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Are you using a Small Computer System Interface (SCSI) or a Integrated Digital Environment (IDE) controller What is the best way to load your software If you do not install the software in the proper sequence, the system will underperform, not boot properly, freeze, or just hang at times when shutting down

For example, if we were to parallelize the following loop, how would we decide how many threads to use, how best to schedule them, how to assign iteration ranges to threads, and so on

void F o r ( int 10, int h i , Action < i nt > bod y ) { for ( i nt i = 10; i < h i ; i++ ) { body ( i ) ; } }

a network Many times, an incumbent has paid high volumes of money to get a license to sell regulated services Legally, it then becomes dif cult for the community to compete Fair agreements need to be developed for relations between the community and the incumbent Right of way (ROW) issues are also of paramount importance, and most community network deployments face competition from the incumbent service providers

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The same questions are equally interesting for parallelizing code that iterates over collections of data, for example, an array or any other data structure with an indexer (such as I L i s t < T > in the NET Framework and std : : vector in C++'s STL)

v o i d F o r < T > ( T [ ] a r r , Action < T > body ) { for ( i nt i = a; i < a r r Lengt h ; i++ ) { body ( a r r [ i ] ) ; }

The main content of this chapter provides a step-by-step procedure to assist you in building an encoder and developing suitable answers to the questions just posed, but before we begin, let s take a closer look at the components

void F o r < T > ( T [ ] a r r , Action < T > body ) { F or ( a , a r r Lengt h , i = > body ( a r r [ i ] ; }

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

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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