TY  - BOOK
AU  - Wang,Liang
AU  - Zhao,Jianxin
ED  - SpringerLink (Online service)
TI  - Architecture of Advanced Numerical Analysis Systems: Designing a Scientific Computing System using OCaml 
SN  - 9781484288535
AV  - QA76.7-.73 
U1  - 005.13 23
PY  - 2023///
CY  - Berkeley, CA
PB  - Apress, Imprint: Apress
KW  - Programming languages (Electronic computers)
KW  - Computer science
KW  - Artificial intelligence
KW  - Data processing
KW  - Programming Language
KW  - Computer Science
KW  - Data Science
KW  - Models of Computation
N1  - 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
N2  - 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
UR  - https://doi.org/10.1007/978-1-4842-8853-5
ER  -