Panorama 360: Performance Data Capture and Analysis for End-to-end Scientific Workflows

— Panorama 360 Project Vision:
Provide a resource for the collection, analysis, and sharing of performance data about end-to-end scientific workflows executing on DOE facilities
— A Collaborative Project between:


Diamonds that deliver!

Neutrons, simulation analysis of tRNA-nanodiamond combo could transform drug delivery design principles.

A unique combination of experimentation and simulation was used to shed light on the design principles for improved delivery of RNA drugs, which are promising candidates in the treatment of a number of medical conditions including cancers and genetic disorders.


The DOE Panorama project has developed an SNS workflow to confirm that nanodiamonds enhance the dynamics of tRNA in presence of water. The workflow, enacted by the Pegasus Workflow Management System, calculates the epsilon that best matches experimental data. These calculations were for 10 ns each and the workflows used almost 400,000 CPU hours of time on DOE leadership class systems.

Water is seen as small red and white molecules on large nanodiamond spheres. The colored tRNA can be seen on the nanodiamond surface (Image by Michael Mattheson, OLCF, ORNL)


Panorama 360 Overview


Characterization of instrument data capture, data summarization, and publication


An open access common repository for storing end-to-end workflow performance and resource data captured using a variety of tools


Development of ML techniques for workflow performance analysis and infrastructure troubleshooting


Our Scientific Contributions

IoT-Hub: New IoT data-platform for Virtual Research Environments
R. Filgueira, R. Ferreira da Silva, E. Deelman, V. Christodoulou, and A. Krause

10th International Workshop on Science Gateways (IWSG 2018), 2018

On the Use of Burst Buffers for Accelerating Data-Intensive Scientific Workflows
R. Ferreira da Silva, S. Callaghan, and E. Deelman

12th Workshop on Workflows in Support of Large-Scale Science (WORKS'17), 2017

Toward Prioritization of Data Flows for Scientific Workflows Using Virtual Software Defined Exchanges
A. Mandal, P. Ruth, I. Baldin, R. Ferreira da Silva, and E. Deelman

First International Workshop on Workflow Science (WoWS 2017), 2017

Distributed Workflows for Modeling Experimental Data
V. Lynch, J. B. Calvo, E. Deelman, R. Ferreira da Silva, M. Goswami, Y. Hui, E. Lingerfelt, and J. Vetter

IEEE Xplore, Nov. 2017

Classifying elephant and nice flows in high performance networks
A. Chabbra, and M.Kiran

Innovating the Network for Data Intensive Science (INDIS'17), 2017

PANORAMA: An Approach to Performance Modeling and Diagnosis of Extreme Scale Workflows
E. Deelman, C. Carothers, A. Mandal, B. Tierney, J. S. Vetter, I. Baldin, C. Castillo, G. Juve, D. Krol, V. Lynch, B. Mayer, J. Meredith, T. Proffen, P. Ruth, R. Ferreira da Silva

International Journal of High Performance Computing Applications, vol. 31, iss. 1, pp. 4-18, 2017

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