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This is just one view of parallel programs (and algorithms).
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Note: The tasks mentioned here may not be the tasks you're used to.
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Here, łtask˛ means ła sequence of operations that is scheduled atomically and can run to completion once started˛
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That is, a task is the computation between synchronization points
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For example, an MPI process is a collection of tasks
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A parallel algorithm is a collection of tasks and a partial ordering between them.
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Design goals:
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Match tasks to the available processors (exploit parallelism).
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Minimize ordering (avoid unnecessary synchronizations).
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Utilize remaining ordering (exploit locality).
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Sources of parallelism:
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Data parallelism: updating array elements simultaneously.
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Functional parallelism: conceptually different tasks which combine to solve the problem.
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Speculative parallelism: temporarily ignore partial ordering, repair later if this causes problems.
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This list is not exhaustive
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