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The included OPEN DESIRE program for Linux solves up to 40,000 ordinary differential equations and implements exceptionally fast and convenient vector operations A smaller educational 20,000 differential-equations version for Microsoft WindowsTM can be obtained without charge from the author by sending an email to gatmkorn@aolcom The user can run, edit, and modify the example simulations keyed to the figures in this book, plus many other examples Many of our programming principles also apply to simulation programs other than DESIRE 1 introduces our subject with a few programs for small differential-equation models, including a simple guided-missile simulation The remainder of the book presents new material 2 begins with a careful discussion of models that involve sampled-data operations and sampled-data difference equations together with differential equations We model mixed analog digital systems such as simulated digital controllers and systems with limiters and switches At this point, we show that many very useful devices (eg, track-hold circuits, trigger circuits, signal generators, automatic scaling) are neatly and efficiently modeled with simple difference equations Last, but not least, we propose improved techniques for proper numerical integration of switched variables Truly powerful simulation programs need a readable notation for vector and matrix assignments, differential equations, and difference equations 3 introduces a novel vector compiler that produces very fast programs for vector and matrix operations We also demonstrate efficient use of submodels We present examples from control engineering and nuclear-reactor simulation The following chapter then shows how we use vectors to replicate complete models 4 describes practical model replication (vectorization): a single simulation run with a vector model will replace hundreds or thousands of conventional simulation runs We apply the vectorizing compiler introduced in 3 to parameter-influence studies and Monte Carlo simulation of dynamic systems with noise-perturbed parameters and random initial conditions We compare repeated-run and vectorized Monte Carlo studies of a weapon trajectory Our interactive programs produce not just time histories of system variables but also time histories of statistics such as averages, mean squares, and probability estimates We also show explicitly how to estimate probability densities 5 discusses more difficult vectorized Monte Carlo simulations involving time-varying noise, which has to be derived from periodic pseudorandom noise samples Examples include Monte Carlo simulation of a continuous random walk, another trajectory study, and two vectorized control-system simulations An inexpensive 24-GHz personal computer exercised 1000 random-input control-system models in seconds We also describe a new heuristic test for the quality of pseudorandom noise.



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Figure 5.12 Rebuild time versus media block size (Quantum Atlas-10K)

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6 discusses vector models of neural networks including a new pulsed-neuron model 7 deals with vectorized programs for fuzzy-set controllers, partial differential equations, and agroecological models replicated at many points of a landscape map The Appendix gets a small selection of reference material out of the way of the main text GRANINO A KORN Chelan, Washington October 2006





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In this chapter we have investigated two algorithms for rebuilding data lost in a failed disk to a spare disk automatically and transparently. We rst presented a block-based rebuild algorithm derived from the conventional CSCAN disk scheduler and analyzed its performance. A buffersharing scheme was then introduced to eliminate the additional buffer requirement during rebuild. Next, we presented a track-based rebuild algorithm that can reduce the rebuild time by 70 80%. The large buffer requirement incurred in track-based rebuild is then reduced to insigni cant levels by a novel pipelined rebuild algorithm. Numerical results show that it is feasible to completely rebuild a failed disk using the presented rebuild algorithms in a practical amount of time without causing any performance degradation to the media server. While the algorithms presented in this chapter are designed for media servers serving CBR media streams, they can also be extended to media servers serving variable-bit-rate (VBR) media streams. One possibility is to replace xed-size block retrievals with variable-size block retrievals, with the block size corresponding to the video bit-rate. As long as the sizes of the blocks to be retrieved in a disk round is known, we can use the same worst-case analysis as in Section 5.5 to determine how much disk time to allocate for rebuild. The rest of the rebuild process will be similar.

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