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This paper proposes a new concurrent data structure, called parallel hash table, for synchronizing the access of multiple threads to resources stored in a shared buffer.
Simultaneous multithreading (SMT) is an architectural technique that improves resource utilization by allowing instructions from multiple threads to coexist in a processor and share resources.
To simulate microstructure evolution process during actual casting solidification more effectively, a high performance computing method based on CUDA + GPU architecture is explored in this paper, and larger-scale computation is implemented by the concurrent execution of multiple threads to improve computational efficiency.
Thus, the authors propose a system that allows multiple threads to share processor cores concurrently while maintaining isolation and fair resource sharing.
The processing time could be further reduced by executing a pipe fragment within sdfMultiplexer that can span multiple threads to parallelize the execution.
When using multiple threads to implement multi-view video coding, it was found that MV-HEVC benefrom from inter-view predictions but it is technically difficult to implement multi-view video coding.
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OLego is implemented in C++ with full support of multiple threading, to allow for fast processing of large-scale data.
Keep in mind that only data shared by multiple threads needs to be synchronized.
The basin is divided into sub-basins, and subsequently the different sub-basins operate on multiple threads concurrently to calculate flow directions.
To account for threading when assessing computational efficiency, we employed all the aligners to perform aligning process in single-thread mode without any competition and we also were careful about some aligners supported multiple threads function to accelerate computation speed; thus these aligners were evaluated and compared in three-thread mode without any competition.
It needs to get enough applications running on multiple threads and cores to be able to really make a dent in the enterprise in a way that provides a performance boost that virtualization cannot.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com