by Project Mac, Massachusetts Institute of Technology in Cambridge .
Written in English
|Statement||[by] Stephen W. Smoliar.|
|Contributions||Project MAC (Massachusetts Institute of Technology)|
|LC Classifications||MT58 .S64|
|The Physical Object|
|Number of Pages||275|
|LC Control Number||74153456|
A Parallel Processing Model of Musical Structures. Author(s) Smoliar, Stephen W. DownloadAITRps (Mb) Euterpe is a real-time computer system for the modeling of musical structures. It provides a formalism wherein familiar concepts of musical analysis may be readily expressed. This is verified by its application to the analysis of a Cited by: When such a system does not provide the performance requirements, pipelined and parallel process ing structures can be employed. The concept of parallel processing is a depar ture from sequential processing. In sequential computation one processor is in volved and performs one operation at a time. It presents a few models of parallel computation that either are extensions of the von Neumann model or are developed especially for parallel computations. Parallel processing has great flexibility that causes many programming problems, but permits parallelism to be analyzed at several levels of complexity. Purchase Parallel Processing from Applications to Systems - 1st Edition. Print Book & E-Book. ISBN ,
Moving beyond the sequential algorithms and data structures of the earlier related title, this book takes into account the paradigm shift towards the parallel processing required to solve modern performance-critical applications and how this impacts on the teaching of algorithms. Stream processing is a computer programming paradigm, equivalent to dataflow programming, event stream processing, and reactive programming, that allows some applications to more easily exploit a limited form of parallel applications can use multiple computational units, such as the floating point unit on a graphics processing unit or field-programmable gate arrays (FPGAs. software design issues in parallel processing, thus resulting in new designs and products with mass-market appeal. Given the computation-intensive nature of many application areas (such as encryption, physical modeling, and multimedia), parallel processing will continue to thrive for years to come. The combination of signal processing and intelligent systems in hybrid structures rather than serial or parallel processing provide the best mechanism that is a better fit with the comprehensive nature of human. This book is a practical reference that places the emphasis on principles and applications of DSP in digital systems.
gorithms, and languages makes a data-parallel programming model desirable for any kind of tightly-coupled parallel or vector machine, including multiple-instruction multiple-data (MIMD) machines. 2. The range of applications and algorithms that can be described using data-parallel programming is extremely broad, much broader than is often expected. In computing, a parallel programming model is an abstraction of parallel computer architecture, with which it is convenient to express algorithms and their composition in value of a programming model can be judged on its generality: how well a range of different problems can be expressed for a variety of different architectures, and its performance: how efficiently the compiled. Abstract. Graphics processing unit (GPU) is an electronic circuit which manipulates and modifies the memory for better image output. Deep learning involves huge amounts of matrix multiplications and other operations which can be massively parallelized and thus sped up on GPUs. Guide is intended to help you choose among the different parallel processing and computational methods, and ultimately increase the performance of analysis by reducing CPU time, memory and disk space requirements. This main material in this book covers the parallel processing .