Content Description #Includes bibliographical references and index.

This book constitutes the refereed proceedings of the Third International Symposium on Computing in Object-Oriented Parallel Environments, ISCOPE 99, held in San Francisco, CA, USA in December 1999. The 14 revised full papers presented together with six short papers were selected from 41 submissions. The papers are devoted to compilers and optimization techniques, new application fields, components and metacomputing, numerical frameworks, generic programming and skeletons, application-specific frameworks, and runtime systems and techniques.

This volume contains the Proceedings of the International Symposium on C- puting in Object-Oriented Parallel Environments (ISCOPE ’98), held at Santa 1 Fe, New Mexico, USA on December 8{11, 1998. ISCOPE is in its second year, and continues to grow both in attendance and in the diversity of the subjects covered. ISCOPE’97 and its predecessor conferences focused more narrowly on scienti c computing in the high-performance arena. ISCOPE ’98 retains this emphasis, but has broadened to include discrete-event simulation, mobile c- puting, and web-based metacomputing. The ISCOPE ’98 Program Committee received 39 submissions, and acc- ted 10 (26%) as Regular Papers, based on their excellent content, maturity of development, and likelihood for widespread interest. These 10 are divided into three technical categories. Applications: The rst paper describes an approach to simulating advanced nuclear power reactor designs that incorporates multiple local solution - thods and a natural extension to parallel execution. The second paper disc- ses a Time Warp simulation kernel that is highly con gurable and portable. The third gives an account of the development of software for simulating high-intensity charged particle beams in linear particle accelerators, based on the POOMA framework, that shows performance considerably better than an HPF version, along with good parallel speedup.

Parallel processing has been an enabling technology in scientific computing for more than 20 years. This book is the first in-depth discussion of parallel computing in 10 years; it reflects the mix of topics that mathematicians, computer scientists, and computational scientists focus on to make parallel processing effective for scientific problems. Presently, the impact of parallel processing on scientific computing varies greatly across disciplines, but it plays a vital role in most problem domains and is absolutely essential in many of them. Parallel Processing for Scientific Computing is divided into four parts: The first concerns performance modeling, analysis, and optimization; the second focuses on parallel algorithms and software for an array of problems common to many modeling and simulation applications; the third emphasizes tools and environments that can ease and enhance the process of application development; and the fourth provides a sampling of applications that require parallel computing for scaling to solve larger and realistic models that can advance science and engineering.

Since the dawn of computing, the quest for a better understanding of Nature has been a driving force for technological development. Groundbreaking achievements by great scientists have paved the way from the abacus to the supercomputing power of today. When trying to replicate Nature in the computer’s silicon test tube, there is need for precise and computable process descriptions. The scienti?c ?elds of Ma- ematics and Physics provide a powerful vehicle for such descriptions in terms of Partial Differential Equations (PDEs). Formulated as such equations, physical laws can become subject to computational and analytical studies. In the computational setting, the equations can be discreti ed for ef?cient solution on a computer, leading to valuable tools for simulation of natural and man-made processes. Numerical so- tion of PDE-based mathematical models has been an important research topic over centuries, and will remain so for centuries to come. In the context of computer-based simulations, the quality of the computed results is directly connected to the model’s complexity and the number of data points used for the computations. Therefore, computational scientists tend to ?ll even the largest and most powerful computers they can get access to, either by increasing the si e of the data sets, or by introducing new model terms that make the simulations more realistic, or a combination of both. Today, many important simulation problems can not be solved by one single computer, but calls for parallel computing.

The 17th International Workshop on Languages and Compilers for High Performance Computing was hosted by Purdue University in September 2004 on Purdue campus in West Lafayette, Indiana, USA.