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Distributed-memory multiprocessing systems (DMS), such as Intel's hypercubes, the Paragon, Thinking Machine's CM-5, and the Meiko Computing Surface, have rapidly gained user acceptance and promise to deliver the computing power required to solve the grand challenge problems of Science and Engineering. These machines are relatively inexpensive to build, and are potentially scalable to large numbers of processors. However, they are difficult to program: the non-uniformity of the memory which makes local accesses much faster than the transfer of non-local data via message-passing operations implies that the locality of algorithms must be exploited in order to achieve acceptable performance. The management of data, with the twin goals of both spreading the computational workload and minimizing the delays caused when a processor has to wait for non-local data, becomes of paramount importance. When a code is parallelized by hand, the programmer must distribute the program's work and data to the processors which will execute it. One of the common approaches to do so makes use of the regularity of most numerical computations. This is the so-called Single Program Multiple Data (SPMD) or data parallel model of computation. With this method, the data arrays in the original program are each distributed to the processors, establishing an ownership relation, and computations defining a data item are performed by the processors owning the data.
Compiling for parallelism is a longstanding topic of compiler research. This book describes the fundamental principles of compiling "regular" numerical programs for parallelism. We begin with an explanation of analyses that allow a compiler to understand the interaction of data reads and writes in different statements and loop iterations during program execution. These analyses include dependence analysis, use-def analysis and pointer analysis. Next, we describe how the results of these analyses are used to enable transformations that make loops more amenable to parallelization, and discuss transformations that expose parallelism to target shared memory multicore and vector processors. We then discuss some problems that arise when parallelizing programs for execution on distributed memory machines. Finally, we conclude with an overview of solving Diophantine equations and suggestions for further readings in the topics of this book to enable the interested reader to delve deeper into the field. Table of Contents: Introduction and overview / Dependence analysis, dependence graphs and alias analysis / Program parallelization / Transformations to modify and eliminate dependences / Transformation of iterative and recursive constructs / Compiling for distributed memory machines / Solving Diophantine equations / A guide to further reading
I Unidimensional Problems.- 1 Scheduling DAGs without Communications.- 2 Scheduling DAGs with Communications.- 3 Cyclic Scheduling.- II Multidimensional Problems.- 4 Systems of Uniform Recurrence Equations.- 5 Parallelism Detection in Nested Loops.
Readership This book is devoted to the study of compiler transformations that are needed to expose the parallelism hiddenin a program. This book is notan introductory book to parallel processing, nor is it an introductory book to parallelizing compilers. Weassume thatreaders are familiar withthebooks High Performance Compilers for Parallel Computingby Wolfe [121] and Super­ compilers for Parallel and Vector Computers by Zima and Chapman [125], and that they want to know more about scheduling transformations. In this book we describe both task graph scheduling and loop nest scheduling. Taskgraphschedulingaims atexecuting tasks linked by prece­ dence constraints; it is a run-time activity. Loop nest scheduling aims at ex­ ecutingstatementinstances linked bydata dependences;it is a compile-time activity. We are mostly interested in loop nestscheduling,butwe also deal with task graph scheduling for two main reasons: (i) Beautiful algorithms and heuristics have been reported in the literature recently; and (ii) Several graphscheduling, like list scheduling, are the basis techniques used in task ofthe loop transformations implemented in loop nest scheduling. As for loop nest scheduling our goal is to capture in a single place the fantastic developments of the last decade or so. Dozens of loop trans­ formations have been introduced (loop interchange, skewing, fusion, dis­ tribution, etc.) before a unifying theory emerged. The theory builds upon the pioneering papers of Karp, Miller, and Winograd [65] and of Lam­ port [75], and it relies on sophisticated mathematical tools (unimodular transformations, parametric integer linear programming, Hermite decom­ position, Smithdecomposition, etc.).
The automatic generation of parallel code from high level sequential description is of key importance to the wide spread use of high performance machine architectures. This text considers (in detail) the theory and practical realization of automatic mapping of algorithms generated from systems of uniform recurrence equations (do-lccps) onto fixed size architectures with defined communication primitives. Experimental results of the mapping scheme and its implementation are given.
Uncertainty in Computational Intelligence-Based Decision-Making focuses on techniques for reasoning and decision-making under uncertainty that are used to solve issues in artificial intelligence (AI). It covers a wide range of subjects, including knowledge acquisition and automated model construction, pattern recognition, machine learning, natural language processing, decision analysis, and decision support systems, among others. The first chapter of this book provides a thorough introduction to the topics of causation in Bayesian belief networks, applications of uncertainty, automated model construction and learning, graphic models for inference and decision making, and qualitative reasoning. The following chapters examine the fundamental models of computational techniques, computational modeling of biological and natural intelligent systems, including swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems, and evolutionary computation. They also examine decision making and analysis, expert systems, and robotics in the context of artificial intelligence and computer science. - Provides readers a thorough understanding of the uncertainty that arises in artificial intelligence (AI), computational intelligence (CI) paradigms, and algorithms - Encourages readers to put concepts into practice and solve complex real-world problems using CI development frameworks like decision support systems and visual decision design - Provides a comprehensive overview of the techniques used in computational intelligence, uncertainty, and decision
This book presents the state of the art in high-performance computing and simulation on modern supercomputer architectures. It covers trends in hardware and software development in general and the future of high-performance systems and heterogeneous architectures in particular. The application-related contributions cover computational fluid dynamics, material science, medical applications and climate research; innovative fields such as coupled multi-physics and multi-scale simulations are highlighted. All papers were chosen from presentations given at the 18th Workshop on Sustained Simulation Performance held at the HLRS, University of Stuttgart, Germany in October 2013 and subsequent Workshop of the same name held at Tohoku University in March 2014.
This is an introductory book on supercomputer applications written by a researcher who is working on solving scientific and engineering application problems on parallel computers. The book is intended to quickly bring researchers and graduate students working on numerical solutions of partial differential equations with various applications into the area of parallel processing.The book starts from the basic concepts of parallel processing, like speedup, efficiency and different parallel architectures, then introduces the most frequently used algorithms for solving PDEs on parallel computers, with practical examples. Finally, it discusses more advanced topics, including different scalability metrics, parallel time stepping algorithms and new architectures and heterogeneous computing networks which have emerged in the last few years of high performance computing. Hundreds of references are also included in the book to direct interested readers to more detailed and in-depth discussions of specific topics.
This book constitutes the refereed proceedings of the 6th International Workshop on Advanced Parallel Processing Technologies, APPT 2005, held in Hong Kong, China in September 2005. The 55 revised full papers presented were carefully reviewed and selected from over 220 submissions. All current aspects in parallel and distributed computing are addressed ranging from hardware and software issues to algorithmic aspects and advanced applications. The papers are organized in topical sections on architecture, algorithm and theory, system and software, grid computing, networking, and applied technologies.
Both algorithms and the software . and hardware of automatic computers have gone through a rapid development in the past 35 years. The dominant factor in this development was the advance in computer technology. Computer parameters were systematically improved through electron tubes, transistors and integrated circuits of ever-increasing integration density, which also influenced the development of new algorithms and programming methods. Some years ago the situation in computers development was that no additional enhancement of their performance could be achieved by increasing the speed of their logical elements, due to the physical barrier of the maximum transfer speed of electric signals. Another enhancement of computer performance has been achieved by parallelism, which makes it possible by a suitable organization of n processors to obtain a perform ance increase of up to n times. Research into parallel computations has been carried out for several years in many countries and many results of fundamental importance have been obtained. Many parallel computers have been designed and their algorithmic and program ming systems built. Such computers include ILLIAC IV, DAP, STARAN, OMEN, STAR-100, TEXAS INSTRUMENTS ASC, CRAY-1, C mmp, CM*, CLIP-3, PEPE. This trend is supported by the fact that: a) many algorithms and programs are highly parallel in their structure, b) the new LSI and VLSI technologies have allowed processors to be combined into large parallel structures, c) greater and greater demands for speed and reliability of computers are made.