Recent Advances in Parallel Algorithms


July 24, 2014


Parallelism abounds in modern hardware—from the datacenter to multi-cores, GPUs, and FPGAs. On the other hand, important algorithms, such as graph algorithms, dynamic programming, and finite-state machine processing involve fine-grained dependencies and do not directly map on to this parallel hardware. Harnessing the parallelism available for these algorithms requires new algorithms, new programming languages, and new runtime systems. This session will present recent advances in this area and will serve as forum for bringing together researchers from diverse disciplines—such as algorithms, programming languages, compilers and runtime, machine-learning, architecture, and systems—into this exciting research area.


Grey Ballard, Andrew Lenharth, and Madan Musuvathi

Grey Ballard is currently a Truman Fellow at Sandia National Labs in Livermore, CA. He received his PhD in 2013 from the Computer Science Division (EECS Department) at the University of California Berkeley. He worked in the BeBOP group and Parallel Computing Laboratory under advisor James Demmel. Before coming to Berkeley, he received his BS in math and computer science at Wake Forest University in 2006 and his MA in math at Wake Forest in 2008.

His research interests include numerical linear algebra, high performance computing, and computational science, particularly in developing algorithmic ideas that translate to improved implementations and more efficient software. His work has been recognized with the SIAM Linear Algebra Prize and two conference best paper awards, at SPAA and IPDPS, and he received the C.V. Ramamoorthy Distinguished Research Award at the University of California, Berkeley, for his doctorate work.

Madan Musuvathi is a Senior Researcher in the Research in Software Engineering group at Microsoft Research. His research focus is on parallelism and concurrency and is broadly interested in systems, program analysis, model checking, verification, and theorem proving. His research has resulted in productivity tools for software developers and testers at Microsoft and other companies. He received his Ph.D. from Stanford University in 2004.