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Parallel Programming with Co-Arrays describes the basic techniques used to design parallel algorithms for high-performance, scientific computing. It is intended for upper-level undergraduate students and graduate students who need to develop parallel codes with little or no previous introduction to parallel computing. It is also intended as a reference manual for researchers active in the field of scientific computing. All the algorithms in the book are based on partition operators. These operators provide a unifying principle that fits seemingly disparate techniques into an overall framework for algorithm design. The book uses the co-array programming model to illustrate how to write code for concrete examples, but it emphasizes that the important concepts for algorithm design are independent of the programming model. With these concepts in mind, the reader can write algorithms in different programming models based on personal taste and comfort.

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The last decade has seen a huge and growing interest in processing large data sets on large distributed clusters. This trend began with the MapReduce framework, and has been widely adopted by several other systems, including PigLatin, Hive, Scope, Dremmel, Spark and Myria to name a few. While the applications of such systems are diverse (for example, machine learning, data analytics), most involve relatively standard data processing tasks like identifying relevant data, cleaning, filtering, joining, grouping, transforming, extracting features, and evaluating results. This has generated great interest in the study of algorithms for data processing on large distributed clusters. Algorithmic Aspects of Parallel Data Processing discusses recent algorithmic developments for distributed data processing. It uses a theoretical model of parallel processing called the Massively Parallel Computation (MPC) model, which is a simplification of the BSP model where the only cost is given by the amount of communication and the number of communication rounds. The survey studies several algorithms for multi-join queries, sorting, and matrix multiplication. It discusses their relationships and common techniques applied across the different data processing tasks.

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Computer networks --- Parallel computers --- Parallel processing (Electronic computers)

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Bisseling explains how to use the bulk synchronous parallel (BSP) model and the freely available BSPlib communication library in parallel algorithm design and parallel programming. An appendix on the message-passing interface (MPI) discusses how to program using the MPI communication library.

Parallel processing (Electronic computers) --- Supercomputers --- Scientific applications. --- Scientific applications.