{"product_id":"concurrent-numpy-in-python-faster-numpy-with-blas-python-threads-and-multiprocessing-paperback-1","title":"Concurrent NumPy in Python: Faster NumPy With BLAS, Python Threads, and Multiprocessing - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eJason Brownlee\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003cb\u003eConcurrency in NumPy is not an afterthought\u003c\/b\u003e\u003c\/p\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\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 476\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.96 x 9 x 6 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e September 21, 2023\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":46586016039109,"sku":"9798862038057","price":58.95,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0757\/6718\/5605\/files\/MpCUUodfgW9798862038057_ee43f6d7-72cf-42ec-bb69-95829b3d4dbd.webp?v=1784884595","url":"https:\/\/selloorium.com\/products\/concurrent-numpy-in-python-faster-numpy-with-blas-python-threads-and-multiprocessing-paperback-1","provider":"Selloorium","version":"1.0","type":"link"}