Scheduling DAGs opportunistically: The dream and the reality circa 2016
ARNOLD L. ROSENBERG is a Research Professor in the Computer Science Department at Northeastern University; he also holds the rank of Distinguished University Professor Emeritus in the Computer Science Department at the University of Massachusetts Amherst. Prior to joining UMass, Rosenberg was a Professor of Computer Science at Duke University from 1981 to 1986, and a Research Staff Member at the IBM Watson Research Center from 1965 to 1981. He has held visiting positions at Yale University and the University of Toronto. He was a Lady Davis Visiting Professor at the Technion (Israel Institute of Technology) in 1994, and a Fulbright Senior Research Scholar at the University of Paris-South in 2000. Rosenberg’s research focuses on developing algorithmic models and techniques to exploit the new modalities of “collaborative computing” (wherein multiple computers cooperate to solve a computational problem) that result from emerging computing technologies. Rosenberg is the author or coauthor of roughly 180 technical papers on these and other topics in theoretical computer science and discrete mathematics. He is the coauthor of the research book “Graph Separators, with Applications” and the author of the textbook “The Pillars of Computation Theory: State, Encoding, Nondeterminism”; additionally, he has served as coeditor of several books. Dr. Rosenberg is a Fellow of the ACM, a Fellow of the IEEE, and a Golden Core member of the IEEE Computer Society. Rosenberg received an A.B. in mathematics at Harvard College and an A.M. and Ph.D. in applied mathematics at Harvard University. More details are available at http://www.cs.umass.edu/~rsnbrg/.
A broad-brush tour of a platform-oblivious approach to scheduling DAG-structured computations on platforms whose resources can change dynamically, both in availability and efficiency.
The main focus is on the IC-scheduling and Area-oriented scheduling paradigms—the motivation, the dream, the implementation, and initial work on evaluation.