Algorithmic Differentiation of Pragma-Defined Parallel by Michael Förster

By Michael Förster

Numerical courses usually use parallel programming concepts corresponding to OpenMP to compute the program's output values as effective as attainable. furthermore, by-product values of those output values with admire to convinced enter values play a vital position. to accomplish code that computes not just the output values concurrently but in addition the by-product values, this paintings introduces numerous source-to-source transformation principles. those principles are in keeping with a strategy known as algorithmic differentiation. the main target of this paintings lies at the very important opposite mode of algorithmic differentiation. The inherent data-flow reversal of the opposite mode has to be dealt with competently throughout the transformation. the 1st a part of the paintings examines the modifications in a really normal method for the reason that pragma-based parallel areas take place in lots of other forms akin to OpenMP, OpenACC, and Intel Phi. the second one half describes the transformation principles of an important OpenMP constructs.

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5. 6. 7. 8. private(list) firstprivate(list) lastprivate(list) reduction(operator: list) schedule(kind[, chunk_size]) collapse(n) ordered nowait The for directive places restrictions on the structure of all associated for-loops. [. ] The canonical form allows the iteration count of all associated loops to be computed before executing the outermost loop. [. ] The loop construct is associated with a loop nest consisting of one or more loops that follow the directive. There is an implicit barrier at the end of a loop construct unless a nowait clause is specified.

This does not need to be clear in all details since the next section provides an explanation for this. At this point it is sufficient to know that all equations are partitioned into chunks defined by a lower bound lb and an upper bound ub. Afterwards, a while-loop iterates over the elements of each chunk and the corresponding thread processes these elements. Since each thread computes only its local sum the overall value of the objective function is computed by adding all these local sums after the parallel region.

In a task construct, if no default clause is present, a variable that in the enclosing context is determined to be shared by all implicit tasks bound to the current team is shared. 1 49 • In a task construct, if no default clause is present, a variable whose datasharing attribute is not determined by the rules above is firstprivate. Additional restrictions [. 2) The first pragma introduced here is the threadprivate directive. This directive defines static data to be replicated for each thread.

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