RCE an HPC Podcast
The Fasterdata Knowledge Base provides proven, operationally sound methods for troubleshooting and solving performance issues. For over 25 years, ESnet has operated an advanced research network with the goal of enabling the highest levels of performance for the Department of Energy (DOE) scientific community. During this time, our engineers have identified a common set of issues that hinder performance and we would like to share our experiences and findings in this knowledge base.
Eli Dart is a network engineer in the ESnet Science Engagement Group, which seeks to use advanced networking to improve scientific productivity and science outcomes for the DOE science facilities, their users, and their collaborators. Eli is a primary advocate for the Science DMZ design pattern, and works with facilities, laboratories, universities, science collaborations, and science programs to deploy data-intensive science infrastructure based on the Science DMZ model. Eli also runs the ESnet network requirements program, which collects, synthesizes, and aggregates the networking needs of the science programs ESnet serves.
Eli has over 15 years of experience in network architecture, design, engineering, performance, and security in scientific and research environments. His primary professional interests are high-performance architectures and effective operational models for networks that support scientific missions, and building collaborations to bring about the effective use of high-performance networks by science projects.
As a member of ESnet's Network Engineering Group, Eli was a primary contributor to the design and deployment of two iterations of the ESnet backbone network - ESnet4 and ESnet5. Prior to ESnet Eli was a lead network engineer at NERSC, DOE's primary supercomputing facility, where he co-led a complete redesign and several years of successful operation of the high-performance network infrastructure there. In addition, Eli spent 14 years as a member of SCinet, the group of volunteers that builds and operates the network for the annual IEEE/ACM Supercomputing conference series, from 1997 through 2010. He served as Network Security Chair for SCinet for the 2000 and 2001 conferences and was a member of the SCinet routing group from 2001 through 2010. Eli holds a Bachelor of Science degree in Computer Science from the Oregon State University College of Engineering.
Brock Palen and Jeff Squyres speak with Jason Zurawski about perfSONAR, a network measurement toolkit designed to provide federated coverage of paths, and help to establish end-to-end usage expectations.
Jason Zurawski is a Science Engagement Engineer at the Energy Sciences Network (ESnet) in the Scientific Networking Division of the Computing Sciences Directorate of the Lawrence Berkeley National Laboratory. ESnet is the high performance networking facility of the US Department of Energy Office of Science. ESnet''s mission is to enable those aspects of the DOE Office of Science research mission that depend on high performance networking for success.
Jason's primary responsibilities include working with members of the research community to identify the roll of networking in scientific workflows, evaluate current requirements, and suggest improvements for future innovations. Jason's professional interests include network monitoring and performance measurement, high performance computing, grid computing, and application development. He is a founding member of several open source R&E software developments, including perfSONAR, OWAMP, BWCTL, NDT, and OSCARS.
Brock Palen and Jeff Squyres speak with Matei Zaharia about Apache Spark, a fast and general engine for large-scale data processing.
Matei Zaharia is an assistant professor of computer science at MIT and CTO of Databricks, the company commercializing Apache Spark. He begun the Spark project at UC Berkeley and continues to do research in big data processing and computer systems. Apart from Spark, he has contributed to other open source projects including Apache Mesos, the SNAP sequence aligner, and Apache Hadoop.