Brian J. d'Auriol, Ph.D.

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Brian J. d'Auriol, Solving the Problem of Programming High Performance Computers in Parallel Environments, Departmental Seminar, Nov. 3, 2004, Department of Computer Science, University of Texas at El Paso, TX, USA.

The many advances in parallel and distributed computing and related technologies have propelled high performance computing to its present state of scientific and commercial success. Yet, despite such success, programming high performance computers remains difficult, time consuming and error prone. Exacerbating the situation are the many existing legacy programs together with those written by programmers with widely varying backgrounds and experience. Understanding high performance computing programs helps to: identify program fragments that could be optimized; understand the overall algorithms or techniques used; and, enhance, maintain and integrate existing programs into new operational environments. This research proposes developments aimed at improving the programming environment for high performance computing programs. Such improvements could have significant benefit since high performance computing broadly includes MPPs, clusters, and grids and applications span collaborations to data intensive computing. This talk characterizes the essential nature of programming high performance computers as one of conceptual specification and conceptual understanding. Conceptual domains inherent in parallel programming models are explored by examples drawn from Occam 2 programming on transputers and C/MPI programming on an Itanium 2 cluster. A visual/visualization approach based on semantic relationships is proposed. This approach has been developed into the Advanced Relation Model for Program Visualization (ARM 4 PV). Extensions suitable for parallel computing environments are described. Visualizations of several programs, including a C/MPI program, complete the talk.

Last Updated: August 1, 2007