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Practically and deeply understand concurrency in Python to write efficient programsKey Features\[\*\] Build highly efficient, robust, and concurrent applications\[\*\] Work through practical examples that will help you address the challenges of writing concurrent code\[\*\] Improve the overall speed of execution in multiprocessor and multicore systems and keep them highly availableBook
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Build high-performance applications for scientific computing

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Embedded Computing for High Performance

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Embedded Computing for High Performance: Design Exploration and Customization Using High-level Compilation and Synthesis Tools provides a set of real-life example implementations that migrate traditional desktop systems to embedded systems. Working with popular hardware, including Xilinx and ARM, the book offers a comprehensive description of techniques for mapping computations expressed in ... Read more

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Topics in Parallel and Distributed Computing provides resources and guidance for those learning PDC as well as those teaching students new to the discipline. The pervasiveness of computing devices containing multicore CPUs and GPUs, including home and office PCs, laptops, and mobile devices, is making even common users dependent on parallel processing. Certainly, it is no longer sufficient for
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