The Schedule

Keynote Speaker

Devesh Tiwari

Abstract: Traditionally, we have assumed that large-scale computing users are fairly boring and that their workloads often do similar things repetitively. Their “boring” nature has served us well so far — we could design “boring” systems and get away with it. But, now things are changing and changing fast. Our workloads and users are becoming interesting and, often, are surprising us with new trends and behavior. That means it is springing excitement into our lives. We need to design interesting solutions and come out of our boredom. The race to apply AI/ML methods is faster than ever. In this talk, I’ll discuss a few lessons I learned as we applied AI/ML methods to computer systems resource management problems.

Biography: Professor Devesh Tiwari is an educator and researcher at Northeastern University where he directs the Goodwill Labs. His group innovates new solutions to make large-scale classical HPC systems and quantum computing systems more efficient, reliable, and cost-effective. Before joining the Northeastern faculty, Devesh was a staff scientist at the United States Department of Energy (DOE) Oak Ridge National Laboratory. Devesh was recognized with multiple awards including the DSN Dependability Rising Star Award, the NSF CAREER Award, and the Facebook Faculty Research Award.

Devesh’s research group has lowered the barrier to entry and accelerated the R&D efforts in multiple emerging computer systems areas including HPC, quantum system software, serverless computing, and AI-driven data center optimizations, via open-sourcing novel software artifacts and datasets. The research contributions from his group have been recognized with many best paper nominations and fellowships/awards. For his teaching and mentoring contributions, he was awarded the Professor of the Year by the Northeastern University chapter of the IEEE Eta Kappa Nu honor society. Devesh has also introduced several novel peer-review elements in the computer systems community in his role as the program co-chair/track co-chair for various conferences. Most recently, he was the Technical Program Committee Co-Chair for HPDC’22 and is the overall Technical Program Committee Co-Chair for IPDPS’23. He is an Associate Editor for Transactions of Parallel & Distributed Computing (TPDS), Transactions of Storage (ToS), and Journal of Parallel & Distributed Computing (JPDC). He was recognized with the TPDS Editorial Excellence Award for his exceptional contributions to the TPDS journal as an editor.

Streaming Machine Learning for Supporting Data Prefetching in Modern Data Storage Systems

Edson Ramiro Lucas Filho, Yang Lun, Fu Kebo and Herodotos Herodotou

Towards Practical Machine Learning Frameworks for Performance Diagnostics in Supercomputers

Burak Aksar, Efe Sencan, Benjamin Schwaller, Vitus Leung, Jim Brandt, Brian Kulis, Manuel Egele and Ayse Coskun

Anomaly Detection in Scientific Datasets using Sparse Representation

Aekyeung Moon, Minjun Kim, Jiaxi Chen and Seung Woo Son

Panel with Speakers

Jai Dayal moderating