Mon, Nov 21, 2022 @ 01:00 PM - 03:00 PM
PhD Candidate: Tu Do
Title: Optimizing Execution of In situ Workflows
Committee: Ewa Deelman (Chair), Aiichiro Nakano, Viktor Prasanna, Michela Taufer
Advances in high-performance computing (HPC) allow scientific simulations to run at an ever-increasing scale, generating a large amount of data that needs to be analyzed over time. Conventionally, the simulation outputs the entire simulated data set to the file system for later post-processing. Unfortunately, the slow growth of I/O technologies compared to the computing capability of present-day processors causes an I/O bottleneck of post-processing as saving data to storage is not as fast as data is generated. According to data-centric models, a new processing paradigm has recently emerged, called in situ, where simulation data is analyzed on-the-fly to reduce the expensive I/O cost of saving massive data for post-processing. Since an in situ workflow usually consists of co-located tasks running concurrently on the same resources in an iterative manner, the execution yields complicated behaviors that create challenges in evaluating the efficiency of an in situ run. To enable efficient execution of in situ workflows, this dissertation proposes a framework to enable in situ execution between simulations and analyses and introduces a computational efficiency model to characterize efficiency of an in situ execution. By extending the proposed performance model to resource-aware performance indicators, we introduce a method to assess resource usage, resource allocation, and resource provisioning for in situ workflow ensembles. Finally, we discuss the ideas of designing effective scheduling of a workflow ensemble through determining appropriate co-scheduling strategies and resource assignment for each simulation and analysis in the ensemble.
WebCast Link: https://usc.zoom.us/j/94496448526
Audiences: Everyone Is Invited
Contact: Lizsl De Leon