Performance Prediction

Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows (IPPD)

Integrated End-to-End Performance Prediction and Diagnosis for Extreme Scientific Workflows (IPPD)

This report details the accomplishments from the ASCR funded project “Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows” under the award numbers FWP-66406 and DE-SC0012630, with a focus on the UC San Diego (Award No. DE-SC0012630) part of the accomplishments. We refer to the project as IPPD.

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Performance Prediction
AI-Integrated Workflows
Composable Systems
Distributed Computing
Use-Inspired Solutions
ML & Deep Learning Applications
IPPD

IPPD: Integrated End-to-End Performance Prediction and Diagnosis for Extreme Scientific Workflows

Scientific workflows execute on a loosely connected set of distributed and heterogeneous computational resources. The Integrated End-to-End Performance Prediction and Diagnosis (IPPD) project contributes to clear understanding of the factors that influence the performance and potential optimization of scientific workflows. IPPD addressed three core issues in order to provide insights into workflow execution that can be used to both explain and optimize their execution:

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Performance Prediction
AI-Integrated Workflows
Composable Systems
Distributed Computing
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