pMapper: Automatic Mapping of Parallel Matlab Programs. Algorithm implementation efficiency is key to delivering high-performance computing capabilities to demanding, high throughput DoD signal and image processing applications and simulations. Significant progress has been made in compiler optimization of serial programs, but many applications require parallel processing, which brings with it the difficult task of determining efficient mappings of algorithms to multiprocessor computers. The pMapper infrastructure addresses the problem of performance optimization of multistage MATLAB applications on parallel architectures. pMapper is an automatic performance tuning library written as a layer on top of pMatlab. pMatlab is a parallel Matlab toolbox that provides MATLAB users with global array semantics. While pMatlab abstracts the message-passing interface, the responsibility of generating maps for numerical arrays still falls on the user. A processor map for a numerical array is defined as an assignment of blocks of data to processing elements. Choosing the best mapping for a set of numerical arrays in a program is a nontrivial task that requires significant knowledge of programming languages, parallel computing, and processor architecture. pMapper automates the task of map generation, increasing the ease of programming and productivity. In addition to automating the mapping of parallel Matlab programs, pMapper could be used as a mapping tool for embedded systems. This paper addresses the design details of the pMapper infrastructure and presents preliminary results.
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- Hendry, Gilbert; Robinson, Eric; Gleyzer, Vitaliy; Chan, Johnnie; Carloni, Luca P.; Bliss, Nadya; Bergman, Keren: Time-division-multiplexed arbitration in silicon nanophotonic networks-on-chip for high-performance chip multiprocessors (2011)
- O’Reilly, Una-May; Robinson, Eric; Mohindra, Sanjeev; Mullen, Julie; Bliss, Nadya: Hogs and slackers: Using operations balance in a genetic algorithm to optimize sparse algebra computation on distributed architectures (2010)