Thu, Sep 02, 2021 @ 08:30 AM - 10:00 AM
Date and Time: Thursday, September 2nd @ 8:30-10 AM PDT
Committee: Ewa Deelman, Rafael Ferreira Da Silva, Aiichiro Nakano, Ramesh Govindan, Viktor Prasanna, Michela Taufer
Title: Enabling Efficient Execution of In Situ Workflows
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 proposal proposes a theoretical framework that models the efficiency of in situ execution for evaluating the performance of in situ workflows. 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. In support of the thesis, this work serves as a prerequisite of evaluating in situ scheduling decisions.
WebCast Link: https://usc.zoom.us/j/99685923807
Audiences: Everyone Is Invited
Contact: Lizsl De Leon