By Michael Förster
Numerical courses frequently use parallel programming ideas corresponding to OpenMP to compute the program's output values as effective as attainable. furthermore, spinoff values of those output values with appreciate to yes enter values play a vital position. to accomplish code that computes not just the output values at the same time but in addition the spinoff values, this paintings introduces a number of source-to-source transformation ideas. those principles are in accordance with a strategy referred to 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 needs to be dealt with adequately through the transformation. the 1st a part of the paintings examines the ameliorations in a really common method when you consider that pragma-based parallel areas happen in lots of other kinds corresponding to OpenMP, OpenACC, and Intel Phi. the second one half describes the transformation ideas of crucial OpenMP constructs.
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Numerical courses frequently use parallel programming recommendations similar to OpenMP to compute the program's output values as effective as attainable. additionally, by-product values of those output values with recognize to definite enter values play an important function. 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 ideas.
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Additional resources for Algorithmic Differentiation of Pragma-Defined Parallel Regions: Differentiating Computer Programs Containing OpenMP
This code is referred to as F(2) and it is achieved by applying dcc two times: d c c F . c −t d c c t1_F . c. Subsequently, we apply dcc to its own output by providing the option -a that defines that the reverse mode should be applied. For technical reasons, we have to inform the compiler that the outcome is a second derivative code (-d 2). 2 Algorithmic Differentiation 23 Without going into details, the reader recognizes again that each floating-point (1) parameter is augmented by another derivative component.
3. 4. private(list) firstprivate(list) copyprivate(list) nowait [. ] The method of choosing a thread to execute the structured block is implementation defined. " There are another two worksharing constructs which are a combination of the parallel construct and a worksharing construct. The reason for defining a separate construct is that it often occurs that, for example, a loop is parallelizable. Without the combined version the developer would have to use the parallel construct first, and the loop construct would have to be placed inside of the associated structured code block.
N − 1 was partitioned by an explicit data decomposition and therefore each thread in the team was responsible for only one partition. Here, each thread processes all n iterations of the loop. In iteration i, each thread sets first the value of yi and then it sets the component xi to zero. The value of yi depends on the value of xi and therefore it depends on the fact whether or not the assignment that sets xi to zero has been already executed by another thread. The result in yi is decided by a race between read and store operations from different threads.