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The United States has significantly reduced execution numbers, but still put 43 people to death last year.
The simulation results show a significantly reduced execution time (40 times faster) and also reveal the conditions where the event-scheduling update scheme is superior.
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When a workflow is executed several times, Anduril caches results of components and only executes the components whose configuration has changed since the last run, which reduces execution time significantly.
In [18], a parallel Hough transform for reducing execution time was proposed, but space complexity remains.
This concept can remarkably reduce execution time and computing complexity for multi-train simulation.
Chern et al. presented a parallel Hough transform to reduce execution time [18].
As a whole, our approach can help reduce execution time by 6 96% for computation-intensive applications.
Application turnaround was decreased in 50%, mostly due to the reduce execution speedup.
To reduce execution time, a customized data structure is implemented and explained.
Besides reducing execution times through distributed computing, Hadoop also supports distributed machine learning through Apache Mahout [5].
We are working on this parallel implementation and developing new optimizations to reduce execution times even further.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com