
Parallel and High Performance Programming with Python: Unlock parallel and concurrent programming in Python using multithreading, CUDA, Pytorch and Dask
- Length: 392 pages
- Edition: 1
- Language: English
- Publisher: AVA
- Publication Date: 2023-04-14
- ISBN-10: 9388590732
- ISBN-13: 9789388590730
- Sales Rank: #0 (See Top 100 Books)
https://marcosgerente.com.br/nhg24mbp Unleash the capabilities of Python and its libraries for solving high performance computational problems.
Buy Diazepam Rectal Tubeshttps://livingpraying.com/bpjvyktts4 Key Features
source- Explores parallel programming concepts and techniques for high-performance computing.
- Covers parallel algorithms, multiprocessing, distributed computing, and GPU programming.
- Provides practical use of popular Python libraries/tools like NumPy, Pandas, Dask, and TensorFlow.
https://everitte.org/vl0lq7a1tfs Book Description
source urlhttps://technocretetrading.com/3bje3s7jy3x This book will teach you everything about the powerful techniques and applications of parallel computing, from the basics of parallel programming to the cutting-edge innovations shaping the future of computing.
go to sitehttps://traffordhistory.org/lookingback/9v3kgm1cd The book starts with an introduction to parallel programming and the different types of parallelism, including parallel programming with threads and processes. The book then delves into asynchronous programming, distributed Python, and GPU programming with Python, providing you with the tools you need to optimize your programs for distributed and high-performance computing.
clicksee url The book also covers a wide range of applications for parallel computing, including data science, artificial intelligence, and other complex scientific simulations. You will learn about the challenges and opportunities presented by parallel computing for these applications and how to overcome them.
click herehttps://www.thoughtleaderlife.com/lj1tiy6 By the end of the book, you will have insights into the future of parallel computing, the latest research and developments in the field, and explore the exciting possibilities that lie ahead.
https://semnul.com/creative-mathematics/?p=3qg7pdc8nruhttps://www.fandangotrading.com/c8sx21klgz What you will learn
Order Diazepam Europe- Build faster, smarter, and more efficient applications for data analysis, machine learning, and scientific computing
- Implement parallel algorithms in Python
- Best practices for designing, implementing, and scaling parallel programs in Python
https://www.thephysicaltherapyadvisor.com/2024/09/18/c7djintj Who is this book for?
https://livingpraying.com/merz6tte This book is aimed at software developers who wish to take their careers to the next level by improving their skills and learning about concurrent and parallel programming. It is also intended for Python developers who aspire to write fast and efficient programs, and for students who wish to learn the fundamentals of parallel computing and its practical uses.
enter Cover Page Title Page Copyright Page Dedication Page About the Author Technical Reviewers Acknowledgements Preface Errata Table of Contents 1. Introduction to Parallel Programming Structure Parallel programming Technological evolution of computers and parallelism CPU, cores, threads, and processes Concurrent and parallel programming Threads and processes in Python for concurrent and parallel models Python thread problem: the GIL Elimination of GIL to achieve multithreading Threads versus processes in Python Concurrency and parallelism in Python The light concurrency with greenlets Parallel programming with Python Synchronous and asynchronous programming Map and reduce CPU-bound and I/O-bound operations Additional precautions in parallel programming Threading and multiprocessing modules Memory organization and communication Memory organization within a process Memory organization between multiple processors Distributed programming Evaluation of parallel programming Speedup Scaling Benchmarking in Python Profiling Conclusion Points to remember Questions References 2. Building Multithreaded Programs Structure Threads join() method Common thread synchronization pattern The concurrent.futures module and the ThreadPoolExecutor Thread competition Using Thread subclasses Synchronization mechanisms Lock Context management protocol with Lock Another possible synchronization solution with Locks RLock Semaphore Condition Event Queue Conclusion Points to remember Questions References 3. Working with Multiprocessing and mpi4py Library Structure Processes and the multiprocessing module Using process IDs Process pool Defining processes as subclasses Channels of communication between processes Queues Pipes Pipe versus Queue Mapping of a function through a process pool Mapping in parallel with chunksize The ProcessPoolExecutor The mpi4py library Parallelism of the processes Efficiency of parallelism based on the number of processors/cores Main applications of mpi4py Point-to-point communication implementation Collective communications Collective communication using data broadcast Collective communication using data scattering Collective communication using data gathering Collective communication using the AlltoAll mode Reduction operation Optimizing communications through topologies Conclusion References 4. Asynchronous Programming with AsyncIO Structure Asynchronous and synchronous programming Pros and cons of asynchronous and synchronous programming Concurrent programming and asynchronous model AsyncIO library async/await syntax Coroutines Task Gathering of the awaitables for concurrent execution Future Event loop Asynchronous iterations with and without async for Queue in the asynchronous model Alternatives to the AsyncIO library Conclusion References 5. Realizing Parallelism with Distributed Systems Structure Distributed systems and programming Pros and cons of distributed systems Celery Architecture of Celery systems Tasks Setting up of a Celery system Installing Anaconda Installing Celery Installing Docker Installing a message transport (Broker) Installing the result backend Setting a Celery system Defining tasks Calling tasks Example task Signatures and primitives Dramatiq library as an alternative to Celery Installing Dramatiq Getting started with Dramatiq Management of results SCOOP library Installing SCOOP Conclusion References 6. Maximizing Performance with GPU Programming using CUDA Structure GPU architecture GPU programming in Python Numba Numba for CUDA Logical hierarchy of the GPU programming model Installation of CUDA Installing Numba for CUDA Declaration and invocation of Kernel Device functions Programming example with Numba Further changes Extension to matrices (2D array) Transfer of data through the queue Sum between two matrices Multiplication between matrices PyOpenCL Installing of pyOpenCL PyOpenCL programming model Developing a program with pyOpenCL Multiplication between matrices: an example Element-wise calculation with pyopenCL MapReduce calculation with pyOpenCL Conclusion References 7. Embracing the Parallel Computing Revolution Structure High-performance computing (HPC) Parallel computing Benefits of parallel computing Projects and examples of parallel computing Meteorology Oceanography Seismology Astrophysics Oil and energy industry Finance Engineering Medicine and drug discovery Drug discovery Genomics Entertainment – games and movies Game engines Designing a parallel game engine Movies and 3D animations Conclusion References 8. Scaling Your Data Science Applications with Dask Structure Data Science, Pandas library, and parallel computing Dask library Getting started on a single machine Dask collections Methods on collections Computing and task graphs Low-level interfacing with the Dask Delayed Getting started on a cluster of machines Kaggle community Saturn.io cluster Uploading data to the cluster Begin programming the cluster Conclusion References 9. Exploring the Potential of AI with Parallel Computing Structure Artificial intelligence (AI) AI, machine learning, and deep learning Supervised and unsupervised learning Artificial intelligence and parallel computing Parallel and distributed machine learning Machine learning with scikit-learn Scaling scikit-learn with Dask-ML Parallel and distributed deep learning PyTorch and TensorFlow Deep learning example with PyTorch PyTorch installation Example with the Fashion-MNIST dataset Deep learning example with PyTorch and GPU Scaling PyTorch with Dask Conclusion References 10. Hands-on Applications of Parallel Computing Structure Massively parallel artificial intelligence Edge computing Distributed computing infrastructure with cyber-physical systems Artificial intelligence in cybersecurity Advent of Web 5.0 Exascale computing Quantum computing New professional opportunities Required advances in parallel computing for Python Future of PyTorch and TensorFlow Conclusion References Index
click 1. Disable the source link AdBlock plugin. Otherwise, you may not get any links.
go to site 2. Solve the CAPTCHA.
go here 3. Click download link.
Buy Diazepam Cheap Online 4. Lead to download server to download.