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Jay Srinivasan

Jay Srinivasan , Ph.D.
Group Lead
Computational Systems Group
Phone: (510) 495-2942
Fax: (510) 486-6459
1 Cyclotron Road
Mailstop: 59-4010A
Berkeley, CA 94720 US

Biographical Sketch

Jay Srinivasan is group lead for the Computational Systems Group. Prior to that Jay was the team lead for the PDSF system at NERSC. Jay earned his Ph.D. in chemical physics from the University of Minnesota in 1999. He worked at the Minnesota Supercomputing Institute before coming to Berkeley Lab.

Conference Papers

Jay Srinivasan, Richard Shane Canon, "Evaluation of A Flash Storage Filesystem on the Cray XE-6", CUG 2013, May 2013,

Flash storage and other solid-state storage technolo-gies are increasingly being considered as a way to address the growing gap between computation and I/O. Flash storage has a number of benefits such as good random read performance and lower power consumption. However, it has a number of challenges too, such as high cost and high-overhead for write operations. There are a number of ways Flash can be integrated into HPC systems. This paper will discuss some of the approaches and show early results for a Flash file system mounted on a Cray XE-6 using high-performance PCI-e based cards. We also discuss some of the gaps and challenges in integrating flash intoHPC systems and potential mitigations as well as new solid state storage technologies and their likely role in the future

Jay Srinivasan, Richard Shane Canon, Lavanya Ramakrishnan, "My Cray can do that? Supporting Diverse Workloads on the Cray XE-6", CUG 2012, May 2012,

The Cray XE architecture has been optimized to support tightly coupled MPI applications, but there is an in- creasing need to run more diverse workloads in the scientific and technical computing domains. These needs are being driven by trends such as the increasing need to process “Big Data”. In the scientific arena, this is exemplified by the need to analyze data from instruments ranging from sequencers, telescopes, and X-ray light sources. These workloads are typically throughput oriented and often involve complex task dependencies. Can platforms like the Cray XE line play a role here? In this paper, we will describe tools we have developed to support high-throughput workloads and data intensive applications on NERSC’s Hopper system. These tools include a custom task farmer framework, tools to create virtual private clusters on the Cray, and using Cray’s Cluster Compatibility Mode (CCM) to support more diverse workloads. In addition, we will describe our experience with running Hadoop, a popular open-source implementation of MapReduce, on Cray systems. We will present our experiences with this work including successes and challenges. Finally, we will discuss future directions and how the Cray platforms could be further enhanced to support these class of workloads.


Jay Srinivasan, Computational Systems Group Update for NUG 2014, February 6, 2014,