SeeSAw: Optimizing Performance of In-Situ Analytics Applications under Power Constraints

Marincic, Ivana; Vishwanath, Venkatram; Hoffmann, Henry

doi:10.1109/ipdps47924.2020.00086

Cited by 3 publications

(1 citation statement)

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“…Zacarias et al 28 estimated the performance degradation arising from co‐located applications using a machine learning model. SeeSAw 29 maximized the performance of in situ analysis under power constraints using energy management approaches. WOWMON 30 implemented a runtime that provides a monitoring scheme for scientific workflows composed of in situ tasks by collecting a set of proposed metrics, and a machine learning‐based performance diagnosis to validate if the collected metrics are necessary or redundant.…”

Section: Related Workmentioning

confidence: 99%

Performance assessment of ensembles of in situ workflows under resource constraints

Anh

Pottier

Silva

et al. 2022

Concurrency and Computation

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Summary 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.

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Section: Related Workmentioning

confidence: 99%