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To reduce memory usage decomposition-based methods can be successfully applied.
It was modified by Wheeler and Pearlman [28] by making a no list SPIHT (NLS) to reduce memory usage.
The horizontal scaling sharding middleware for MySQL let users segment their database to reduce memory usage while still being able to rapidly run operations.
Our mixed precision approach serves both to reduce memory usage as well as maximizing the benefit from AVX/SSE as well as GPU floating point hardware.
Furthermore, for convolutions of multidimensional data, the segregation of data and work buffers can be exploited to reduce memory usage and execution time significantly.
It might be possible to reduce memory usage and increase performance of the element storage by using data structures that are more well suited, such as the persistent data structures described by Okasaki [46].
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However, the configuration that reduces memory usage by 50% degrades the accuracy in 22% with a speedup of ×3.3.
A commonly used solution for reducing memory usage is provided by calculating a running mean [10], using a forgetting factor.
A configuration that reduces memory usage by 50% degrades the accuracy from 15 to 25% with a range of speedup which varies from ×2.8 to ×3.5.
By using this hybrid method, the proposed system improves the robustness against unknown word patterns, reduces memory usage, and increases accuracy.
This approximation Q ≈ LL′ dramatically reduces memory usage from O(N M) to O NMR).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com