Field Of High Performance Computing Research Articles

High performance horizons [high performance computing

The field of high performance computing is undergoing a lot of changes. At the low end, supercomputers are becoming ever more affordable while, at the high end, it is becoming increasingly difficult to exploit the full potential of the most advanced hardware, given the sheer scale of the software challenges involved. There are also signs that the boundary between the worlds of the supercomputer and the desktop - traditionally very distinct - is starting to blur. Supercomputers continue to scale, with performance advancing at a rate that exceeds the rate of chip-level improvements, as characterized by Moore's law. These advances largely stem from the dramatic growth in cluster technology, which allows many processors to be closely coupled to solve large computational problems.

High-performance computing: clusters, constellations, MPPs, and future directions

In a recent paper, Gordon Bell and Jim Gray (2002) put forth a view of the past, present, and future of high-performance computing (HPC) that is both insightful and thought provoking. Identifying key trends with a grace and candor rarely encountered in a single work, the authors describe an evolutionary past drawn from their vast experience and project an enticing and compelling vision of HPC's future. Yet, the underlying assumptions implicit in their treatment, particularly those related to terminology and dominant trends, conflict with our own experience, common practices, and shared view of HPCs future directions. Taken from our vantage points of the Top500 the Lawrence Berkeley National Laboratory NERSC computer center, Beowulf-class computing, and research in petaflops-scale computing architectures, we offer an alternate perspective on several key issues in the form of a constructive counterpoint. One objective of this article is to restore the strength and value of the term cluster by degeneralizing its applicability to a restricted subset of parallel computers. We'll further consider this class in conjunction with its complementing terms constellation, Beowulf class, and massively parallel processing systems (MPPs), based on the classification used by the Top500 list, which has tracked the HPC field for more than a decade.

Application of Parallelized SOR Method to Electromagnetic Field Analysis of Superconductors

PC cluster systems are becoming popular in the field of high-performance computing. The authors have been studying the electromagnetic field analysis of high T/sub c/ superconductors (HTS) by the finite element method (FEM) for AC loss estimations. Superconductors are highly nonlinear electromagnetic media, and considerable computation time is required to calculate the temporal evolution of the electromagnetic field distribution in superconductors. In this study, parallel computing techniques were applied to the electromagnetic field analysis of HTSs by the finite element method. In one of the FEM codes used by the authors, the successive over-relaxation (SOR) method, was used to solve a system of equations. This part was parallelized using the multicolor SOR method, and it helped in reducing computation time. Data are explicitly passed between the processor elements (PEs) through an MPI. First, the HTS model was analyzed under one operating condition, using the code implemented multicolor SOR method, using four PEs, and the parallelization efficiency was confirmed. Next, the generality of the efficiency of the multicolor SOR method in our numerical simulation was examined under several operating conditions, using different models.

Interactive debugging and performance analysis of massively parallel applications

In the field of high performance computing, massively parallel processing systems (MPPs) get more and more important. A rising number of complex applications is parallelized for execution on these machines. Still a significant portion of the time needed for parallelization is spent for the process of debugging and performance tuning. A main reason for this fact is the absence of adequate tools supporting this phase of program development. In this article, we present a novel tool environment, consisting of a parallel debugger (DETOP), a performance analyzer (PATOP), and a common monitoring system for PowerPC-based parallel computers. The environment specifically addresses the topics of scalability, usability for dynamic, multithreaded programming models, minimal intrusion, debugging and tuning methodology and comfortable user interfaces. We derive requirements for tools monitoring the runtime behavior of parallel programs, present the concepts used to meet these requirements in our tool environment, and describe its implementation and its usage. DETOP is based on the event-action paradigm and supports both data parallel codes and programs based on functional decomposition. Special features are provided for applications that dynamically create new threads or consist of multiple executables. PATOP supports a systematic search for performance bottlenecks in massively parallel applications using the concept of attributed measurements and distributed evaluation. Both tools are based on a common, distributed on-line monitoring system providing the necessary runtime information.

Science and industry in HPCN

The interplay of science and industry in the development and exploitation of a mutually beneficial HPCN policy is a non-issue. Everyone, I believe, is willing to subscribe to this view. Just a tad harder is to outline in detail the precise lines along which such a partnership should evolve and what the advantages are for both partners. CRS4 is a research institute based in Cagliari (Sardegna) with a vocation for mathematical modeling and numerical simulation. Research at CRS4 means applied research in close contact with industry. It would seem that we are occupying a privileged position to entertain thoughts and develop theories and models on the interplay of science and industry in HPCN. Nonetheless our experience is too time-limited to allow me to make any general conclusion on the subject. In my paper I will first speculate on what kind of development we will see in the coming years in the field of High Performance Computing and Networks. I will describe the interaction between CRS4 and industry, and extrapolate from this some of the needs of industry from HPCN. I will close by describing our view of the interaction between a HPCN and CRS4.