One of the approaches to realize the concurrency is using multi thread programming.
Applicability of multi-threading for similarity search over multiple time-series streams.
In this paper, we presented a dynamic management mechanism of hardware multi-threading for pipelined hardware multi-threading architecture.
Using multi-threading for cache-friendly, fine-grained parallelization can outperform coarse-grained alternatives which are often less cache-friendly, a likely scenario in modern predictive analytics workflow such as Hierarchical Bayesian GLM, variable selection, and ensemble regression and classification.
Performance was enhanced using multi-threading for acceleration of surface chemistry, material properties calculations, and species equation solvers, and using graphical processing units for acceleration of the linear solvers and pre-conditioners.
Multi-threading, for example, allows the distribution of computational tasks in terminals over the network, and CUDA (available on Nvidia graphic cards) allows parallel calculations at multiple graphical processing units.
All alignment tools were run with a single thread for alignment except for multi-thread tests.
In the system designed in this paper, single-thread and multi-thread writing are used for the operation inside the data writing area.
The multi-thread computation is suitable for computers equipped with processors with more than one computational core as it decreases computational time significantly (see Section 5.4).
We also implemented multi-threading in RAPSearch2, and the multi-thread modes achieve significant acceleration (e.g. 3.5X for 4-thread mode).
We also implemented mass-spring simulation for various deformable objects and tested with multi-thread CPU, GPU combined with multi-threaded CPU, and CPU only.
