An open-source job management framework for parameter-space exploration: OACIS

Abstract

We present an open-source software framework for parameter-space exploration, named OACIS, which is useful to manage vast amount of simulation jobs and results in a systematic way. Recent development of high-performance computers enabled us to explore parameter spaces comprehensively, however, in such cases, manual management of the workflow is practically impossible. OACIS is developed aiming at reducing the cost of these repetitive tasks when conducting simulations by automating job submissions and data management. In this article, an overview of OACIS as well as a getting started guide are presented.

References in corpus (3)

• Multilayer weighted social network model
• Modeling the role of relationship fading and breakup in social network formation
• A tool for parameter-space explorations

Cited by in corpus (10)

• Structural transition in social networks: The role of homophily
• Do economic effects of the anti-COVID-19 lockdowns in different regions interact through supply chains?
• Five rules for friendly rivalry in direct reciprocity
• Evolution of direct reciprocity in group-structured populations
• Friendly-rivalry solution to the iterated $n$-person public-goods game
• Grouping promotes both partnership and rivalry with long memory in direct reciprocity
• Deep learning based parameter search for an agent based social network model
• CARAVAN: a framework for comprehensive simulations on massive parallel machines
• PaPaS: A Portable, Lightweight, and Generic Framework for Parallel Parameter Studies
• Supercharging the APGAS Programming Model with Relocatable Distributed Collections