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With the advent of multicore processors as the norm rather than the exception, it is more tempting and more practical than ever before to want to spread the processing load so as to get the most out of all the available cores There are two main approaches to spreading the workload One is to use multiple processes and the other is to use multiple threads This chapter shows how to use both approaches Using multiple processes, that is, running separate programs, has the advantage that each process runs independently This leaves all the burden of handling concurrency to the underlying operating system The disadvantage is that communication and data sharing between the invoking program and the separate processes it invokes can be inconvenient On Unix systems this can be solved by using the exec and fork paradigm, but for cross-platform programs other solutions must be used The simplest, and the one shown here, is for the invoking program to feed data to the processes it runs and leave them to produce their output independently A more exible approach that greatly simpli es two-way communication is to use networking Of course, in many situations such communication isn t needed we just need to run one or more other programs from one orchestrating program An alternative to handing off work to independent processes is to create a threaded program that distributes work to independent threads of execution This has the advantage that we can communicate simply by sharing data (providing we ensure that shared data is accessed only by one thread at a time), but leaves the burden of managing concurrency squarely with the programmer Python provides good support for creating threaded programs, minimizing the work that we must do Nonetheless, multithreaded programs are inherently more complex than single-threaded programs and require much more care in their creation and maintenance In this chapter s rst section we will create two small programs The rst program is invoked by the user and the second program is invoked by the rst pro439.

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The first step is to make the loop iterations independent One way to accomplish this is shown in Fig A6 Because the combine() function must be called in the same order as in the sequential program, there is an extra ordering constraint introduced into the parallel algorithm This creates a dependency between the iterations of the loop If we want to run the loop iterations in parallel, we need to remove this dependency In this example, we've assumed the combine() function is simple and doesn't take much time Hence, it should be acceptable to run the calls to combine () outside the parallel region We do this by placing each intermediate result computed by big_comp() into an element of an array Then the array elements can be passed to the combine() function in the sequential order in a separate loop This code transformation preserves the meaning of the original program (that is, the results are identical between the parallel code and the original version of the program) With this transformation, the iterations of the first loop are independent and they can be safely computed in parallel To divide the loop iterations among multiple threads, an OpenMP worksharing construct is used This construct assigns loop iterations from the immediately following loop onto a team of threads Later we will discuss how to control the way the loop iterations are scheduled, but for now, we leave it to the system to figure out how the loop iterations are to be mapped onto the threads The parallel versions are shown in Fig A7

gram, with the second program invoked once for each separate process that is required In the second section we will begin by giving a bare-bones introduction to threaded programming Then we will create a threaded program that has the same functionality as the two programs from the rst section combined so as to provide a contrast between the multiple processes and the multiple threads approaches And then we will review another threaded program, more sophisticated than the rst, that both hands off work and gathers together all the results

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