Practical Parallel Algorithms for Dynamic Data Redistribution, Median Finding, and Selection

A common statistical problem is that of nding the median element in a set of data. This paper presents a fastand portable parallel algorithm for nding the median given a set of elements distributed across a parallel machine. In fact, our algorithm solves the general selection problem that requires the determination of the element of rank i, for an arbitrarily given integer i. Practical algorithms needed by our selection algorithm for the dynamic redistribution of data are also discussed. Our general framework is a single-address space, distributed memory programming model that is enhanced by a set of communication primitives. We use e cient techniques for distributing, coalescing, and load balancing data as well as e cient combinations of task and data parallelism. The algorithms have been coded in Split-C and run on a variety of platforms, including the Thinking Machines CM5, IBM SP-1 and SP-2, Cray Research T3D, Meiko Scienti c CS-2, Intel Paragon, and workstation clusters. Our expe...

• Type: Conference
• Year: 1996
• Where: IPPS
• Authors: David A. Bader, Joseph JáJá
• Keywords: Algorithms, Communication Primitives, Distributed And Parallel Computing, IPPS 1996, Portable Parallel Algorithm