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We study conflict-free data distribution schemes in parallel memories in multiprocessor system architectures.
In addition, we showed that ExaML runs up to three times faster [see Fig. 4b in Kobert et al. (2014)] than with the previous data distribution schemes.
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In contrast to existing parallel algorithms, the multiprocessor partitioning strategy is not governed by an underlying static data distribution scheme.
This alternative data distribution scheme improved parallel run times by one order of magnitude on a partitioned protein alignment with 1000 partitions under CAT.
Our results demonstrate that the data delivery delay and rate of our algorithm outperform those of non-cooperative data delivery, and the probability of data distribution of our scheme is 4%, 12%, and 19% higher than that of LGC, LCA, and LRU.
Then, the SMOTE Big Data approach [41] was applied for each binary subset of imbalanced binary class to balance the data distribution, following the same scheme as suggested in [62].
To the best of our knowledge, our selection scheme, is the first to automatically select compression and distribution schemes for sparse data arrays with the array intrinsics of Fortran 90 in a distributed memory environment.
Several frequency distribution schemes have been suggested to improve the system data rate of an OFDMA system.
A further sin weighting scheme was applied to counterbalance the denser data distribution near the poles.
A sin weighting scheme was used to counterbalance the denser data distribution near the poles.
To overcome the denser data distribution close to the poles, we used a 1/ sinθ weighting scheme (with θ being the colatitude).
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