parameter files based on keyword-value pairs syntax, thus removing from the user the overhead of creating complex scripts to manage the workflow. A parameter set consists of any combination of environment variables, files, partial file contents, and command line arguments. PaPaS is being developed in Python 3 with support for distributed parallelization using SSH, batch systems, and C++ MPI. The PaPaS framework will run as user processes, and can be used in single/multi-node and multi-tenant computing systems. An example simulation using the BehaviorSpace tool from NetLogo and a matrix multiply using OpenMP are presented as parameter and performance studies, respectively. The results demonstrate that the PaPaS framework offers a simple method for defining and managing parameter studies, while increasing resource utilization.