Parallel Processing Research In The Former Soviet Union

The technique of randomization has been employed to solve numerous prob lems of computing both sequentially and in parallel. Examples of randomized algorithms that are asymptotically better than their deterministic counterparts in solving various fundamental problems abound. Randomized algorithms have the advantages of simplicity and better performance both in theory and often in practice. This book is a collection of articles written by renowned experts in the area of randomized parallel computing. A brief introduction to randomized algorithms In the aflalysis of algorithms, at least three different measures of performance can be used: the best case, the worst case, and the average case. Often, the average case run time of an algorithm is much smaller than the worst case. 2 For instance, the worst case run time of Hoare's quicksort is O(n ), whereas its average case run time is only O( n log n). The average case analysis is conducted with an assumption on the input space. The assumption made to arrive at the O( n log n) average run time for quicksort is that each input permutation is equally likely. Clearly, any average case analysis is only as good as how valid the assumption made on the input space is. Randomized algorithms achieve superior performances without making any assumptions on the inputs by making coin flips within the algorithm. Any analysis done of randomized algorithms will be valid for all p0:.sible inputs.

Computing and information and communications technology (ICT) has dramatically changed how we work and live, has had profound effects on nearly every sector of society, has transformed whole industries, and is a key component of U.S. global leadership. A fundamental driver of advances in computing and ICT has been the fact that the single-processor performance has, until recently, been steadily and dramatically increasing year over years, based on a combination of architectural techniques, semiconductor advances, and software improvements. Users, developers, and innovators were able to depend on those increases, translating that performance into numerous technological innovations and creating successive generations of ever more rich and diverse products, software services, and applications that had profound effects across all sectors of society. However, we can no longer depend on those extraordinary advances in single-processor performance continuing. This slowdown in the growth of single-processor computing performance has its roots in fundamental physics and engineering constraints-multiple technological barriers have converged to pose deep research challenges, and the consequences of this shift are deep and profound for computing and for the sectors of the economy that depend on and assume, implicitly or explicitly, ever-increasing performance. From a technology standpoint, these challenges have led to heterogeneous multicore chips and a shift to alternate innovation axes that include, but are not limited to, improving chip performance, mobile devices, and cloud services. As these technical shifts reshape the computing industry, with global consequences, the United States must be prepared to exploit new opportunities and to deal with technical challenges. The New Global Ecosystem in Advanced Computing: Implications for U.S. Competitiveness and National Security outlines the technical challenges, describe the global research landscape, and explore implications for competition and national security.

Since its first volume in 1960, Advances in Computers has presented detailed coverage of innovations in hardware and software and in computer theory, design, and applications. It has also provided contributorswith a medium in which they can examine their subjects in greater depth and breadth than that allowed by standard journal articles. As a result, many articles have become standard references that continue to be of significant, lasting value despite the rapid growth taking place in the field.