DateOctober 3, 2008
TopicMapReduce at Google
Abstract: Google processed over 400 PB of data using the MapReduce system in September of 2007 alone. The MapReduce model enables rapid expression of a wide range of computations. Its implementation masks failures and scales to tens of thousands of cores. As a result, the system is the foundation of high-performance computing at Google. The talk will overview the MapReduce system.

Bio: Grzegorz Malewicz received the BA degrees in computer science and in applied mathematics in 1996 and 1998, respectively, and the MS degree in computer science in 1998, all from the University of Warsaw. He received the PhD degree in computer science from the University of Connecticut in 2003. He is an engineer at Google. He has had internships at the AT&T Shannon Laboratory (summer 2001) and Microsoft Corp. (summer 2000 and fall 2001). He visited the Supercomputing Technologies Group at the Massachusetts Institute of Technology (academic year 2002-2003), and was a visiting scientist at the University of Massachusetts, Amherst (summer 2004) and Argonne National Laboratory (summer 2005). He was an assistant professor at the University of Alabama, where he taught computer science from 2003 until 2005. His research focuses on high-performance parallel and distributed computing, experimental and theoretical algorithmics, combinatorial optimization, and scheduling. His research appears in top journals and conferences and includes a singly authored SIAM Journal on Computing paper that solves a decade-old problem in distributed computing.