Parallel processing

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  • Parallel Processing & Distributed Systems: Lecture 1 Introduction What is parallel processing? Why do we use parallel processing? Applications, Parallelism, Parallelism, Application Demands, Grand Challenge Problems, Parallel Processing Terminology.

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  • Parallel Processing & Distributed Systems: Lecture 2 - MPI presents about Communication modes; MPI – Message Passing Interface Standard, Compiling MPI Programs, Executing MPI Programs, Terminating LAM; MPI_Init, MPI_Comm_Size, MPI_Comm_Rank,...

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  • Parallel Processing & Distributed Systems: Lecture 3 - Abstract Machine Models includes Parallel Computer Models, Abstract Machine Models, Time Complexity Problem, Conflicts Resolution Schemes, PRAM Algorithm, Parallel Reduction.

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  • Parallel Processing & Distributed Systems: Lecture 4 - Parallel Computer Architectures provides about Flynn’s Taxonomy, Classification of Parallel Computers Based on Architectures, Classification based on Architecture.

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  • Parallel Processing & Distributed Systems: Lecture 5 - Speedup includes about Speedup & Efficiency, Amdahl’s Law (Fixed Problem Size, Enhanced Amdahl’s Law), Gustafson’s Law, Sun & Ni’s Law and something esle.

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  • Parallel Processing & Distributed Systems: Lecture 6 - Processor Organization presents about Criteria (Diameter, bisection width, number of edges per node maximum edge length), Processor Organizations (Mesh, binary tree, hypertree, pyramid, butterfly, hypercube, shuffle-exchange).

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  • Parallel Processing & Distributed Systems: Lecture 7 - Parallel Job Schedulings presents about Scheduling on UMA Scheduling on UMA, Deterministic model, Gantt chart, Optimal schedule, Graham’s list scheduling algorithm, Classes of scheduling.

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  • Parallel Processing & Distributed Systems: Lecture 8 - Parallel Paradigms & Programming Models presents about Parallel programming paradigms, Programmability issues, Parallel programming models (Implicit parallelism, Explicit parallel models, Other programming models).

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  • Parallel Processing & Distributed Systems: Lecture 9 - Parallel Algorithms presents about Introduction to parallel algorithms development, Reduction algorithms, Broadcast algorithms, Prefix sums algorithms.

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  • Parallel Processing & Distributed Systems: Lecture 10 - Matrix Multiplication includes Sequential matrix multiplication, Algorithms for processor arrays (Matrix multiplication on 2-D mesh SIMD model, Matrix multiplication on hypercube SIMD model), Matrix multiplication on UMA multiprocessors, Matrix multiplication on multicomputers.

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  • Parallel Processing & Distributed Systems: Lecture 11 - Distributed System presents about Introduction, Definition of a Distributed System, Computers vs. Web Servers in the Internet, Potentials and Challenges, Transparency in a Distributed System, Scalability Problems.

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  • Parallel Processing & Distributed Systems: Lecture 12 provides about Communication, Communication Protocols, Layered Protocols, Client-Server TCP, Middleware Protocols, Client-Server Communication Model, Issues in Client-Server Communication, Addressing Issues.

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  • Single processor supercomputers have achieved great speeds and have been pushing hardware technology to the physical limit of chip manufacturing. But soon this trend will come to an end, because there are physical and architectural bounds, which limit the computational power that can be achieved with a single processor system. In this book, we study advanced computer architectures that utilize parallelism via multiple processing units.

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  • In this chapter discuss the pipelining and parallel processing. The main contents of this chapter include all of the following: Pipelining of FIR digital filters, parallel processing, pipelining and parallel processing for low power.

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  • While speech recognition systems have come a long way in the last thirty years, there is still room for improvement. Although readily available, these systems are sometimes inaccurate and insufficient. The research presented here outlines a technique called Distributed Listening which demonstrates noticeable improvements to existing speech recognition methods. The Distributed Listening architecture introduces the idea of multiple, parallel, yet physically separate automatic speech recognizers called listeners. Distributed Listening also uses a piece of middleware called an interpreter.

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  • This book is a collection of 19 excellent works presenting different applications of several MATLAB tools that can be used for educational, scientific and engineering purposes. Chapters include tips and tricks for programming and developing Graphical User Interfaces (GUIs), power system analysis, control systems design, system modelling and simulations, parallel processing, optimization, signal and image processing, finite different solutions, geosciences and portfolio insurance. Thus, readers from a range of professional fields will benefit from its content....

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  • Oracle8i Parallel Server Concepts and Administration contains information that describes the features and functionality of the Oracle8i and the Oracle8i Enterprise Edition products. Oracle8i and the Oracle8i Enterprise Edition have the same basic features. However, several advanced features are available only with the Oracle8i Enterprise Edition, and some of these are optional. For example, to use client application failover, you must have the Enterprise Edition and the Parallel Server Option....

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  • Research Article The Acoustic and Peceptual Effects of Series and Parallel Processing

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  • Lecture VLSI Digital signal processing systems - Chapter 5 discuss the unfolding. The main contents of this chapter include: Algorithm for unfolding, applications of unfolding, sample period reduction, parallel processing,... Inviting you refer.

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  • Chapter 10 introduce the pipelined and parallel pecursive and adaptive filters. This chapter includes content: Pipelining in 1st-Order IIR Digital Filters, Pipelining in Higher-Order IIR Digital Filters, Parallel Processing for IIR Filters, Combined Pipelining and Parallel Processing for IIR Filters.

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