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It is used to prove the wait-free computability theorem [25] that characterizes the tasks that are wait-free solvable in a read/write shared memory environment, and is intimately related to the simplicial approximate agreement theorem of topology.
MPI implementation can be smart enough to realize that it runs on a shared memory environment and consequently to optimize its behavior accordingly.
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The emergence of persistent memories has powered the data processing with the in-memory environment and in-memory data analytics have become an advance of high-performance data processing.
PLINK 1.9's implementation of this calculation is designed to compensate for GCTA 1.24's limitations it is cross-platform, works in low-memory environments, and uses double-precision arithmetic while remaining within a factor of 2-5 on speed.
In the shared memory environment, we used OpenMP and CUDA.
We introduce our efforts to extend the Libtensor framework to work in the distributed memory environment in a scalable and energy-efficient manner.
Using the ideas of the distributed memory BSP/CGM algorithms that were implemented with MPI, we implemented them in a shared memory environment using OpenMP (Table 6 and Fig. 9).
Four of those computing systems (CS-1, CS-2, CS-3, and CS-5) use shared memory and have at least one CUDA GPU (one of them has four identical GPUs) with different specifications, which allow us to check the consistency and performance of our algorithms using shared memory environment.
In this work, parallel CFD DEM algorithms have been developed under the distributed memory environment with a fluid flow solver based on finite volume method and arbitrary 3D unstructured meshes.
The shared memory environment was tested with several configurations.
This leads us to explore another shared memory environment, the GPGPU with CUDA.
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