Scientific Computing With Matlab And Octave 2nd Edition

Mastering Python Scientific Computing By Hemant Kumar Mehta 2015 | 300 Pages A complete guide for Python programmers to master scientific computing using Python APIs and tools If you are a Python programmer and want to get your hands on scientific computing, this book is for you. The book expects you to have had exposure to various concepts of Python programming. In today's world, along with theoretical and experimental work, scientific computing has become an important part of scientific disciplines. Numerical calculations, simulations and computer modeling in this day and age form the vast majority of both experimental and theoretical papers. In the scientific method, replication and reproducibility are two important contributing factors. A complete and concrete scientific result should be reproducible and replicable. Python is suitable for scientific computing. A large community of users, plenty of help and documentation, a large collection of scientific libraries and environments, great performance, and good support makes Python a great choice for scientific computing. At present Python is among the top choices for developing scientific workflow and the book targets existing Python developers to master this domain using Python. The main things to learn in the book are the concept of scientific workflow, managing scientific workflow data and performing computation on this data using Python.

mfem：轻量，通用，可扩展的C ++库，用于有限元方法

Distributed Computing with Python by Francesco Pierfederici AZW3/MOBI/EPUB/PDF 多版本 This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task. We will cover synchronous and asynchronous models, shared memory and file systems, communication between various processes, synchronization, and more.

NUMERICAL RECIPES -- The Art of Scientific Computing, Third Edition © Cambridge University Press 1988, 1992, 2002, 2007 except for 13.10, which is placed into

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AMR-风 | | AMR-Wind是一种大规模并行，块状结构的自适应网格，不可压缩的流量切换器，用于风力涡轮机和风电场仿真。 该代码库是的专注于风的分支。 该求解器建立在顶部。 AMReX库提供了网格数据结构，网格适应性以及用于求解控制方程的线性求解器。 AMR-Wind由，和多机构热忱的团队积极开发和维护。 AMR-Wind的主要应用是：对大气边界层（ABL）流动进行大涡模拟（LES），使用致动器盘或涡轮致动器线模型来模拟风场涡轮-尾流相互作用，并在耦合时作为背景求解器与具有近距离方法的近身求解器（例如Nalu ）一起对风电场中的多个风力涡轮机执行叶片分解模拟。 对于海上应用，建模海海相互作用影响及其对ABL特性的影响的能力是代码开发工作的另一个重点。 与生态系统中的其他代码，AMR-wind具有以下目标： 一个公开的，有据可查的，先进的计算模型实现，用于以各种保真度对风电场流

pylbm pylbm是使用Lattice Boltzmann求解器进行数值模拟的多合一软件包。 该软件包提供了用于描述1D，2D和3D问题中的格子Boltzmann方案的所有工具。 我们选择D'Humières形式主义来描述问题。 您可以使用一组简单的形状（例如圆形，球形，...）来制作复杂的几何图形。 pylbm使用Cython，NumPy或Loo.py根据用户指定的方案和域执行数值方案。 Pythran和Numba即将面市。 pylbm具有mpi4py的MPI支持。 安装 您可以通过多种方式安装pylbm 与曼巴或conda mamba install pylbm -c conda-forge conda install pylbm -c conda-forge 与Pypi pip install pylbm 或者 pip install pylbm --user 从来源

Heterogeneous Computing with OpenCL 2.0 teaches OpenCL and parallel programming for complex systems that may include a variety of device architectures: multi-core CPUs, GPUs, and fully-integrated Accelerated Processing Units (APUs). This fully-revised edition includes the latest enhancements in OpenCL 2.0 including: • Shared virtual memory to increase programming flexibility and reduce data transfers that consume resources • Dynamic parallelism which reduces processor load and avoids bottlenecks • Improved imaging support and integration with OpenGL Designed to work on multiple platforms, OpenCL will help you more effectively program for a heterogeneous future. Written by leaders in the parallel computing and OpenCL communities, this book explores memory spaces, optimization techniques, extensions, debugging and profiling. Multiple case studies and examples illustrate high-performance algorithms, distributing work across heterogeneous systems, embedded domain-specific languages, and will give you hands-on OpenCL experience to address a range of fundamental parallel algorithms. Updated content to cover the latest developments in OpenCL 2.0, including improvements in memory handling, parallelism, and imaging support Explanations of principles and strategies to learn parallel programming with OpenCL, from understanding the abstraction models to thoroughly testing and debugging complete applications Example code covering image analytics, web plugins, particle simulations, video editing, performance optimization, and more

dwave-ocean-sdk D-Wave海洋工具的安装程序。 安装 兼容Python 3.5+： pip install dwave-ocean-sdk 要从源代码安装： python setup.py install 下一步 有关配置求解器和使用Ocean工具的信息，请参见 。