FlowBench Raw Data Archive
The repo that provides data archive for DOE PoSeiDon project. It also contains scripts and instructions to parse the data.
George, Papadimitriou↗
Engineering topics
Mayani, Rajiv
Publications and source records attributed to Mayani, Rajiv.
End-to-end online performance data capture and analysis for scientific workflows
With the increased prevalence of employing workflows for scientific computing and a push towards exascale computing, it has become paramount that we are able to analyze characteristics of scientific applications to better understand their impact on the underlying infrastructure and vice-versa. Such analysis can help drive the design, development, and optimization of these next generation systems and solutions. Here, we present the architecture, integrated with existing well-established and newly developed tools, to collect online performance statistics of workflow executions from various, heterogeneous sources and publish them in a distributed database (Elasticsearch). Using this architecture, we are able to correlate online workflow performance data, with data from the underlying infrastructure, and present them in a useful and intuitive way via an online dashboard. We have validated our approach by executing two classes of real-world workflows, both under normal and anomalous conditions. The first is an I/O-intensive genome analysis workflow; the second, a CPU- and memory-intensive material science workflow. Based on the data collected in Elasticsearch, we are able to demonstrate that we can correctly identify anomalies that we injected. The resulting end-to-end data collection of workflow performance data is an important resource of training data for automated machine learning analysis.
97 MATHEMATICS AND COMPUTING↗
The Evolution of the Pegasus Workflow Management Software
Since 2001 the Pegasus Workflow Management System has evolved into a robust and scalable system that automates the execution of a number of complex applications running on a variety of heterogeneous, distributed high-throughput, and high-performance computing environments. Pegasus was built on the principle of separation between the workflow description and workflow execution, providing the ability to port and adapt the workflow based on the target execution environment. Through its user-driven research and development it has adapted to the needs of a number of scientific communities, utilizing and developing novel algorithms and software engineering solutions. This paper describes the evolution of Pegasus over time and provides motivations behind the design decisions. The paper concludes with selected lessons learned.
97 MATHEMATICS AND COMPUTING↗
Toward an End-to-end Framework for Modeling, Monitoring and Anomaly Detection for Scientific Workflows
Explore the source record for details and available documents.
Mandal, Anirban↗