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Architecture of advanced numerical a...

Wang, Liang.

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Architecture of advanced numerical analysis systems = designing a scientific computing system using OCaml /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Architecture of advanced numerical analysis systems/ by Liang Wang, Jianxin Zhao.
Reminder of title:	designing a scientific computing system using OCaml /
Author:	Wang, Liang.
other author:	Zhao, Jianxin.
Published:	Berkeley, CA :Apress : : 2023.,
Description:	xiii, 472 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Introduction -- Chapter 2: Core Optimization -- Chapter 3: Algorithm Differentiation -- Chapter 4: Mathematical Optimization -- Chapter 5: Deep Neural Networks -- Chapter 6: Computation Graph -- Chapter 7: Performance Accelerators -- Chapter 8: Compiler Backends -- Chapter 9: Composition and Deployment -- Chapter 10: Distributed Computing -- Chapter 11: Testing Framework -- Appendix A: Basic Analytics Examples -- Appendix B: System Conventions -- Appendix C: Metric Systems and Constants -- Appendix D: AlgoDiff Module -- Appendix E: Neural Network Module -- Appendix F: Actor System for Distributed Computing -- Bibliography.
Contained By:	Springer Nature eBook
Subject:	OCaml (Computer program language) -
Online resource:	https://doi.org/10.1007/978-1-4842-8853-5
ISBN:	9781484288535

Architecture of advanced numerical analysis systems = designing a scientific computing system using OCaml /
Wang, Liang.

Architecture of advanced numerical analysis systemsdesigning a scientific computing system using OCaml /[electronic resource] :by Liang Wang, Jianxin Zhao. - Berkeley, CA :Apress :2023. - xiii, 472 p. :ill., digital ;24 cm.

Chapter 1: Introduction -- Chapter 2: Core Optimization -- Chapter 3: Algorithm Differentiation -- Chapter 4: Mathematical Optimization -- Chapter 5: Deep Neural Networks -- Chapter 6: Computation Graph -- Chapter 7: Performance Accelerators -- Chapter 8: Compiler Backends -- Chapter 9: Composition and Deployment -- Chapter 10: Distributed Computing -- Chapter 11: Testing Framework -- Appendix A: Basic Analytics Examples -- Appendix B: System Conventions -- Appendix C: Metric Systems and Constants -- Appendix D: AlgoDiff Module -- Appendix E: Neural Network Module -- Appendix F: Actor System for Distributed Computing -- Bibliography.

Open access.

This unique open access book applies the functional OCaml programming language to numerical or computational weighted data science, engineering, and scientific applications. This book is based on the authors' first-hand experience building and maintaining Owl, an OCaml-based numerical computing library. You'll first learn the various components in a modern numerical computation library. Then, you will learn how these components are designed and built up and how to optimize their performance. After reading and using this book, you'll have the knowledge required to design and build real-world complex systems that effectively leverage the advantages of the OCaml functional programming language. You will: Optimize core operations based on N-dimensional arrays Design and implement an industry-level algorithmic differentiation module Implement mathematical optimization, regression, and deep neural network functionalities based on algorithmic differentiation Design and optimize a computation graph module, and understand the benefits it brings to the numerical computing library Accommodate the growing number of hardware accelerators (e.g. GPU, TPU) and execution backends (e.g. web browser, unikernel) of numerical computation Use the Zoo system for efficient scripting, code sharing, service deployment, and composition Design and implement a distributed computing engine to work with a numerical computing library, providing convenient APIs and high performance.

ISBN: 9781484288535

Standard No.: 10.1007/978-1-4842-8853-5doiSubjects--Topical Terms:

3597860
OCaml (Computer program language)

LC Class. No.: QA76.73.O117 / W36 2023

Dewey Class. No.: 005.117

Architecture of advanced numerical analysis systems = designing a scientific computing system using OCaml /
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245 1 0 $a Architecture of advanced numerical analysis systems $h [electronic resource] : $b designing a scientific computing system using OCaml / $c by Liang Wang, Jianxin Zhao.
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505 0 $a Chapter 1: Introduction -- Chapter 2: Core Optimization -- Chapter 3: Algorithm Differentiation -- Chapter 4: Mathematical Optimization -- Chapter 5: Deep Neural Networks -- Chapter 6: Computation Graph -- Chapter 7: Performance Accelerators -- Chapter 8: Compiler Backends -- Chapter 9: Composition and Deployment -- Chapter 10: Distributed Computing -- Chapter 11: Testing Framework -- Appendix A: Basic Analytics Examples -- Appendix B: System Conventions -- Appendix C: Metric Systems and Constants -- Appendix D: AlgoDiff Module -- Appendix E: Neural Network Module -- Appendix F: Actor System for Distributed Computing -- Bibliography.
506 $a Open access.
520 $a This unique open access book applies the functional OCaml programming language to numerical or computational weighted data science, engineering, and scientific applications. This book is based on the authors' first-hand experience building and maintaining Owl, an OCaml-based numerical computing library. You'll first learn the various components in a modern numerical computation library. Then, you will learn how these components are designed and built up and how to optimize their performance. After reading and using this book, you'll have the knowledge required to design and build real-world complex systems that effectively leverage the advantages of the OCaml functional programming language. You will: Optimize core operations based on N-dimensional arrays Design and implement an industry-level algorithmic differentiation module Implement mathematical optimization, regression, and deep neural network functionalities based on algorithmic differentiation Design and optimize a computation graph module, and understand the benefits it brings to the numerical computing library Accommodate the growing number of hardware accelerators (e.g. GPU, TPU) and execution backends (e.g. web browser, unikernel) of numerical computation Use the Zoo system for efficient scripting, code sharing, service deployment, and composition Design and implement a distributed computing engine to work with a numerical computing library, providing convenient APIs and high performance.
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856 4 0 $u https://doi.org/10.1007/978-1-4842-8853-5
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