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Because of this simple translation, we will not discuss the second style The only advantage to writing it longhand is to avoid the double delegate invocation per iteration But it is implied that the same parallelization tech niques apply Different techniques are typically needed for loops that aren't based on indices (such as wh i l e loops) and for code that iterates over collection data structures that do not offer random access indexers We'll encounter such a situation later when we deal with NET I E n ume r a b l e < T > inputs where the size of the input isn' t even known

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Exhibit 22 Employee Number 00417 00612 00058 01023 03048 03401 02208 Employee Name Smith, J Avery, Q Jones, L Dennison, A Grumman, O Smith, J Botha, TL

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Before discussing how to run these loops in parallel, it should be made clear that a necessary prerequisite to parallelizing is that the loop's body is thread safe If it isn't, running it in parallel is sure to cause trouble In our previous example, that means that all code run inside of the body delegate must be thread safe Being thread safe isn't enough for our purposes, however Thread safety means that it's correct to run separate iterations in parallel (which is impor tant); but thread safety might just involve body acquiring a lock for the duration of its entire function body If we're running a loop in parallel in an attempt to attain better performance, we'd have done nothing but add a lot of concurrency related overhead to our program-with forking, join ing, waiting, context switches, cache effects, and so on-and will likely see negative performance effects rather than gains, not to mention code com plexity Part of the data parallelism process, therefore, must also involve an analysis of the code that will be run inside of the loop bodies and possibly a restructuring of it so that it doesn' t depend on shared state, uses more efficient fine-grained synchronization, and so forth Additionally, the fact that synchronization is involved may not be suf ficient either If the loop itself isn't associative-that is, order of execution doesn't matter-or it is performing nonassociative operations on data read and written by the loop bodies-then the loop may produce incorrect answers

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Once we've done the work to ensure that body is safe to run in parallel, the simplest approach to parallelizing the loop is to divide the size of the loop (ie, h i - 10, assuming the iterations of the loop are in ascending order, that is, that 10 < = h i ) by the number of processors, to get a per thread iteration count and to have each thread process a series of contiguous iterations This approach, called static decomposition, while simple, is not ideal for a few reasons, but mainly because it can lead to inefficient use of the available processors An alternative to static decomposition is to spawn a certain number of threads, or to somehow arrange for the number of threads to scale based on available processors and to have each of those

We will study conditional probability in 3 Independent events and disjoint events are commonly confused Independent events refer to events that can occur together; disjoint events cannot occur together We refer now to events that do not have probability 0 (such events are encountered in nondenumerably in nite sample spaces) If events are independent, then they cannot be disjoint since they must be able to occur together; if events are disjoint, then they cannot be independent because they cannot occur together

threads calculate iterations on demand In this approach, which we call dynamic decomposition, threads do not know a priori which iterations they will be executing Instead, they find out as they execute and as they become available to run extra iterations Both approaches will be examined

Timekeeping Data Correction Report Hours Worked 10 8 8 12 8 8 25 Job Number Charged A-312 D-040 A-312 D-040 A-310 Comments Overtime approval needed Invalid job number No job number Overtime approval needed Invalid job number Date is for a weekend No job number

Contiguous Iterations To begin, let's take the loop example seen before and see what happens when we use the straightforward static decomposi tion already outlined above: dividing the iteration space into contiguous chunks of indices Applying this technique to the sequential F o r method seen earlier, we might end up with code that looks like the following P a r a l l e l F o r method

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