Vector Models for Data-parallel Computing
Author: Guy E. Blelloch
Publisher: MIT Press (MA)
Published: 1990
Total Pages: 288
ISBN-13:
DOWNLOAD EBOOK →Mathematics of Computing -- Parallelism.
Author: Guy E. Blelloch
Publisher: MIT Press (MA)
Published: 1990
Total Pages: 288
ISBN-13:
DOWNLOAD EBOOK →Mathematics of Computing -- Parallelism.
Author: Guy-Rene Perrin
Publisher: Springer Science & Business Media
Published: 1996-09-11
Total Pages: 316
ISBN-13: 9783540617365
DOWNLOAD EBOOK →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.
Author: Pavan Balaji
Publisher: MIT Press
Published: 2015-11-06
Total Pages: 488
ISBN-13: 0262528819
DOWNLOAD EBOOK →An overview of the most prominent contemporary parallel processing programming models, written in a unique tutorial style. With the coming of the parallel computing era, computer scientists have turned their attention to designing programming models that are suited for high-performance parallel computing and supercomputing systems. Programming parallel systems is complicated by the fact that multiple processing units are simultaneously computing and moving data. This book offers an overview of some of the most prominent parallel programming models used in high-performance computing and supercomputing systems today. The chapters describe the programming models in a unique tutorial style rather than using the formal approach taken in the research literature. The aim is to cover a wide range of parallel programming models, enabling the reader to understand what each has to offer. The book begins with a description of the Message Passing Interface (MPI), the most common parallel programming model for distributed memory computing. It goes on to cover one-sided communication models, ranging from low-level runtime libraries (GASNet, OpenSHMEM) to high-level programming models (UPC, GA, Chapel); task-oriented programming models (Charm++, ADLB, Scioto, Swift, CnC) that allow users to describe their computation and data units as tasks so that the runtime system can manage computation and data movement as necessary; and parallel programming models intended for on-node parallelism in the context of multicore architecture or attached accelerators (OpenMP, Cilk Plus, TBB, CUDA, OpenCL). The book will be a valuable resource for graduate students, researchers, and any scientist who works with data sets and large computations. Contributors Timothy Armstrong, Michael G. Burke, Ralph Butler, Bradford L. Chamberlain, Sunita Chandrasekaran, Barbara Chapman, Jeff Daily, James Dinan, Deepak Eachempati, Ian T. Foster, William D. Gropp, Paul Hargrove, Wen-mei Hwu, Nikhil Jain, Laxmikant Kale, David Kirk, Kath Knobe, Ariram Krishnamoorthy, Jeffery A. Kuehn, Alexey Kukanov, Charles E. Leiserson, Jonathan Lifflander, Ewing Lusk, Tim Mattson, Bruce Palmer, Steven C. Pieper, Stephen W. Poole, Arch D. Robison, Frank Schlimbach, Rajeev Thakur, Abhinav Vishnu, Justin M. Wozniak, Michael Wilde, Kathy Yelick, Yili Zheng
Author: Yidi Song
Publisher:
Published: 2013
Total Pages:
ISBN-13:
DOWNLOAD EBOOK →"During the past decade, technology in the electronics industry has advanced considerably. The integrated circuits we are using today are becoming more and more complex. As a result, modeling those complex systems has become a difficult task. The vector fitting method is a very efficient tool for building a model based on measured or simulated data. However, for large scale systems, the vector fitting method runs slowly or even fails to converge at the end. One of the solutions to the problem is the parallel vector fitting which was introduced a few years ago. Recently, the parallel computing and cloud computing have become more popular. It would be much more efficient if we can use the concept of parallel computing to do the vector fitting. Since each column in the admittance matrix Y is independent from each other. Calculations on one column will not affect the results of another column. Thus, we can do multiple column vector fittings at the same time. This concept leads to the idea of doing the vector fitting in a parallel way. During the algorithm, many columns are being vector fitted at the same time. There is one small model for each column. After all columns are done, an extra routine will be executed to combine all sub-models into one complete model. In this way, we can achieve a descent speedup factor which leads to less total computing time. The final model is verified so that it is as accurate as the one generated by the traditional vector fitting. In this thesis, detailed concepts will be presented. Methods will be explained step by step and examples will be tested and analyzed." --
Author: Philip J. Hatcher
Publisher: MIT Press
Published: 1991
Total Pages: 262
ISBN-13: 9780262082051
DOWNLOAD EBOOK →Mathematics of Computing -- Parallelism.
Author: Guy E. Blelloch
Publisher: American Mathematical Soc.
Published: 1994
Total Pages: 413
ISBN-13: 0821802534
DOWNLOAD EBOOK →This volume contains papers presented at the DIMACS workshop on Specification of Parallel Algorithms, held in May 1994 at Princeton University. The goal of the workshop was to bring together some of the best researchers in parallel languages, algorithms, and systems to present and discuss recent developments in their areas of expertise. Among the topics discussed were new specification techniques for concurrent and distributed systems, behavioral and operational specification techniques, new parallel language and system abstractions, novel concurrent architectures and systems, large-scale parallel systems, specification tools and environments, and proof techniques for concurrent systems.
Author: Yuefan Deng
Publisher: World Scientific
Published: 2013
Total Pages: 218
ISBN-13: 9814307602
DOWNLOAD EBOOK →The book provides a practical guide to computational scientists and engineers to help advance their research by exploiting the superpower of supercomputers with many processors and complex networks. This book focuses on the design and analysis of basic parallel algorithms, the key components for composing larger packages for a wide range of applications.
Author: Sandra Arlinghaus
Publisher: CRC Press
Published: 2020-08-26
Total Pages: 324
ISBN-13: 1000102017
DOWNLOAD EBOOK →The guidance and special techniques provided in this handbook will allow you to understand and use complex spatial statistical techniques. You will learn how to apply proper spatial analysis techniques and why they are generally different from conventional statistical analyses. Clear and concise information on weighting, aggregation effects, sampling, spatial statistics and GIS, and visualization of spatial dependence is provided. Discussions on specific applications using actual data sets fill obvious gaps in the literature, and coverage of critical research frontiers allows readers to explore current areas of active research.
Author: L. Ridgway Scott
Publisher: Princeton University Press
Published: 2021-03-09
Total Pages: 392
ISBN-13: 0691227659
DOWNLOAD EBOOK →What does Google's management of billions of Web pages have in common with analysis of a genome with billions of nucleotides? Both apply methods that coordinate many processors to accomplish a single task. From mining genomes to the World Wide Web, from modeling financial markets to global weather patterns, parallel computing enables computations that would otherwise be impractical if not impossible with sequential approaches alone. Its fundamental role as an enabler of simulations and data analysis continues an advance in a wide range of application areas. Scientific Parallel Computing is the first textbook to integrate all the fundamentals of parallel computing in a single volume while also providing a basis for a deeper understanding of the subject. Designed for graduate and advanced undergraduate courses in the sciences and in engineering, computer science, and mathematics, it focuses on the three key areas of algorithms, architecture, languages, and their crucial synthesis in performance. The book's computational examples, whose math prerequisites are not beyond the level of advanced calculus, derive from a breadth of topics in scientific and engineering simulation and data analysis. The programming exercises presented early in the book are designed to bring students up to speed quickly, while the book later develops projects challenging enough to guide students toward research questions in the field. The new paradigm of cluster computing is fully addressed. A supporting web site provides access to all the codes and software mentioned in the book, and offers topical information on popular parallel computing systems. Integrates all the fundamentals of parallel computing essential for today's high-performance requirements Ideal for graduate and advanced undergraduate students in the sciences and in engineering, computer science, and mathematics Extensive programming and theoretical exercises enable students to write parallel codes quickly More challenging projects later in the book introduce research questions New paradigm of cluster computing fully addressed Supporting web site provides access to all the codes and software mentioned in the book