MATLAB® is the dominant programming language for implementing numerical computations and is widely used for algorithm development, simulation, data reduction, testing, and system evaluation. Many of these computations could benefit from faster execution on a parallel computer. There have been many previous attempts to provide an efficient mechanism for running Matlab programs on parallel computers. pMatlab provides a set of Matlab data structures and functions that implement distributed Matlab arrays. Parallel array programming has proven to be an effective programming style for a wide variety of parallel applications and is consistent with standard Matlab programming style. The primary advantages of distributed array programming are: Message passing is done implicitly; Existing Matlab program can be made parallel with modifications; to a handful of statements.

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  1. 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) ioport
  2. Hudak, David E.; Ludban, Neil; Krishnamurthy, Ashok; Gadepally, Vijay; Samsi, Siddharth; Nehrbass, John: A computational science IDE for HPC systems: design and applications (2009)
  3. Kepner, Jeremy: Parallel MATLAB for multicore and multinode computers. (2009)
  4. Sharma, Gaurav; Martin, Jos: MATLAB(^\circledR): A language for parallel computing (2009)
  5. Sala, Marzio; Spotz, William F.; Heroux, Michael A.: PyTrilinos: High-performance distributed-memory solvers for Python (2008)