MatlabMPI

The true costs of high performance computing are currently dominated by software. Addressing these costs requires shifting to high productivity languages such as Matlab. MatlabMPI is a Matlab implementation of the Message Passing Interface (MPI) standard and allows any Matlab program to exploit multiple processors. MatlabMPI currently implements the basic six functions that are the core of the MPI point-to-point communications standard. The key technical innovation of MatlabMPI is that it implements the widely used MPI ``look and feel'' on top of standard Matlab file I/O, resulting in an extremely compact (~250 lines of code) and ``pure'' implementation which runs anywhere Matlab runs, and on any heterogeneous combination of computers. The performance has been tested on both shared and distributed memory parallel computers (e.g. Sun, SGI, HP, IBM, Linux and MacOSX). MatlabMPI can match the bandwidth of C based MPI at large message sizes. A test image filtering application using MatlabMPI achieved a speedup of ~300 using 304 CPUs and ~15% of the theoretical peak (450 Gigaflops) on an IBM SP2 at the Maui High Performance Computing Center. In addition, this entire parallel benchmark application was implemented in 70 software-lines-of-code, illustrating the high productivity of this approach. MatlabMPI is available for download on the web (www.ll.mit.edu/MatlabMPI).Comment: Download software from http://www.ll.mit.edu/MatlabMPI, 12 pages including 7 color figures; submitted to the Journal of Parallel and Distributed Computin

Efficient Stand-Alone Generalized Inverse Algorithms and Software for Engineering/Sciences Applications: Research and Education

Efficient numerical procedures for finding the generalized (or pseudo) inverse of a general (square/rectangle, symmetrical/unsymmetrical, non-singular/singular, real/complex numbers) matrix and solving systems of Simultaneous Linear Equations (SLE) are formulated and explained. The developed procedures and its associated computer software (under MATLAB computer environment) have been based on special Cholesky factorization schemes (for a singular matrix), the generalized inverse of the matrix product, and were further enhanced by the Domain Decomposition (DD) formulation. Test matrices from different fields of applications have been chosen, tested and compared with other existing algorithms. The results of the numerical tests have indicated that the developed procedures are far more efficient than existing algorithms. Furthermore, an educational version of the generalized inverse algorithms and software for solving SLE has also been developed to run any FORTRAN and/or \u27C\u27 programs over the web. This developed technology and software is freely available and can run on any device with internet connectivity and browser capability

MATLAB*P 2.0: A unified parallel MATLAB

MATLAB is one of the most widely used mathematical computing environments in technical computing. It is an interactive environment that provides high performance computational routines and an easy-to-use, C-like scripting language. Mathworks, the company that develops MATLAB, currently does not provide a version of MATLAB that can utilize parallel computing. This has led to academic and commercial efforts outside Mathworks to build a parallel MATLAB, using a variety of approaches. In a survey, 26 parallel MATLAB projects utilizing four different approaches have been identified. MATLAB*P is one of the 26 projects. It makes use of the backend support approach. This approach provides parallelism to MATLAB programs by relaying MATLAB commands to a parallel backend. The main difference between MATLAB*P and other projects that make use of the same approach is in its focus. MATLAB*P aims to provide a user-friendly supercomputing environment in which parallelism is achieved transparently through the use of objected oriented programming features in MATLAB. One advantage of this approach is that existing scripts can be run in parallel with no or minimal modifications. This paper describes MATLAB*P 2.0, which is a complete rewrite of MATLAB*P. This new version brings together the backend support approach with embarrassingly parallel and MPI approaches to provide the first complete parallel MATLAB framework.Singapore-MIT Alliance (SMA

Lessons Learned from a Decade of Providing Interactive, On-Demand High Performance Computing to Scientists and Engineers

For decades, the use of HPC systems was limited to those in the physical sciences who had mastered their domain in conjunction with a deep understanding of HPC architectures and algorithms. During these same decades, consumer computing device advances produced tablets and smartphones that allow millions of children to interactively develop and share code projects across the globe. As the HPC community faces the challenges associated with guiding researchers from disciplines using high productivity interactive tools to effective use of HPC systems, it seems appropriate to revisit the assumptions surrounding the necessary skills required for access to large computational systems. For over a decade, MIT Lincoln Laboratory has been supporting interactive, on-demand high performance computing by seamlessly integrating familiar high productivity tools to provide users with an increased number of design turns, rapid prototyping capability, and faster time to insight. In this paper, we discuss the lessons learned while supporting interactive, on-demand high performance computing from the perspectives of the users and the team supporting the users and the system. Building on these lessons, we present an overview of current needs and the technical solutions we are building to lower the barrier to entry for new users from the humanities, social, and biological sciences.Comment: 15 pages, 3 figures, First Workshop on Interactive High Performance Computing (WIHPC) 2018 held in conjunction with ISC High Performance 2018 in Frankfurt, German