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PRO: A Model for the Design and Analysis of Efficient and Scalable Parallel Algorithms
Abstract : We present a new parallel computation model called the Parallel Resource-Optimal computation model. PRO is a framework being proposed to enable the design of efficient and scalable parallel algorithms in an architecture-independent manner, and to simplify the \emph{analysis} of such algorithms. A focus on three key features distinguishes PRO from existing parallel computation models. First, the design and analysis of a parallel algorithm in the PRO model is performed relative to the time and space complexity of a specific sequential algorithm. Second, a PRO algorithm is required to be both time- and space-optimal relative to the reference sequential algorithm. Third, the quality of a PRO algorithm is measured by the maximum number of processors that can be employed while optimality is maintained. Inspired by the Bulk Synchronous Parallel model, an algorithm in the PRO model is organized as a sequence of \emph{supersteps}. Each superstep consists of distinct computation and communication phases, but the supersteps are not required to be separated by synchronization barriers. Both computation and communication costs are accounted for in the runtime analysis of a PRO algorithm. Experimental results on parallel algorithms designed using the PRO model---and implemented using its accompanying programming environment SSCRAP---demonstrate that the model indeed delivers efficient and scalable implementations on a wide range of platforms.
Cited literature [28 references]
https://hal.inria.fr/inria-00000899
Contributor : Jens Gustedt Connect in order to contact the contributor
Submitted on : Thursday, October 5, 2006 - 12:04:34 PM
Last modification on : Friday, February 26, 2021 - 3:28:02 PM
Long-term archiving on: : Friday, November 25, 2016 - 12:12:49 PM
Contributor : Jens Gustedt Connect in order to contact the contributor
Submitted on : Thursday, October 5, 2006 - 12:04:34 PM
Last modification on : Friday, February 26, 2021 - 3:28:02 PM
Long-term archiving on: : Friday, November 25, 2016 - 12:12:49 PM