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

This book presents a solution to the challenging issue of optimizing expensive-to-evaluate industrial problems such as the hyperparameter tuning of machine learning models. The approach combines two well-established concepts, Surrogate-Based Optimization (SBO) and parallelization, to efficiently search for optimal parameter setups with as few function evaluations as possible. Through in-depth analysis, the need for parallel SBO solvers is emphasized, and it is demonstrated that they outperform model-free algorithms in scenarios with a low evaluation budget. The SBO approach helps practitioners save significant amounts of time and resources in hyperparameter tuning as well as other optimization projects. As a highlight, a novel framework for objectively comparing the efficiency of parallel SBO algorithms is introduced, enabling practitioners to evaluate and select the most effective approach for their specific use case. Based on practical examples, decision support is delivered, detailing which parts of industrial optimization projects can be parallelized and how to prioritize which parts to parallelize first. By following the framework, practitioners can make informed decisions about how to allocate resources and optimize their models efficiently.

This book constitutes the refereed proceedings of the 6th International Conference on Computers and Games, CG 2008, held in Beijing, China, in September/October 2008 co-located with the 13th Computer Olympiad and the 16th World Computer-Chess Championship. The 24 revised full papers presented were carefully reviewed and selected from 40 submissions. The papers cover all aspects of artificial intelligence in computer-game playing dealing with many different research topics, such as cognition, combinatorial game theory, search, knowledge representation, and optimization.

Rapid advances in microelectronic integration and the advent of Systems-on-Chip have fueled the need for high-level synthesis, i.e., an automated approach to the synthesis of hardware from behavioral descriptions. SPARK: A Parallelizing Approach to the High - Level Synthesis of Digital Circuits presents a novel approach to the high-level synthesis of digital circuits -- that of parallelizing high-level synthesis (PHLS). This approach uses aggressive code parallelizing and code motion techniques to discover circuit optimization opportunities beyond what is possible with traditional high-level synthesis. This PHLS approach addresses the problems of the poor quality of synthesis results and the lack of controllability over the transformations applied during the high-level synthesis of system descriptions with complex control flows, that is, with nested conditionals and loops. Also described are speculative code motion techniques and dynamic compiler transformations that optimize the circuit quality in terms of cycle time, circuit size and interconnect costs. We describe the SPARK parallelizing high-level synthesis framework in which we have implemented these techniques and demonstrate the utility of SPARK's PHLS approach using designs derived from multimedia and image processing applications. We also present a case study of an instruction length decoder derived from the Intel Pentium-class of microprocessors. This case study serves as an example of a typical microprocessor functional block with complex control flow and demonstrates how our techniques are useful for such designs. SPARK: A Parallelizing Approach to the High - Level Synthesis of Digital Circuits is targeted mainly to embedded system designers and researchers. This includes people working on design and design automation. The book is useful for researchers and design automation engineers who wish to understand how the main problems hindering the adoption of high-level synthesis among designers.

Containing over 300 entries in an A-Z format, the Encyclopedia of Parallel Computing provides easy, intuitive access to relevant information for professionals and researchers seeking access to any aspect within the broad field of parallel computing. Topics for this comprehensive reference were selected, written, and peer-reviewed by an international pool of distinguished researchers in the field. The Encyclopedia is broad in scope, covering machine organization, programming languages, algorithms, and applications. Within each area, concepts, designs, and specific implementations are presented. The highly-structured essays in this work comprise synonyms, a definition and discussion of the topic, bibliographies, and links to related literature. Extensive cross-references to other entries within the Encyclopedia support efficient, user-friendly searchers for immediate access to useful information. Key concepts presented in the Encyclopedia of Parallel Computing include; laws and metrics; specific numerical and non-numerical algorithms; asynchronous algorithms; libraries of subroutines; benchmark suites; applications; sequential consistency and cache coherency; machine classes such as clusters, shared-memory multiprocessors, special-purpose machines and dataflow machines; specific machines such as Cray supercomputers, IBM’s cell processor and Intel’s multicore machines; race detection and auto parallelization; parallel programming languages, synchronization primitives, collective operations, message passing libraries, checkpointing, and operating systems. Topics covered: Speedup, Efficiency, Isoefficiency, Redundancy, Amdahls law, Computer Architecture Concepts, Parallel Machine Designs, Benmarks, Parallel Programming concepts & design, Algorithms, Parallel applications. This authoritative reference will be published in two formats: print and online. The online edition features hyperlinks to cross-references and to additional significant research. Related Subjects: supercomputing, high-performance computing, distributed computing

Robert Siegfried presents a framework for efficient agent-based modeling and simulation of complex systems. He compares different approaches for describing structure and dynamics of agent-based models in detail. Based on this evaluation the author introduces the “General Reference Model for Agent-based Modeling and Simulation” (GRAMS). Furthermore he presents parallel and distributed simulation approaches for execution of agent-based models –from small scale to very large scale. The author shows how agent-based models may be executed by different simulation engines that utilize underlying hardware resources in an optimized fashion.

A wide range of modern computer applications require the performance and flexibility of parallel and distributed systems. Better software support is required if the technical advances in these systems are to be fully exploited by commerce and industry. This involves the provision of specialised techniques and tools as well as the integration of standard software engineering methods. This book will reflect current advances in this area, and will address issues of theory and practice with contributions from academia and industry. It is the aim of the book to provide a focus for information on this developing which will be of use to both researchers and practitioners.

Molecular dynamics is a well-established technique for simulating complex many-particle systems in many areas of physics, chemistry, and astrophysics. The huge computational requirements for simulations of large systems, especially with long-range forces, demand the use of massively parallel computers. Designing efficient algorithms for these problems is a highly non-trivial task. This book contains the invited talks and abstracts presented at a conference by more than 100 researchers from various fields: computer science, solid state physics, high energy physics, polymers, biochemistry, granular materials and astrophysics. Most of the contributions have been written by users of massively parallel computers and deal with practical issues, but there are also contributions tackling more fundamental algorithmic problems.

This book constitutes the refereed proceedings of the 24th Australasian Joint Conference on Artificial Intelligence, AI 2011, held in Perth, Australia, in December 2011. The 82 revised full papers presented were carefully reviewed and selected from 193 submissions. The papers are organized in topical sections on data mining and knowledge discovery, machine learning, evolutionary computation and optimization, intelligent agent systems, logic and reasoning, vision and graphics, image processing, natural language processing, cognitive modeling and simulation technology, and AI applications.