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Initial research indicates that Tradeshift's data set may even improve parallel decision-making, a current weakness of machine learning.
The hyper-threading technology can somehow reduce the context switching overhead to improve parallel performance.
Further, use of speech recognition technologies for shadowing assessment to improve parallel processing skills for conversation is described.
The initial work on improving load balance for partitioned datasets (Stamatakis and Ott, 2009) is hard-coded in RAxML-Light and can improve parallel efficiency by >50%.
In a future version the dependencies between nodes could be modelled as a directed acyclic graph and the parallelism be based on a dataflow model (YarKhan et al., 2011) to study and potentially further improve parallel performance.
Results: We introduce several improvements and extensions to ExaML: Extensions of substitution models and supported data types, the integration of a novel load balance algorithm as well as a parallel I/O optimization that significantly improve parallel efficiency, and a production-level implementation for Intel MIC-based hardware platforms.
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Hence, in ExaML (v. 1) we implemented a radically different parallelization approach (Stamatakis and Aberer, 2013) that improved parallel efficiency by up to a factor of 3 and also increased scalability.
Recently, we have worked on improving parallel performance of this application by re-designing the parallelization approach.
Improved Parallel Polynomial Division and Its Extensions.
Improved Parallel Computations with Toeplitz-like and Hankel-like Matrices.
We present a novel methodology for teaching parallel programming centered on improving parallel applications written by students through their experiences obtained during classes.
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