Abstract

Modern science is often conducted on large scale, distributed, heterogeneous and high-performance computing infrastructures. Increasingly, the scale and complexity of both the applications and the underlying execution platforms have been growing. Scientific workflows have emerged as a flexible representation to declaratively express complex applications with data andcontrol dependences. However, it is extremely challengingfor scientists to execute their science workflows in a reliable and scalable way due to a lack of understanding of expected and realistic behavior of complex scientific workflows on large scale and distributed HPC systems. This is exacerbated by failures and anomalies in largescale systems and applications, which makes detecting, analyzing and acting on anomaly events challenging. In this work, we present a prototype of an end-to-end system for modeling and diagnosing the runtime …

Metadata

publication

2016 IEEE International Parallel and Distributed Processing Symposium …, 2016

year

2016

publication date

2016/5/23

authors

Anirban Mandal, Paul Ruth, Ilya Baldin, Dariusz Krol, Gideon Juve, Rajiv Mayani, Rafael Ferreira Da Silva, Ewa Deelman, Jeremy Meredith, Jeffrey Vetter, Vickie Lynch, Ben Mayer, James Wynne, Mark Blanco, Chris Carothers, Justin Lapre, Brian Tierney

link

https://ieeexplore.ieee.org/abstract/document/7530026/

resource_link

http://scitech.isi.edu/wordpress/wp-content/papercite-data/pdf/mandal-lspp-2016.pdf

conference

2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

pages

1370-1379

publisher

IEEE