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.

翻译：本文形式化了一种并行在线有向无环图探索的变体，其通用性足以直接映射到多种计算场景。在离线和在线版本中，顶点均被赋予处理所需的工作单元权重，子顶点的处理必须至少在其一个父顶点完全处理完成后进行，且在任何给定时刻，任一顶点仅能由一个处理器进行处理。在线版本具有以下额外的自然约束：仅当顶点被处理后，其所需工作单元或其子顶点信息才被获知。通过采用并行计算的Actor模型，本文证明一类自然的并行在线算法满足简单的竞争比界。我们重点展示了该问题在成对势能下的能量景观路径规划或图谱构建场景中的出现，这是涉及软物质组装的多种应用中计算与存储密集型的核心建模组件。该方法通过构建于EASAL（高效组装景观图谱与搜索系统）——一个大型开源软件套件——之上的C++ Actor框架实现进行实验验证，并在HiperGator超级计算机的多CPU核心上运行，展示了线性加速效果。