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The main goal of this graduate-level text is to provide a language for understanding, unifying, and implementing a wide variety of algorithms for digital signal processing -- in particular, to provide rules and procedures that can simplify or even automate the task of writing code for the newest parallel and vector machines. It thus bridges the gap between digital signal processing algorithms and their implementation on a variety of computing platforms. The mathematical concept of tensor product is a recurring theme throughout the book: tensor product factors have a direct interpretation on many vector and parallel computers and tensor product identities can be matched to machine implementation. These formulations also highlight the data flow, which is especially important on supercomputers, where data flow may be the factor limiting the efficiency of a computation. Because of its importance in many applications, much of the discussion centers on algorithms related to the finite Fourier transform and to multiplicative FFT algorithms; other topics covered include convolution algorithms and prime-factor algorithms. This second edition has been revised and brought up to date throughout. |
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 Performance by Design: Computer Capacity Planning By ExamplePractical systems modeling: planning performance, availability, security, and more
Computing systems must meet increasingly strict Quality of Service (QoS) requirements for performance, availability, security, and maintainability. To achieve these goals, designers, analysts, and capacity planners need a far more thorough understanding of... | | | | Mac OS X Developer's GuideAs you begin developing for Mac OS X, you'll face major changes, lots of difficult decisions, and also some terrific opportunities. The Mac OS X Developer's Guide aims to do more than help you make the transition painlessly: It's dedicated to helping you capitalize on these opportunities and positively thrive in this new environment.... |
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