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solutions of problems of advance computer architecture and parallel processing

hi,
are there anyone have solutions of problems of advance computer
architecture and parallel processing for HESHAM EL-REWINI

thanks in advance
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3/21/2010 8:58:08 PM
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CALL FOR PAPERS -- Special Session on Massively Parallel Processing at the 9th Workshop on Advances in Parallel and Distributed Computational Models -- IPDPS
*** CALL FOR PAPERS *** 2007 International Parallel & Distributed Processing Symposium Workshop on Advances in Parallel and Distributed Computational Models Special Session on Massively Parallel Processing *** Submission Deadline December 4th 2006 *** As part of the Workshop on Advances in Parallel and Distributed Computing Models (APDCM), the aim of this special session is to focus on computer systems that can scale to many thousands of processing elements and are used to solve a single problem. The focus is on identifying new and novel ideas rather than proving incremental advances. By concurrently exploring architecture, programming models, algorithms and applications, the session seeks to advance the state-of-the-art of Massively Parallel Processing (MPP) systems. Following the usual IPDPS practice, all MPP papers will be published in the Proceedings of the IEEE/ACM International Parallel and Distributed Processing Symposium (IPDPS). Topics of Interest: The topics of interest to this special session are: (+) Architectures and Experimental Systems: the use of increased parallelism on a single chip (MPP on a chip), the coordination and communication between massive numbers of processing elements, the use of processors in memory (PIMS), and parallelism in emerging technologies. (+) Large-scale Computing: the utilization of MPPs for large-scale computing, achieving peta-scale levels of processing, use of heterogeneous processing capabilities. (+) Paralleli...

CALL FOR PAPERS -- Special Session on Massively Parallel Processing at the 9th Workshop on Advances in Parallel and Distributed Computational Models -- IPDPS
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Is the parallel computing toolbox the solution to this problem?
Hi, I am designing a feedback control loop as part of a project. The MATLAB program reads data through the analog input of a DAC (NI USB 6009), and when there is enough data (say N), it starts to process the data to get a control output and send it through the digital output of the DAC. The reading should be kept going while processing the control signal; and the control signal calculation is refreshed when every time when N/3 data comes in (like a moving window of size N, averaging the old data with the new ones) My problem is how to perform these in parallel? This doesn't need t...

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need to compute this problem having problems with how to start this problem help need urgently
Let   U[0; 2theta] be a uniform random variable from the interval [0; 2theta] and let A  Exp(1) be exponentially distributed with mean 1. Assume  and A independent. Compute the mean mX(t) =E[X(t)] and autocorrelation RX(s; t) = E[X(s)X(t)] of the phase-shifted sinusoid.X(t) = A*  cos(t +theta ): State also if X(t) is Wide Sense Stationary (WSS). plot 10 realisations of X(t) plotR(s-t,0)as a function of s-t "pramod kumar" <pramod.kilu@gmail.com> wrote in message <jpapso$44b$1@newscl01ah.mathworks.com>... > Let   U[0; 2theta] be a uniform random variable from the interval [0; 2theta] and let A  Exp(1) be exponentially distributed with mean 1. Assume  and A independent. Compute the mean mX(t) =E[X(t)] and autocorrelation RX(s; t) = E[X(s)X(t)] of the phase-shifted sinusoid.X(t) = A*  cos(t +theta ): > State also if X(t) is Wide Sense Stationary (WSS). > plot 10 realisations of X(t) > plotR(s-t,0)as a function of s-t So what have you tried? If you have not tried anything, this is a suggestion that you were not paying attention in class. Make an effort. John "pramod kumar" <pramod.kilu@gmail.com> wrote in message <jpapso$44b$1@newscl01ah.mathworks.com>... > Let   U[0; 2theta] be a uniform random variable from the interval [0; 2theta] and let A  Exp(1) be exponentially distributed with mean 1. Assume  and A independent. Compute the mean mX(t) =E[X(t)] and autocorrelation RX(s; t) = E[X(s)X(t)]...

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