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User-defined tensor data analysis

Byna, Suren.

User-defined tensor data analysis

Record Type:	Electronic resources : Monograph/item
Title/Author:	User-defined tensor data analysisby Bin Dong, Kesheng Wu, Suren Byna.
Author:	Dong, Bin.
other author:	Wu, Kesheng.
Published:	Cham :Springer International Publishing :2021.
Description:	xii, 101 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Calculus of tensorsData processing.
Online resource:	https://doi.org/10.1007/978-3-030-70750-7
ISBN:	9783030707507$q(electronic bk.)

User-defined tensor data analysis
Dong, Bin.

User-defined tensor data analysis[electronic resource] /by Bin Dong, Kesheng Wu, Suren Byna. - Cham :Springer International Publishing :2021. - xii, 101 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5776. - SpringerBriefs in computer science..

1. Introduction -- 1.1 Lessons from Big Data Systems -- 1.2 Data Model -- 1. 3 Programming Model High-Performance Data Analysis for Science -- 2. FasTensor Programming Model -- 2.1 Introduction to Tensor Data Model -- 2.2 FasTensor Programming Model -- 2.2.1 Stencils -- 2.2.2 Chunks -- 2.2.3 Overlap -- 2.2.4 Operator: Transform -- 2.2.5 FasTensor Execution Engine -- 2.2.6 FasTensor Scientific Computing Use Cases -- 2.3 Summary -- Illustrated FasTensor User Interface -- 3.1 An Example -- 3.2 The Stencil Class -- 3.2.1 Constructors of the Stencil -- 3.2.2 Parenthesis operator () and ReadPoint -- 3.2.3 SetShape and GetShape -- 3.2.4 SetValue and GetValue -- 3.2.5 ReadNeighbors and WriteNeighbors -- 3.2.6 GetOffsetUpper and GetOffsetLower -- 3.2.7 GetChunkID -- 3.2.8 GetGlobalIndex and GetLocalIndex -- 3.2.9 Exercise of the Stencil class -- 3.3 The Array Class -- 3.3.1 Constructors of Array -- 3.3.2 SetChunkSize, SetChunkSizeByMem, SetChunkSizeByDim, and GetChunkSize -- 3.3.3 SetOverlapSize, SetOverlapSizeByDetection, GetOverlapSize, SetOverlapPadding, and SyncOverlap -- 3.3.4 Transform -- 3.3.5 SetStride and GetStride -- 3.3.6 AppendAttribute, InsertAttribute, GetAttribute and EraseAttribute -- 3.3.7 SetEndpoint and GetEndpoint -- 3.3.8 ControlEndpoint -- 3.3.9 -- ReadArray and WriteArray -- 3.3.10 SetTag and GetTag -- 3.3.11 GetArraySize and SetArraySize -- 3.3.12 Backup and Restore -- 3.3.13 CreateVisFile -- 3.3.14 ReportCost -- 3.3.15 EP_DIR Endpoint -- 3.3.16 EP_HDF5 and Other Endpoints -- Other Functions in FasTensor -- 3.4.1 FT_Init -- 3.4.2 FT_Finalize -- 3.4.3 Data types in FasTensor -- 4. FasTensor in Real Scientific Applications -- 4.1 DAS: Distributed Acoustic Sensing -- 4.2 VPIC: Vector Particle-In-Cell -- Appendix -- A.1 Installation Guide of FasTensor -- A.2 How to Develop a New Endpoint Protocol -- Alphabetical Index -- Bibliography -- References.

Ths SpringerBrief introduces FasTensor, a powerful parallel data programming model developed for big data applications. This book also provides a user's guide for installing and using FasTensor. FasTensor enables users to easily express many data analysis operations, which may come from neural networks, scientific computing, or queries from traditional database management systems (DBMS) FasTensor frees users from all underlying and tedious data management tasks, such as data partitioning, communication, and parallel execution. This SpringerBrief gives a high-level overview of the state-of-the-art in parallel data programming model and a motivation for the design of FasTensor. It illustrates the FasTensor application programming interface (API) with an abundance of examples and two real use cases from cutting edge scientific applications. FasTensor can achieve multiple orders of magnitude speedup over Spark and other peer systems in executing big data analysis operations. FasTensor makes programming for data analysis operations at large scale on supercomputers as productively and efficiently as possible. A complete reference of FasTensor includes its theoretical foundations, C++ implementation, and usage in applications. Scientists in domains such as physical and geosciences, who analyze large amounts of data will want to purchase this SpringerBrief. Data engineers who design and develop data analysis software and data scientists, and who use Spark or TensorFlow to perform data analyses, such as training a deep neural network will also find this SpringerBrief useful as a reference tool.

ISBN: 9783030707507$q(electronic bk.)

Standard No.: 10.1007/978-3-030-70750-7doiSubjects--Topical Terms:

280410
Calculus of tensors
--Data processing.

LC Class. No.: QA433

Dewey Class. No.: 515.63

User-defined tensor data analysis
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505 0 $a 1. Introduction -- 1.1 Lessons from Big Data Systems -- 1.2 Data Model -- 1. 3 Programming Model High-Performance Data Analysis for Science -- 2. FasTensor Programming Model -- 2.1 Introduction to Tensor Data Model -- 2.2 FasTensor Programming Model -- 2.2.1 Stencils -- 2.2.2 Chunks -- 2.2.3 Overlap -- 2.2.4 Operator: Transform -- 2.2.5 FasTensor Execution Engine -- 2.2.6 FasTensor Scientific Computing Use Cases -- 2.3 Summary -- Illustrated FasTensor User Interface -- 3.1 An Example -- 3.2 The Stencil Class -- 3.2.1 Constructors of the Stencil -- 3.2.2 Parenthesis operator () and ReadPoint -- 3.2.3 SetShape and GetShape -- 3.2.4 SetValue and GetValue -- 3.2.5 ReadNeighbors and WriteNeighbors -- 3.2.6 GetOffsetUpper and GetOffsetLower -- 3.2.7 GetChunkID -- 3.2.8 GetGlobalIndex and GetLocalIndex -- 3.2.9 Exercise of the Stencil class -- 3.3 The Array Class -- 3.3.1 Constructors of Array -- 3.3.2 SetChunkSize, SetChunkSizeByMem, SetChunkSizeByDim, and GetChunkSize -- 3.3.3 SetOverlapSize, SetOverlapSizeByDetection, GetOverlapSize, SetOverlapPadding, and SyncOverlap -- 3.3.4 Transform -- 3.3.5 SetStride and GetStride -- 3.3.6 AppendAttribute, InsertAttribute, GetAttribute and EraseAttribute -- 3.3.7 SetEndpoint and GetEndpoint -- 3.3.8 ControlEndpoint -- 3.3.9 -- ReadArray and WriteArray -- 3.3.10 SetTag and GetTag -- 3.3.11 GetArraySize and SetArraySize -- 3.3.12 Backup and Restore -- 3.3.13 CreateVisFile -- 3.3.14 ReportCost -- 3.3.15 EP_DIR Endpoint -- 3.3.16 EP_HDF5 and Other Endpoints -- Other Functions in FasTensor -- 3.4.1 FT_Init -- 3.4.2 FT_Finalize -- 3.4.3 Data types in FasTensor -- 4. FasTensor in Real Scientific Applications -- 4.1 DAS: Distributed Acoustic Sensing -- 4.2 VPIC: Vector Particle-In-Cell -- Appendix -- A.1 Installation Guide of FasTensor -- A.2 How to Develop a New Endpoint Protocol -- Alphabetical Index -- Bibliography -- References.
520 $a Ths SpringerBrief introduces FasTensor, a powerful parallel data programming model developed for big data applications. This book also provides a user's guide for installing and using FasTensor. FasTensor enables users to easily express many data analysis operations, which may come from neural networks, scientific computing, or queries from traditional database management systems (DBMS) FasTensor frees users from all underlying and tedious data management tasks, such as data partitioning, communication, and parallel execution. This SpringerBrief gives a high-level overview of the state-of-the-art in parallel data programming model and a motivation for the design of FasTensor. It illustrates the FasTensor application programming interface (API) with an abundance of examples and two real use cases from cutting edge scientific applications. FasTensor can achieve multiple orders of magnitude speedup over Spark and other peer systems in executing big data analysis operations. FasTensor makes programming for data analysis operations at large scale on supercomputers as productively and efficiently as possible. A complete reference of FasTensor includes its theoretical foundations, C++ implementation, and usage in applications. Scientists in domains such as physical and geosciences, who analyze large amounts of data will want to purchase this SpringerBrief. Data engineers who design and develop data analysis software and data scientists, and who use Spark or TensorFlow to perform data analyses, such as training a deep neural network will also find this SpringerBrief useful as a reference tool.
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