Download Introduction to Parallel Processing: Algorithms and by Behrooz Parhami PDF

By Behrooz Parhami

This unique textual content presents entire assurance of parallel algorithms and architectures, starting with primary techniques and carrying on with via architectural diversifications and points of implementation. in contrast to the authors of comparable texts, Professor Parhami experiences the circuit version and problemdriven parallel machines, variations of mesh architectures, and composite and hierarchical structures, between different matters. With its balanced remedy of conception and useful designs, classtested lecture fabric and difficulties, and worthwhile case reports, the ebook is desirable to graduate and upperlevel undergraduate scholars of complicated structure or parallel processing.

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Extra resources for Introduction to Parallel Processing: Algorithms and Architectures

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Version E/O corresponds to each odd-numbered node being unit-time and each even-numbered node taking twice as long. a. b. c. d. e. Convert the E/O version to an equivalent V version where each node is unit-time. Find the maximum attainable speed-up for each of the U and V versions. What is the minimum number of processors needed to achieve the speed-ups of part (b)? What is the maximum attainable speed-up in each case with three processors? Which of the U and V versions of the task graph would you say is “more parallel” and why?

For example, banks and airlines, among others, use transaction processing systems that handle large volumes of data. Higher computational power, or solving larger problems. , 5-day, as opposed to 24-hour, weather forecasting). All three aspects above are captured by a figure-of-merit often used in connection with parallel processors: the computation speed-up factor with respect to a uniprocessor. The ultimate efficiency in parallel systems is to achieve a computation speed-up factor of p with p processors.

2, assuming that a microprocessor’s FLOPS rating is roughly half of its MIPS rating. Compare and discuss the observed trends. 3. 6 shows that in the control-parallel implementation of the sieve of Eratosthenes algorithm, a single processor is always responsible for sieving the multiples of 2. For n = 1000, this is roughly 35% of the total work performed. 48. Yet, for p = 2, we note a speed-up of about 2 in Fig. 6. What is wrong with the above reasoning? 4. Sieve of Eratosthenes Consider the data-parallel implementation of the sieve of Eratosthenes algorithm for n = 106.

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