Combining Parallelization Strategies
In many situations, a single parallelization strategy might be all that is required to produce a parallel solution for a problem. However, in other situations, there is no single strategy sufficient to solve the problem effectively, and it is necessary to select a combina-tion of approaches.
The pipeline strategy represents a good starting point for a combination of approaches. The various stages in the pipeline can be further parallelized. For example, one stage might use multiple threads to perform a calculation on one item of data. A dif-ferent stage might have multiple threads working on separate items of data.
When mapping a process to an implementation, it is important to consider all the ways that it is possible to exploit parallelism and to avoid limiting yourself to the first approach that comes to mind. Consider a situation where a task takes 100 seconds to complete. Suppose that it’s possible to take 80 of those seconds and use four threads to complete the work. Now the runtime for the task is 20 serial seconds, plus 20 seconds when four threads are active, for a total of 40 seconds. Suppose that it is possible to use a different strategy to spread the serial 20 seconds over two threads, leading to a perform-ance gain of 10 seconds, so the total runtime is now 30 seconds: 10 seconds with two threads and 20 seconds with four threads. The first parallelization made the application two and a half times faster. The second parallelization made it 1.3x faster, which is not nearly as great but is still a significant gain. However, if the second optimization had been the only one performed, it would have resulted in only a 1.1x performance gain, not nearly as dramatic a pay-off as the 1.3x gain that it obtained when other parts of the code had already been made parallel.
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