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Massively parallel algorithms for private-cache chip multiprocessors (2008)
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BibTeX
@MISC{Arge08massivelyparallel,
author = {Lars Arge and Michael T. Goodrich and Michael Nelson and Nodari Sitchinava},
title = {Massively parallel algorithms for private-cache chip multiprocessors },
year = {2008}
}
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Abstract
In this paper, we study massively parallel algorithms for private-cache chip multiprocessors (CMPs), focusing on methods for foundational problems that can scale to hundreds or even thousands of cores. By focusing on private-cache CMPs, we show that we can design efficient algorithms that need no additional assumptions about the way that cores are interconnected, for we assume that all inter-processor communication occurs through the memory hierarchy. We study several fundamental problems, including prefix sums, selection, and sorting, which often form the building blocks of other parallel algorithms. Indeed, we present two sorting algorithms, a distribution sort and a mergesort. All algorithms in the paper are asymptotically optimal in terms of the parallel cache accesses and space complexity under reasonable assumptions about the relationships between the number of processors, the size of memory, and the size of cache blocks. In addition, we study sorting lower bounds in a computational model, which we call the parallel external-memory (PEM) model, that formalizes the essential properties of our algorithms for private-cache chip multiprocessors. [Regular paper submission to SPAA 2008, which may be considered for a normal track or the special track on multicore systems.
Keyphrases
private-cache chip multiprocessor parallel algorithm cache block private-cache cmps foundational problem special track parallel external-memory building block multicore system prefix sum efficient algorithm parallel cache access additional assumption essential property normal track distribution sort memory hierarchy computational model reasonable assumption regular paper submission several fundamental problem inter-processor communication space complexity
