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Titlebook: Introduction to HPC with MPI for Data Science; Frank Nielsen Textbook 2016 Springer International Publishing Switzerland 2016 Data Science

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發(fā)表于 2025-3-21 16:24:26 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Introduction to HPC with MPI for Data Science
編輯Frank Nielsen
視頻videohttp://file.papertrans.cn/474/473755/473755.mp4
概述Contains numerous exercises and a test exam.Features material that has been used and tested with students.Provides additional material, including source C++/MPI codes and slides for each chapter, on a
叢書名稱Undergraduate Topics in Computer Science
圖書封面Titlebook: Introduction to HPC with MPI for Data Science; Frank Nielsen Textbook 2016 Springer International Publishing Switzerland 2016 Data Science
描述.This gentle introduction to High Performance Computing (HPC) for Data Science using the Message Passing Interface (MPI) standard has been designed as a first course for undergraduates on parallel programming on distributed memory models, and requires only basic programming notions..Divided into two parts the first part covers high performance computing using C++ with the Message Passing Interface (MPI) standard followed by a second part providing high-performance data analytics on computer clusters..In the first part, the fundamental notions of blocking versus non-blocking point-to-point communications, global communications (like broadcast or scatter) and collaborative computations (reduce), with Amdalh and Gustafson speed-up laws are described before addressing parallel sorting and parallel linear algebra on computer clusters. The common ring, torus and hypercube topologies of clusters are then explained and global communication procedures on these topologies are studied. This first part closes with the MapReduce (MR) model of computation well-suited to processing big data using the MPI framework..In the second part, the book focuses on high-performance data analytics. Flat and
出版日期Textbook 2016
關(guān)鍵詞Data Science; Exploratory analytics and knowledge discovery; High Performance Computing (HPC); Large-sc
版次1
doihttps://doi.org/10.1007/978-3-319-21903-5
isbn_softcover978-3-319-21902-8
isbn_ebook978-3-319-21903-5Series ISSN 1863-7310 Series E-ISSN 2197-1781
issn_series 1863-7310
copyrightSpringer International Publishing Switzerland 2016
The information of publication is updating

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發(fā)表于 2025-3-21 23:04:08 | 只看該作者
Textbook 2016s are then explained and global communication procedures on these topologies are studied. This first part closes with the MapReduce (MR) model of computation well-suited to processing big data using the MPI framework..In the second part, the book focuses on high-performance data analytics. Flat and
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A Glance at High Performance Computing (HPC)mming languages and application programming interfaces (APIs), the dedicated software tools, the international specialized conferences (ACM/IEEE Super-Computing, or SC for short), etc. Loosely speaking, HPC is both the scientific and technical fields of study of “Super-Computers” (SCs).
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Introduction to MPI: The Message Passing Interfacetions, procedures, data-types, constants, etc.) with the precise semantic of communication protocols and global calculation routines, among others. Thus a parallel program using distributed memory can be implemented using various implementations of the MPI interface provided by several vendors (like
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Partition-Based Clustering with ,-Meansle structures (or “patterns”) in those seas of data. Exploratory data analysis is concerned with this challenge of finding such structural information without any prior knowledge: in this case, those techniques that consist in learning from data without prior knowledge are called generically unsuper
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