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**1 - 4**of**4**### Simulating complex SCI topologies

"... This paper shows how rather complex SCI topologies might be constructed and simulated using our present SCI simulator. We also present the serial HIC technology developed in the European OMI/HIC project. A HIC network may be used to transport SCI packets, and our new HIC network simulator under deve ..."

Abstract
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This paper shows how rather complex SCI topologies might be constructed and simulated using our present SCI simulator. We also present the serial HIC technology developed in the European OMI/HIC project. A HIC network may be used to transport SCI packets, and our new HIC network simulator under development is presented as it is intended to simulate such HIC networks. Keywords--- Scalable Coherent Interface (SCI), Simulations, HIC Technology, Serial links, Topologies I. Introduction N ODES in an SCI topology are designed to form ringlets. However, ringlet structures are sensitive to hardware failures, and their peak load is limited; they are not truly scalable [1]. Having access to switches that enable traffic to be directed from one ringlet to another, one may form rather complex networks from quite small ringlets [2], [3], [4]. Ideally, every sending node in the topology should be capable of connecting to any destination node without blocking. We call this network a crossbar networ...

### Commonly Used Distributions

"... umbers. (b) Return the the a smallest number as BT(a# b). 3. If a and b are less than one: (a) Generate two uniform U(0,1) random numbers u (b) Let x = u and y = u . If (x + y) ? 1, go back to the previous step. Otherwise, return x=(x + y) as BT(a# b). 4. If a and b are greater than 1: Us ..."

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umbers. (b) Return the the a smallest number as BT(a# b). 3. If a and b are less than one: (a) Generate two uniform U(0,1) random numbers u (b) Let x = u and y = u . If (x + y) ? 1, go back to the previous step. Otherwise, return x=(x + y) as BT(a# b). 4. If a and b are greater than 1: Use rejection Binomial Distribution ffl The number of successes x in a sequence of n Bernoulli trials has a binomial distribution. ffl Characteristics: p = Probability of success in a trial, 0 ! p ! 1. n = Number of trials# n must be a positive integer. 2. Range: x = 0# 1# : : : # n B B B B C C C C n;x 4. Mean: np 5. Variance: np(1 ; p) successes 1. The number of processors that are up in a multiprocessor system. 2. The number of packets that reach the destination without loss. 3. The number of bits in a packet that are not affected by noise. 4. The number of items in a batch that have certain characteristics. ffl Variance ! Mean ) Binomial Variance ? Mean ) Negative Binomial Va