Techniques and resources for storm-scale numerical weather prediction

The topics discussed include the following: multiscale application of the 5th-generation PSU/NCAR mesoscale model, the coupling of nonhydrostatic atmospheric and hydrostatic ocean models for air-sea interaction studies; a numerical simulation of cloud formation over complex topography; adaptive grid simulations of convection; an unstructured grid, nonhydrostatic meso/cloud scale model; efficient mesoscale modeling for multiple scales using variable resolution; initialization of cloud-scale models with Doppler radar data; and making effective use of future computing architectures, networks, and visualization software

An Application Perspective on High-Performance Computing and Communications

We review possible and probable industrial applications of HPCC focusing on the software and hardware issues. Thirty-three separate categories are illustrated by detailed descriptions of five areas -- computational chemistry; Monte Carlo methods from physics to economics; manufacturing; and computational fluid dynamics; command and control; or crisis management; and multimedia services to client computers and settop boxes. The hardware varies from tightly-coupled parallel supercomputers to heterogeneous distributed systems. The software models span HPF and data parallelism, to distributed information systems and object/data flow parallelism on the Web. We find that in each case, it is reasonably clear that HPCC works in principle, and postulate that this knowledge can be used in a new generation of software infrastructure based on the WebWindows approach, and discussed in an accompanying paper

Global Grids and Software Toolkits: A Study of Four Grid Middleware Technologies

Grid is an infrastructure that involves the integrated and collaborative use of computers, networks, databases and scientific instruments owned and managed by multiple organizations. Grid applications often involve large amounts of data and/or computing resources that require secure resource sharing across organizational boundaries. This makes Grid application management and deployment a complex undertaking. Grid middlewares provide users with seamless computing ability and uniform access to resources in the heterogeneous Grid environment. Several software toolkits and systems have been developed, most of which are results of academic research projects, all over the world. This chapter will focus on four of these middlewares--UNICORE, Globus, Legion and Gridbus. It also presents our implementation of a resource broker for UNICORE as this functionality was not supported in it. A comparison of these systems on the basis of the architecture, implementation model and several other features is included.Comment: 19 pages, 10 figure

The Nimrod computational workbench: a case study in desktop metacomputing

The coordinated use of geographically distributed computers, or metacomputing, can in principle provide more accessible and cost- effective supercomputing than conventional high-performance systems. However, we lack evidence that metacomputing systems can be made easily usable, or that there exist large numbers of applications able to exploit metacomputing resources. In this paper, we present work that addresses both these concerns. The basis for this work is a system called Nimrod that provides a desktop problem-solving environment for parametric experiments. We describe how Nimrod has been extended to support the scheduling of computational resources located in a wide-area environment, and report on an experiment in which Nimrod was used to schedule a large parametric study across the Australian Internet. The experiment provided both new scientific results and insights into Nimrod capabilities. We relate the results of this experiment to lessons learned from the I-WAY distributed computing experiment, and draw conclusions as to how Nimrod and I-WAY- like computing environments should be developed to support desktop metacomputing

SNAP, Crackle, WebWindows!

We elaborate the SNAP---Scalable (ATM) Network and (PC) Platforms---view of computing in the year 2000. The World Wide Web will continue its rapid evolution, and in the future, applications will not be written for Windows NT/95 or UNIX, but rather for WebWindows with interfaces defined by the standards of Web servers and clients. This universal environment will support WebTop productivity tools, such as WebWord, WebLotus123, and WebNotes built in modular dynamic fashion, and undermining the business model for large software companies. We define a layered WebWindows software architecture in which applications are built on top of multi-use services. We discuss examples including business enterprise systems (IntraNets), health care, financial services and education. HPCC is implicit throughout this discussion for there is no larger parallel system than the World Wide metacomputer. We suggest building the MPP programming environment in terms of pervasive sustainable WebWindows technologies. In particular, WebFlow will support naturally dataflow integrating data and compute intensive applications on distributed heterogeneous systems

Integrating a parallel computer and a heterogeneous workstation cluster into a metacomputer system

Two types of parallel computers commonly used or solving large scientific problems are clusters of workstations and distributed-memory multicomputers. Each system has strengths and weaknesses for this task. Workstation clusters have a high performance to cost ratio and the advantage of the latest processors. Workstations are commonly under-utilized and can provide an inexpensive source of CPU cycles. However, clusters of workstations cannot compete with the performance of a dedicated supercomputer. This research proposes that creating a metacomputer combining different types of parallel computers can provide some of the advantages of each separate system. Specifically, I have inte­grated a distributed-memory parallel computer (the MEIKO CS-2) with a heterogeneous cluster of workstations. The integrated system uses the CHARM parallel-programming environment to provide for machine-independence and ease of programming in this heterogeneous environment. The availability of processing capacity limits the size and complexity of the types o[ problems that can be efficiently solved. By creating a meta.computer the amount of processing capacity can be increased at relatively low costs. The low cost of the system and the fact that it is easily reconfigurable make it a good choice for solving large-scale Grand Challenge type scientific problems