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would most certainly use a computer to do this For this sample size, the binomial distribution is very normal-like with a maximum at the expected value, np, and standard deviation n p (1 p) In either case here, n = 100 If p = 050, we the probability distribution nd centered about np = 100 050 = 50 with standard deviation 100 050 050 = 5 Since we are seeking a critical region in the upper tail of this distribution, we look at values of X at least one standard deviation from the mean, so we start at X = 55 We show some probabilities in Table 92 Table 92 Critical region X 56 X 57 X 58 X 59 X 60 X 61 X 62 01356 00967 00443 00284 00176 00105 00060 00000 00000 00001 00001 00003 00007 00014

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We see that decreases as we move to the right on the probability distribution and that increases We have suggested various critical regions here and determined the resulting values of the errors This raises the possibility that the size of one of the errors, say , is chosen in advance and then a critical region found that produces this value of The consequence of this is shown in Table 92 It is not possible, for example, to choose = 005 and nd an appropriate critical region This is because the random variable is discrete in this case If the random variable were continuous, then it is possible to specify in advance We show how this is so in the next example

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Notice that when it comes time to wait on mre, some application specific event of interest, we also pass in m_c a n c e l E v e n t When the wait returns, we check to see if the thread was awakened because the cancellation event was signaled If so, we treat it as if we witnessed m_i s C a n c e l e d as t r u e and break out of the loop, terminating the work This ensures we are disciplined about the termination of the work and have an opportunity to ensure appli cation data is not left in an invalid state

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We focused primarily on data and task parallelism in this chapter, the two most common kinds of parallelism you are apt to encounter in real-world programs We saw some useful patterns, such as parallel for loops, reduc tions, sorts, fork /join, and divide and conquer Once these concepts are known, applying them to particular problems becomes far simpler Mes sage based parallelism is quite common too, but due to the lack of a single standard programming model, we did not spend too much time reviewing the common patterns In the next chapter, we'll focus on the motivation for most of this dis cussion: performance and scalability In it, concepts like parallel speedups and efficiencies will be reviewed, which are useful success metrics for most of the ideas presented in this chapter

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The breaking strength of steel wires used in elevator cables is a crucial characteristic of these cables The cables can be assumed to come from a population with known = 400 lb Before accepting a shipment of these steel wires, an engineer wants to be con dent that >10,000 lb A sample of 16 wires is selected and their mean breaking strength X is measured It would appear sensible to test the null hypothesis H0 : = 10,000 lb against the alternative Ha : < 10,000 A test will be based on the sample mean, X The central limit theorem tells us that X N In this case, we have X N 400 , 16 = N ( , 100) , n

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