Welcometotheproceedingsofthe2ndInternationalSymposiumonParalleland Distributed Processing and Applications (ISPA2004) which was held in Hong Kong, China, 13–15 December, 2004. With the advance of computer networks and hardware technology, parallel and distributed processing has become a key technology which plays an imp- tant part in determining future research and development activities in many academic and industrial branches. It provides a means to solve computati- ally intensive problems by improving processing speed. It is also the only - ableapproachtobuildinghighlyreliableandinherentlydistributedapplications. ISPA2004 provided a forum for scientists and engineers in academia and ind- try to exchange and discuss their experiences, new ideas, research results, and applications about all aspects of parallel and distributed computing. There was a very large number of paper submissions (361) from 26 countries and regions, including not only Asia and the Paci?c, but also Europe and North America. All submissions were reviewed by at least three program or technical committee members or external reviewers. It was extremely di?cult to select the presentations for the conference because there were so many excellent and interesting submissions. In order to allocate as many papers as possible and keep the high quality of the conference, we ?nally decided to accept 78 regular papers and 38 short papers for oral technical presentations. We believe that all of these papers and topics not only provide novel ideas, new results, work in progress and state-of-the-art techniques in this ?eld, but also stimulate the future research activities in the area of parallel and distributed computing with applications.

Thirty-nine papers and 32 posters from the January 1996 workshop assess the current status of parallel computing, present recent developments, and identify major trends. More specifically, they address technical issues connected with numerical algorithms, communications, programming tools, parallel

Proceedings -- Parallel Computing.

The use of parallel programming and architectures is essential for simulating and solving problems in modern computational practice. There has been rapid progress in microprocessor architecture, interconnection technology and software devel- ment, which are in?uencing directly the rapid growth of parallel and distributed computing. However, in order to make these bene?ts usable in practice, this dev- opment must be accompanied by progress in the design, analysis and application aspects of parallel algorithms. In particular, new approaches from parallel num- ics are important for solving complex computational problems on parallel and/or distributed systems. The contributions to this book are focused on topics most concerned in the trends of today’s parallel computing. These range from parallel algorithmics, progr- ming, tools, network computing to future parallel computing. Particular attention is paid to parallel numerics: linear algebra, differential equations, numerical integ- tion, number theory and their applications in computer simulations, which together form the kernel of the monograph. We expect that the book will be of interest to scientists working on parallel computing, doctoral students, teachers, engineers and mathematicians dealing with numerical applications and computer simulations of natural phenomena.

This book presents the proceedings of the First International EURO-PAR Conference on Parallel Processing, held in Stockholm, Sweden in August 1995. EURO-PAR is the merger of the former PARLE and CONPAR-VAPP conference series; the aim of this merger is to create the premier annual scientific conference on parallel processing in Europe. The book presents 50 full revised research papers and 11 posters selected from a total of 196 submissions on the basis of 582 reviews. The scope of the contributions spans the full spectrum of parallel processing ranging from theory over design to application; thus the volume is a "must" for anybody interested in the scientific aspects of parallel processing or its advanced applications.

Building upon the wide-ranging success of the first edition, Parallel Scientific Computation presents a single unified approach to using a range of parallel computers, from a small desktop computer to a massively parallel computer. The author explains how to use the bulk synchronous parallel (BSP) model to design and implement parallel algorithms in the areas of scientific computing and big data, and provides a full treatment of core problems in these areas, starting from a high-level problem description, via a sequential solution algorithm to a parallel solution algorithm and an actual parallel program written in BSPlib. Every chapter of the book contains a theoretical section and a practical section presenting a parallel program and numerical experiments on a modern parallel computer to put the theoretical predictions and cost analysis to the test. Every chapter also presents extensive bibliographical notes with additional discussions and pointers to relevant literature, and numerous exercises which are suitable as graduate student projects. The second edition provides new material relevant for big-data science such as sorting and graph algorithms, and it provides a BSP approach towards new hardware developments such as hierarchical architectures with both shared and distributed memory. A single, simple hybrid BSP system suffices to handle both types of parallelism efficiently, and there is no need to master two systems, as often happens in alternative approaches. Furthermore, the second edition brings all algorithms used up to date, and it includes new material on high-performance linear system solving by LU decomposition, and improved data partitioning for sparse matrix computations. The book is accompanied by a software package BSPedupack, freely available online from the author's homepage, which contains all programs of the book and a set of test driver programs. This package written in C can be run using modern BSPlib implementations such as MulticoreBSP for C or BSPonMPI.