Parallel Online Directed Acyclic Graph Exploration for Atlasing Soft-Matter Assembly Configuration Spaces

TL;DR

This paper introduces a parallel online DAG exploration method tailored for atlasing soft-matter assembly configuration spaces, demonstrating its efficiency and scalability through implementation on supercomputers.

Contribution

It formalizes a parallel online DAG exploration framework and applies it to energy landscape atlasing, showing significant speedup and practical validation on high-performance computing systems.

Findings

Achieves linear speedup on supercomputers

Validates the approach with a C++ Actor Framework implementation

Demonstrates applicability to energy landscape atlasing

Abstract

The paper formalizes a version of parallel online directed acyclic graph (DAG) exploration, general enough to be readily mapped to many computational scenarios. In both the offline and online versions, vertices are weighted with the work units required for their processing, at least one parent must be completely processed before a child is processed, and at any given time only one processor can work on any given vertex. The online version has the following additional natural restriction: only after a vertex is processed, are its required work units or its children known. Using the Actor Model of parallel computation, it is shown that a natural class of parallel online algorithms meets a simple competitive ratio bound. We demonstrate and focus on the problem's occurrence in the scenario of energy landscape roadmapping or atlasing under pair-potentials, a highly compute-and-storage intensive modeling component integral to diverse applications involving soft-matter assembly. The method is experimentally validated using a C++ Actor Framework (CAF) software implementation built atop EASAL (Efficient Atlasing and Search of Assembly Landscapes), a substantial opensource software suite, running on multiple CPU cores of the HiperGator supercomputer, demonstrating linear speedup results.