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The Message-Passing Interface (MPI) library is used to distribute the computation across nodes in distributed-memory systems from small clusters to supercomputers.
We write scripts to distribute the computation among multiple cores/CPUs and run multiple computing analyses simultaneously.
The problem is to distribute the computation time between different tasks.
In order to distribute the computation, the original dataset must be split into partitions and distributed to the available workers.
Therefore, it is crucial to distribute the computation load evenly to multiple cores and to parallelize the computation efficiently.
It would also be desirable to distribute the computation required to implement network MIMO among many nodes, so that the solution scales well with the size of the network.
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The MapReduce framework [ 16, 17] use key-value pairs as the only data type to distribute the computations.
We focus on how to design a parallel computational model to infer decoupled S-system and how to distribute the relevant computation to a cloud computing environment.
However, because of the high number of charges considered, simulation on systems Q1 and Q2 were run on pools of 8 processor using an MPI fortran77 program and employing a data replicated approach to distribute the energy computation between the threads.
For the MMR algorithm, since the base learners which process part of the original datasets work in one single machine sequentially, in the next step, we plan to parallelize this step and distribute the computation to more computing nodes for the sake of increasing the computational efficiency.
To minimize the system overhead, a centralized dispatching with decentralized admission (CDDA) approach is used to distribute the control related computation to each server pool.
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