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In the shared memory environment, we used OpenMP and CUDA.
The shared memory environment was tested with several configurations.
This leads us to explore another shared memory environment, the GPGPU with CUDA.
The algorithms were tested in the distributed memory environment CS-4 using MPI.
In a distributed memory environment this demand severely limits applicability of the FMM to systems with O(10 K atoms).
On the contrary, Trinity employs an in-memory key-value store in a distributed shared memory environment.
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In this work, we implemented our algorithm using both distributed and shared memory environments.
First, the FEM model is reviewed and its extension to high core-count shared memory environments is described.
Since the MPI library is designed for distributed memory environments, a BSP/CGM algorithm can be mapped into an MPI implementation using the message resources of this library.
However, the application of these intrinsic functions to sparse data sets in distributed memory environments, is currently not supported by vendors of Fortran 90 and HPF compilers.
We also investigate the problem of effectively and efficiently mining frequent patterns from such streaming data, in the targeted case of dealing with limited memory environments so that disk support is required.
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