Extreme-Scale Scientific Workflow Analysis and Prediction

The Extreme-Scale Scientific Workflow Analysis and Prediction (X-SWAP) project is developing an integrated analytic performance modeling framework for distributed extreme-scale science workflows. These performance models are designed to reflect observed end-to-end performance of current large scientific workflows and predict performance for future extreme scale workflows.

To develop our framework we focus on experiments in the areas of Light-sources (ALS, LCLS), Astronomical surveys (PTF, LSST), and Genomic sequence production (JGI).

These workflows provide test cases for validation of the models on well-understood use-cases and to enable predictions for future, much larger use-cases in a broad array of science areas.

The modeling framework is build upon performance models developed specifically for individual workflow components such as: Computing; Data transfer; and Data access. Component models are constructed with well-defined interfaces between each other to support aggregation into end-to-end workflow performance models. Verification of component and aggregate models on current workflows is an integral part of our development process supported by our two testbeds at NERSC and ESnet. The workflow models developed and their data-collecting instrumentation will allow the development of workflow monitoring and analysis tools and lead us towards better understanding and predictions for the level of science that a given workflow can deliver.


About the Extreme-Scale Scientific Workflow Analysis and Prediction Project


Publications related to X-Swap


Resources and Links

Members of the collaboration, links to related projects, etc.

Resources and Links

Project Internal

For X-Swap Members only.