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Controlling the Execution of Parallel Algorithms in Ada

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Controlling the Execution of Parallel Algorithms in Ada
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CC Attribution 2.0 Belgium:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Tasking in Ada provides an effective tool for shared memory parallelism. An "any time" algorithm is an algorithm that, given some more resources, will improve the accuracy of an estimate. For example, consider the estimation of Pi by a Monte Carlo method. With multitasking, the status of the running of an "any time" algorithm can be monitored without interrupts. The programming concepts will be illustrated with examples of algorithms in polyhedral geometry. The demonstrated code belongs to the free and open source PHCpack, at https://github.com/janverschelde/PHCpack.