Topics in Parallel and Distributed Computing provides resources and guidance for those learning PDC as well as those teaching students new to the discipline. The pervasiveness of computing devices containing multicore CPUs and GPUs, including home and office PCs, laptops, and mobile devices, is making even common users dependent on parallel processing. Certainly, it is no longer sufficient for even basic programmers to acquire only the traditional sequential programming skills. The preceding trends point to the need for imparting a broad-based skill set in PDC technology. However, the rapid changes in computing hardware platforms and devices, languages, supporting programming environments, and research advances, poses a challenge both for newcomers and seasoned computer scientists. This edited collection has been developed over the past several years in conjunction with the IEEE technical committee on parallel processing (TCPP), which held several workshops and discussions on learning parallel computing and integrating parallel concepts into courses throughout computer science curricula. - Contributed and developed by the leading minds in parallel computing research and instruction - Provides resources and guidance for those learning PDC as well as those teaching students new to the discipline - Succinctly addresses a range of parallel and distributed computing topics - Pedagogically designed to ensure understanding by experienced engineers and newcomers - Developed over the past several years in conjunction with the IEEE technical committee on parallel processing (TCPP), which held several workshops and discussions on learning parallel computing and integrating parallel concepts

Foreword by Bjarne Stroustrup Software is generally acknowledged to be the single greatest obstacle preventing mainstream adoption of massively-parallel computing. While sequential applications are routinely ported to platforms ranging from PCs to mainframes, most parallel programs only ever run on one type of machine. One reason for this is that most parallel programming systems have failed to insulate their users from the architectures of the machines on which they have run. Those that have been platform-independent have usually also had poor performance. Many researchers now believe that object-oriented languages may offer a solution. By hiding the architecture-specific constructs required for high performance inside platform-independent abstractions, parallel object-oriented programming systems may be able to combine the speed of massively-parallel computing with the comfort of sequential programming. Parallel Programming Using C++ describes fifteen parallel programming systems based on C++, the most popular object-oriented language of today. These systems cover the whole spectrum of parallel programming paradigms, from data parallelism through dataflow and distributed shared memory to message-passing control parallelism. For the parallel programming community, a common parallel application is discussed in each chapter, as part of the description of the system itself. By comparing the implementations of the polygon overlay problem in each system, the reader can get a better sense of their expressiveness and functionality for a common problem. For the systems community, the chapters contain a discussion of the implementation of the various compilers and runtime systems. In addition to discussing the performance of polygon overlay, several of the contributors also discuss the performance of other, more substantial, applications. For the research community, the contributors discuss the motivations for and philosophy of their systems. As well, many of the chapters include critiques that complete the research arc by pointing out possible future research directions. Finally, for the object-oriented community, there are many examples of how encapsulation, inheritance, and polymorphism can be used to control the complexity of developing, debugging, and tuning parallel software.

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.

The book comprises selected papers presented at the International Conference on Advanced Computing, Networking and Informatics (ICANI 2018), organized by Medi-Caps University, India. It includes novel and original research work on advanced computing, networking and informatics, and discusses a wide variety of industrial, engineering and scientific applications of the emerging techniques in the field of computing and networking.

This is THE book for every serious researcher in theoretical computer science. The book exposes critical detail in problem solving and researching in the fields of algorithms and complexity that no other book has ever done. It reveals the secrets of doing research and the way of thinking that are so natural to the world’s top computer scientists. Such skills and thinking are so “second nature” to every top computer scientist that they are not even mentioned or talked about. This book is thus for everyone who seriously wants to become an excellent researcher but may not have such skills and thinking.

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