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s t a t i c int P a r a l l e lAdd ( i nt [ ] numbe r s , int p ) { II Compute p a rt i a l s u m s : i nt [ ] p a r t i a l S u m s = new i nt [ p ] ; P a r a l l e l F o r ( a , numbers Lengt h , ( i , id ) = > p a rt i a lSums [ id ] + = numbers [ i ] , p ) ; I I Compute f i n a l s u m : int s u m = a ; f o r ( i nt i = a ; i < p ; i++ ) { s u m += p a r t i a l S u m s [ i ] ;

Every little green box is an available seat The actual basketball court is where the logo of E-Pizza is The buttons at the bottom provide the basic operational functionality The operator can:

ret u rn s u m ; }

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Some operations are nonassociative, which means we cannot use paral lelism in this way Yet others are noncommutative, which means that we can actually use parallelism but must take care to ensure that all combina tions are done in the correct index order; that is, we must never swap the first and second arguments to the operator, when compared to sequential execution A classic example is division, an operation that is associative but noncommutative Also note there is an inherent scalability limitation in the above exam ple At the end we have a sequential for loop from e to p 1 that sums up the partial sums to produce the final answer There are more scalable approaches to this step, the most popular being a so-called logarithmic reduction during which each thread adds two partial sums together at a time to produce half the number of partial sums, and so on, until only one sum remains This yields a theoretical performance of O(log n), but this presumes an infinite number of processors In reality, on the architectures Windows runs on (today) and given the small size of p compared to n, this approach does not perform nearly as well as the previous one, due to the high cost of synchronization, so we will omit any further discussion of it For fine-grained parallelism hardware architectures that offer vector and

Buy a seat (or seats)

word level parallelism, such as those found in the supercomputing industry, however, it often makes sense to use such techniques Another data parallel technique related to reductions is called a scan A scan is very much like a reduction except that the output of the operation is another list of values instead of a scalar Each element i in the result is the partial reduction of the list, obtained by applying the particular binary oper ator to all elements O i-I in the original list In the case of a sum scan (also called the partial sums of a list), for instance, the tenth element contains the sum of elements 0 through 9, the eleventh contains the sum of elements 0 through 1 0, and so on This seems like an inherently sequential problem, but again we can take advantage of associativity and commutativity in the same way we did to achieve parallelism (see Further Reading, Hillis, Steele)

Q Cancel a purchased seat (not implemented in this experiment) Q Update the screen by reading all the purchased seats from the database Q Clear a selected seat (or seats) Q Quit the application Next, the run () method will start the listener and subscribe to the topic with a selector that will choose the desired match:

There are countless ways to sort a list This is true of sequential software, and holds true for parallel software too Parallel quick-sort, parallel merge-sort,

The GAO study concluded that the Pentagon must more effectively resist the urge to achieve revolutionary but unachievable capabilities, instead allowing technologies to mature in the technology base before bringing [them] onto programs, ensuring requirements are well de ned and doable, and instituting shorter development cycles In other words, they need more realistic goals with shorter timelines This in turn would make it easier to estimate costs accurately and, better still, keep them from skyrocketing out of control

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