Loop tiling, as one of the most important compiler optimizations, is beneficial for both parallel machines and uniprocessors with a memory hierarchy. This book explores the use of loop tiling for reducing communication cost and improving parallelism for distributed memory machines. The author provides mathematical foundations, investigates loop permutability in the framework of nonsingular loop transformations, discusses the necessary machineries required, and presents state-of-the-art results for finding communication- and time-minimal tiling choices. Throughout the book, theorems and algorithms are illustrated with numerous examples and diagrams. The techniques presented in Loop Tiling for Parallelism can be adapted to work for a cluster of workstations, and are also directly applicable to shared-memory machines once the machines are modeled as BSP (Bulk Synchronous Parallel) machines. Features and key topics: Detailed review of the mathematical foundations, including convex polyhedra and cones; Self-contained treatment of nonsingular loop transformations, code generation, and full loop permutability; Tiling loop nests by rectangles and parallelepipeds, including their mathematical definition, dependence analysis, legality test, and code generation; A complete suite of techniques for generating SPMD code for a tiled loop nest; Up-to-date results on tile size and shape selection for reducing communication and improving parallelism; End-of-chapter references for further reading. Researchers and practitioners involved in optimizing compilers and students in advanced computer architecture studies will find this a lucid and well-presented reference work with numerous citations to original sources.

Programming Massively Parallel Processors: A Hands-on Approach, Second Edition, teaches students how to program massively parallel processors. It offers a detailed discussion of various techniques for constructing parallel programs. Case studies are used to demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. This guide shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in depth. This revised edition contains more parallel programming examples, commonly-used libraries such as Thrust, and explanations of the latest tools. It also provides new coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more; increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism; and two new case studies (on MRI reconstruction and molecular visualization) that explore the latest applications of CUDA and GPUs for scientific research and high-performance computing. This book should be a valuable resource for advanced students, software engineers, programmers, and hardware engineers. - New coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more - Increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism - Two new case studies (on MRI reconstruction and molecular visualization) explore the latest applications of CUDA and GPUs for scientific research and high-performance computing

This book constitutes the thoroughly refereed post-proceedings of the 23rd International Workshop on Languages and Compilers for Parallel Computing, LCPC 2010, held in Houston, TX, USA, in October 2010. The 18 revised full papers presented were carefully reviewed and selected from 47 submissions. The scope of the workshop spans foundational results and practical experience, and targets all classes of parallel platforms in- cluding concurrent, multithreaded, multicore, accelerated, multiprocessor, and cluster systems.

This textbook covers the new development in processor architecture and parallel hardware. It provides detailed descriptions of parallel programming techniques that are necessary for developing efficient programs for multicore processors as well as for parallel cluster systems and supercomputers. The book is structured in three main parts, covering all areas of parallel computing: the architecture of parallel systems, parallel programming models and environments, and the implementation of efficient application algorithms. The emphasis lies on parallel programming techniques needed for different architectures. In particular, this third edition includes an extended update of the chapter on computer architecture and performance analysis taking new developments such as the aspect of energy consumption into consideration. The description of OpenMP has been extended and now also captures the task concept of OpenMP. The chapter on message-passing programming has been extended and updated to include new features of MPI such as extended reduction operations and non-blocking collective communication operations. The chapter on GPU programming also has been updated. All other chapters also have been revised carefully. The main goal of this book is to present parallel programming techniques that can be used in many situations for many application areas and to enable the reader to develop correct and efficient parallel programs. Many example programs and exercises are provided to support this goal and to show how the techniques can be applied to further applications. The book can be used as a textbook for students as well as a reference book for professionals. The material of the book has been used for courses in parallel programming at different universities for many years.

This book introduces new compilation techniques, using the polyhedron model for the resource-adaptive parallel execution of loop programs on massively parallel processor arrays. The authors show how to compute optimal symbolic assignments and parallel schedules of loop iterations at compile time, for cases where the number of available cores becomes known only at runtime. The compile/runtime symbolic parallelization approach the authors describe reduces significantly the runtime overhead, compared to dynamic or just‐in-time compilation. The new, on‐demand fault‐tolerant loop processing approach described in this book protects loop nests for parallel execution against soft errors.

ICA3PP 2000 was an important conference that brought together researchers and practitioners from academia, industry and governments to advance the knowledge of parallel and distributed computing. The proceedings constitute a well-defined set of innovative research papers in two broad areas of parallel and distributed computing: (1) architectures, algorithms and networks; (2) systems and applications.

. We present a theory of loop transformations that is rigorous and yet reader-friendly; this will make it easier to learn the subject and do research in this area.