Date & Time:
Thursday, March 21, 2013; 12:00 PM
Distributed algorithms become necessary to employ the computational resources needed for solving the large scale optimization problems that arise in areas such as machine learning,computation biology and others. We study a very general distributed setting where the data is distributed over many machines that can communicate with one another over a network that does not have any specialized communication infrastructure. In this setting the role of the network becomes critical in the performance of a distributed algorithm. From a more theoretical standpoint we discuss two questions: 1) How many nodes should we use for a given problem before communication becomes a bottleneck? and 2) How often should the nodes communicate to one another for the communication cost to be worth the transmission? In addition, we discuss some more practical issue that one needs to consider in implementing algorithms that are asynchronous and robust to communication delays.
McGill, Montreal, Canada
Konstantinos I. Tsianos is a PhD candidate at the Department of Electrical and Computer Engineering at McGill University in Montreal, QC, Canada. He received his diploma in electrical and computer engineering from the National Technical University of Athens in 2005 and his Master in Computer Science from Rice University in 2009. His current research interests include Machine Learning, Distributed Algorithms and Optimization.