{"product_id":"concurrent-numpy-in-python-faster-numpy-with-blas-python-threads-and-multiprocessing-9798862038057","title":"Concurrent NumPy in Python: Faster NumPy With BLAS, Python Threads, and Multiprocessing","description":"\u003cb\u003eConcurrency in NumPy is not an afterthought\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eDiscover matrix multiplication that is 2.7x faster.\u003c\/li\u003e\n\u003cli\u003eDiscover array initialization that is up to 3.2x faster.\u003c\/li\u003e\n\u003cli\u003eDiscover sharing copied arrays that is up to 516.91x faster.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003eNumPy is how we represent arrays of numbers in Python. \u003cp\u003e\u003c\/p\u003eAn entire ecosystem of third-party libraries has been developed around NumPy arrays, from machine learning and deep learning to image and computer vision and more. \u003cp\u003e\u003c\/p\u003eGiven the wide use of NumPy, it is essential we know how to get the most out of our system when using it. \u003cp\u003e\u003c\/p\u003eWe cannot afford to have CPU cores sit idle when performing mathematical operations on arrays. \u003cp\u003e\u003c\/p\u003eTherefore we must know how to correctly harness concurrency in NumPy, such as: \u003cul\u003e\n\u003cli\u003eNumPy has multithreaded algorithms and functions built-in (using BLAS).\u003c\/li\u003e\n\u003cli\u003eNumPy will release the infamous GIL so Python threads can run in parallel.\u003c\/li\u003e\n\u003cli\u003eNumPy arrays can be shared efficiently between Python processes using shared memory.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003eThe problem is, no one is talking about how. \u003cp\u003e\u003c\/p\u003eIntroducing: \"\u003cb\u003eConcurrent NumPy in Python\u003c\/b\u003e\". A new book designed to teach you how to bring concurrency to your NumPy programs in Python, super fast! \u003cp\u003e\u003c\/p\u003eYou will get fast-paced tutorials showing you how to bring concurrency to the most common NumPy tasks. \u003cp\u003e\u003c\/p\u003eIncluding: \u003cul\u003e\n\u003cli\u003eParallel array multiplication, common math functions, matrix solvers, and decompositions.\u003c\/li\u003e\n\u003cli\u003eParallel array filling and parallel creation of arrays of random numbers.\u003c\/li\u003e\n\u003cli\u003eParallel element-wise array arithmetic and common array math functions\u003c\/li\u003e\n\u003cli\u003eParallel programs for working with many NumPy arrays with thread and process pools.\u003c\/li\u003e\n\u003cli\u003eEfficiently share arrays directly, and copies of arrays between Python processes.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003eDon't worry if you are new to NumPy programming or concurrency, you will also get primers on the background required to get the most out of this book, including: \u003cul\u003e\n\u003cli\u003eThe importance of concurrency when using NumPy and the cost of approaching it naively.\u003c\/li\u003e\n\u003cli\u003eHow to perform common NumPy operations and math functions.\u003c\/li\u003e\n\u003cli\u003eHow to install, query, and configure BLAS libraries for built-in multithreaded NumPy functions.\u003c\/li\u003e\n\u003cli\u003eHow to use Python concurrency APIs including threading, multiprocessing, and pools of workers.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003eEach tutorial is carefully designed to teach one critical aspect of how to bring concurrency to your NumPy projects. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eLearn Python concurrency correctly, step-by-step.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Jason Brownlee\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 09\/21\/2023\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 476\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.39lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.00h x 6.00w x 0.96d\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9798862038057\u003cp\u003e\u003ci\u003eThis title is not returnable\u003c\/i\u003e\u003cbr\u003e\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":43950461452403,"sku":"9.79886E+12","price":35.95,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0555\/9255\/0515\/files\/img_a240ff5e-d4e8-4e82-8cbc-c9e3768ede0f.jpg?v=1759406817","url":"https:\/\/bookstorenmore.com\/en-de\/products\/concurrent-numpy-in-python-faster-numpy-with-blas-python-threads-and-multiprocessing-9798862038057","provider":"Bookstore N More","version":"1.0","type":"link"}