*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Ann Stroup, Adminstrative Assistant to CSE Chair
Summary Sentence: CSE Faculty Candidate Lecture by Devesh Tiwari, Staff Scientist, Oak Ridge National Laboratory
Full Summary: No summary paragraph submitted.
Devesh TiwariAbstract:
The continued increase in computing power and faster storage subsystems are enabling computational scientists to expedite the process of scientific discovery and helping businesses increase profit on investments. As a result, emerging data-intensive applications (e.g., scientific simulations, data analytics, etc.) are able to produce and analyze unprecedented amount of data on scalable computing systems. At the same time, these trends have also significantly aggravated system reliability and data movement challenges for large-scale computing systems – limiting the execution efficiency of data-intensive workloads. Unfortunately, current strategies for mitigating reliability and data movement challenges are far from optimal for future computing systems due to high performance and I/O overhead.
In this talk, I will discuss new interesting approaches and findings in addressing these challenges. I will present evidences that provide new insights and challenge conventional wisdom about system characteristics, application behavior, and interaction between them. Furthermore, I will share novel techniques that exploit these characteristics to improve the overall efficiency of scalable computing systems. Finally, I will discuss the challenges in efficient and reliable management of future heterogeneous data-centric computing systems and data-intensive applications, and possible approaches toward mitigating these challenges.
Bio:
Devesh Tiwari is a staff scientist at the Oak Ridge National Laboratory. His primary research interests revolves around improving the execution efficiency of data-intensive applications and operational efficiency of scalable systems.
His papers have received best paper award nominations at conferences including Supercomputing (SC), DSN, and IPDPS. His work has appeared in various conferences such as USENIX FAST, SC, DSN, HPCA, IPDPS, and have been covered by media including Slashdot and HPCWire. He has served on the program committee of various conferences including Supercomputing, DSN, IPDPS, ICDCS.
Devesh earned his Ph.D. in electrical and computer engineering from North Carolina State University. Before that, he obtained his B.S. degree in computer science and engineering from the Indian Institute of Technology (IIT) in Kanpur, India.
