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Outer-Level Parallelization. It can be assumed that the choice of an appropriate algorithm should be made between independent runs or periodically interacting searches, as they are more suitable for more than few processes. The maximal number of engaged nodes is limited by reasonable shortening of the chain length, to preserve an acceptable quality of results. Inner-Level Parallelization. Within a node a few threads can build one subchain of a length determined at the outer-level. Negligible deterioration of quality is a key requirement.

The parallel implementation of SA presented in this paper had to overcome many practical issues in order to achieve good parallel speedups and efficiency. Tuning of the algorithms for distributed as well as for shared memory environment was conducted. The plan of the paper is as follows: Section 2 presents the theoretical basis of the sequential and parallel SA algorithm. Section 3 describes how the MPI and OpenMP parallelization was done, while Section 4 presents the results of the experiments. Conclusions follows.

2), whereas an outline of the latter one is presented in Figure 4. At the inner-level, the total number of trials (EpochLength from the outer level) in each temperature step is divided into short sets of trials All trials in such a set are done independently. This modification is the basis for the OpenMP parallelization with loop worksharing. To achieve an acceptable speed-up, the following optimizations are necessary: 24 A. Debudaj-Grabysz and R. Rabenseifner – The parallel threads must not be forked and joined for each inner loop because the total execution time for a set of trials can be too short, compared to the OpenMP fork-join overhead; – The size of such a set must be larger than the number of threads to minimize the load imbalance due to the potentially extremely varying execution time for each trial.

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Woodworking Shopnotes 017 - Adjustable Sawhorse


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