Performance assessment of ensembles of in situ workflows under resource constraints
Performance assessment of ensembles of in situ workflows under resource constraints
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DOI：
10.1002/cpe.7111
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发表时间：
2022-06-08
影响因子：
2
通讯作者：
Deelman，Ewa
中科院分区：
文献类型：
--
作者：
Do，Tu Mai Anh;Pottier，Loic;Deelman，Ewa
生物分子方法的科学突破和硬件技术的改进已经从长期运行的模拟转变为一大套同时运行的较短的模拟，称为系综。在合奏中，模拟通常与对模拟产生的数据的分析相结合。现场方法可用于在运行时分析由科学模拟产生的大量数据(即，模拟和分析同时执行)。在这项工作中，我们研究了基于集成的模拟与使用内存分段方法的现场分析配对的执行。使用现场工作流中的分子动力学集合并进行多次模拟和分析，我们首先表明，收集传统的度量指标，如最大完工时间、每个周期的指令数、内存使用率或缓存未命中率，不足以表征集成的复杂行为。我们提出了一种方法来评估工作流集成的性能，这些集成捕获了资源使用的多个方面：资源效率、资源分配和资源供应。实验结果表明，在一个包含多达32个成员的集成中，所提出的方法可以有效地区分不同组件放置的性能。通过评估不同的协同定位场景，我们提出的性能指标展示了在计算节点内协同定位模拟和耦合分析的好处。
Scientific breakthroughs in biomolecular methods and improvements in hardware technology have shifted from a long‐running simulation to a large set of shorter simulations running simultaneously, called an ensemble. In an ensemble, simulations are usually coupled with analyses of data produced by the simulations. In situ methods can be used to analyze large volumes of data generated by scientific simulations at runtime (i.e., simulations and analyses are performed concurrently). In this work, we study the execution of ensemble‐based simulations paired with in situ analyses using in‐memory staging methods. Using an ensemble of molecular dynamics in situ workflows with multiple simulations and analyses, we first show that collecting traditional metrics such as makespan, instructions per cycle, memory usage, or cache miss ratio is not sufficient to characterize complex behaviors of ensembles. We propose a method to evaluate the performance of ensembles of workflows that captures multiple resource usage aspects: resource efficiency, resource allocation, and resource provisioning. Experimental results demonstrate that the proposed method can effectively distinguish the performance of different component placements in an ensemble with up to 32 ensemble members. By evaluating different co‐location scenarios, our proposed performance indicators demonstrate benefits of co‐locating simulation and coupled analyses within a compute node.