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Of these processors, one (id_world == 0) is the master processor and the others are the workers.
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Its architecture consists of a scalable number of identical master processors and a configurable set of shared co-processors.
For each task there can be up to three different implementations: Hardware implementations (HW) placed in the FPGA, Software implementations running on the master processor (MS), and another Software implementation running on the slave processor (SL).
Finally, the master processor computes the means and standard deviation (and other posterior statistics, when relevant) for the mean of the normal model.
Specifically, this can include drivers for the master processor architecture-specific functionality, memory and memory management drivers, bus initialization and transaction drivers, and I/O initialization and control drivers both at the board and master CPU level.
It is comprised of a set of library routines and compiler directives that allow the master processor to distribute the data and tasks among the processing units.
In a genetic driver, there are typically architecture-specific portions of source code, because the master processor is the central control unit and to gain access to anything on the board usually means going through the master processor.
A device driver that is generic manages hardware that is located on the board and not integrated onto the master processor.
The evolutionary processes, such as crossover, mutation and production are performed by the master processor.
The master processor uses a single population as a simple genetic algorithm does, and distributes the tasks of fitness evaluation to slave processors.
The tasks depend upon (input) files which initially reside on a master processor.
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