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Experimental results support that the proposed algorithm has runtime, memory, and scalability performances superior to those of existing algorithm.
An extensive experimental study carried on several real-life datasets shows that the proposed algorithms outperform the previous best algorithm in terms of runtime, memory consumption and scalability.
ParaStream integrates streaming computation with a traditional divide-and-conquer scheme, in which additional erase steps are implemented to reduce the runtime memory footprint.
According to the experimental results, the designed algorithm is more effective than three state-of-the-art algorithms in terms of runtime, memory usage and hiding accuracy.
With this framework, we are able to effectively calibrate the runtime memory access patterns and dynamically apply optimized cache coherence protocols to minimize consistency maintenance overhead.
A static deployment enables static memory allocation, so removing the need for runtime memory administration.
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When considering runtime, extra memory storage, numerical stability and common precisions for Laplace equation, the BLAS version appears as the best one.
To optimize runtime and memory requirements within the plasma, a splitting of the Laplacian is suggested.
This paper presents the first window-based parallel layout decomposition methods for improving both runtime and memory consumption.
The winning strategies in Tracks 2 and 3 showed significant progress over the previous competition by achieving multiple orders of magnitude performance improvement in terms of computational runtime and memory requirements.
Therefore, the runtime and memory efficiencies of the algorithm are able to be enhanced significantly because the search space of its mining process can be considerably reduced.
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