Parallel Algorithms

Parallel computing has undergone a stunning evolution, with high points (e.g., being able to solve many of the grand-challenge computational problems outlined in the 80’s) and low points (e.g., the demise of countless parallel computer vendors). Today, parallel computing is omnipresent across a large spectrum of computing platforms. At the “microscopic” level, processor cores have used multiple functional units in concurrent and pipelined fashion for years and multiple-core chips are now commonplace with a trend toward rapidly increasing numbers of cores per chip. At the “macroscopic” level, one can now build clusters of hundreds to thousands of individual (multi-core) computers. Such distributed-memory systems have become mainstream and affordable in the form of commodity clusters. Furthermore, advances in network technology and infrastructures have made it possible to aggregate parallel computing platforms across wide-area networks in so-called “grids”.