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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.).
This book constitutes the thoroughly refereed post-conference proceedings of the 18th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2014, held in Phoenix, AZ, USA, in May 2014. The 9 revised full papers presented were carefully reviewed and selected from 24 submissions. The papers cover the following topics: single-core parallelism; moving to distributed-memory, larger-scale systems, scheduling fairness; and parallel job scheduling.
This book is one of the first to address the problem of forming useful parallelism from potential parallelism and to provide a general solution. The book presents two approaches to automatic partitioning and scheduling so that the same parallel program can be made to execute efficiently on widely different multiprocessors. The first approach is based on a macro dataflow model in which the program is partitioned into tasks at compile time and the tasks are scheduled on processors at run time. The second approach is based on a compile time scheduling model, where both the partitioning and scheduling are performed at compile time. Both approaches have been implemented in partition programs written in the single assignment language SISAL. The inputs to the partitioning and scheduling algorithms are a graphical representation of the parallel program and a list of parameters describing the target multiprocessor. Execution profile information is used to derive compile-time estimates of execution times and data sizes in the program. Both the macro dataflow and compile-time scheduling problems are expressed as optimization problems and are shown to be NP complete in the strong sense. Efficient approximation algorithms for these problems are presented. Finally, the effectiveness of the partitioning and scheduling algorithms is studied by multiprocessor simulations of various SISAL benchmark programs for different target multiprocessor parameters. Vivek Sarkar is a Member of Research Staff at the IBM T. J. Watson Research Center. Partitioning and Scheduling Parallel Programs for Multiprocessing is included in the series Research Monographs in Parallel and Distributed Computing. Copublished with Pitman Publishing.
This book presents task-scheduling techniques for emerging complex parallel architectures including heterogeneous multi-core architectures, warehouse-scale datacenters, and distributed big data processing systems. The demand for high computational capacity has led to the growing popularity of multicore processors, which have become the mainstream in both the research and real-world settings. Yet to date, there is no book exploring the current task-scheduling techniques for the emerging complex parallel architectures. Addressing this gap, the book discusses state-of-the-art task-scheduling techniques that are optimized for different architectures, and which can be directly applied in real parallel systems. Further, the book provides an overview of the latest advances in task-scheduling policies in parallel architectures, and will help readers understand and overcome current and emerging issues in this field.
A new model for task scheduling that dramatically improves the efficiency of parallel systems Task scheduling for parallel systems can become a quagmire of heuristics, models, and methods that have been developed over the past decades. The author of this innovative text cuts through the confusion and complexity by presenting a consistent and comprehensive theoretical framework along with realistic parallel system models. These new models, based on an investigation of the concepts and principles underlying task scheduling, take into account heterogeneity, contention for communication resources, and the involvement of the processor in communications. For readers who may be new to task scheduling, the first chapters are essential. They serve as an excellent introduction to programming parallel systems, and they place task scheduling within the context of the program parallelization process. The author then reviews the basics of graph theory, discussing the major graph models used to represent parallel programs. Next, the author introduces his task scheduling framework. He carefully explains the theoretical background of this framework and provides several examples to enable readers to fully understand how it greatly simplifies and, at the same time, enhances the ability to schedule. The second half of the text examines both basic and advanced scheduling techniques, offering readers a thorough understanding of the principles underlying scheduling algorithms. The final two chapters address communication contention in scheduling and processor involvement in communications. Each chapter features exercises that help readers put their new skills into practice. An extensive bibliography leads to additional information for further research. Finally, the use of figures and examples helps readers better visualize and understand complex concepts and processes. Researchers and students in distributed and parallel computer systems will find that this text dramatically improves their ability to schedule tasks accurately and efficiently.
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
Focusing on algorithms for distributed-memory parallel architectures, Parallel Algorithms presents a rigorous yet accessible treatment of theoretical models of parallel computation, parallel algorithm design for homogeneous and heterogeneous platforms, complexity and performance analysis, and essential notions of scheduling. The book extract
This monograph-like book assembles the thorougly revised and cross-reviewed lectures given at the School on Data Parallelism, held in Les Menuires, France, in May 1996. The book is a unique survey on the current status and future perspectives of the currently very promising and popular data parallel programming model. Much attention is paid to the style of writing and complementary coverage of the relevant issues throughout the 12 chapters. Thus these lecture notes are ideally suited for advanced courses or self-instruction on data parallel programming. Furthermore, the book is indispensable reading for anybody doing research in data parallel programming and related areas.
Scalable parallel systems or, more generally, distributed memory systems offer a challenging model of computing and pose fascinating problems regarding compiler optimization, ranging from language design to run time systems. Research in this area is foundational to many challenges from memory hierarchy optimizations to communication optimization. This unique, handbook-like monograph assesses the state of the art in the area in a systematic and comprehensive way. The 21 coherent chapters by leading researchers provide complete and competent coverage of all relevant aspects of compiler optimization for scalable parallel systems. The book is divided into five parts on languages, analysis, communication optimizations, code generation, and run time systems. This book will serve as a landmark source for education, information, and reference to students, practitioners, professionals, and researchers interested in updating their knowledge about or active in parallel computing.