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The tasks depend upon (input) files which initially reside on a master processor.
The proposed approach allows to bound communication memories for different loading rates of the master processor.
The evolutionary processes, such as crossover, mutation and production are performed by the master processor.
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 architecture-specific manages the hardware that is integrated into the master processor.
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.
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Its architecture consists of a scalable number of identical master processors and a configurable set of shared co-processors.
Additionally, an on-chip real-time operating system kernel is included to support transparent multi-tasking over the set of master processors.
In this case, although master processors only work for scheduling and somehow reduce scalability, the degree of the reduction of scalability is limited according to the ratio of masters to slaves, as well as the workload balance with limited synchronization costs.
This was performed according to the manufacturer's instruction using the Euroblot-master automated processor (Euroimmun).
The methodology is a hybrid method in which different levels of master and slave processors are used to distribute the computational grid points and to maximize the use of multiple processors utilized in GPU.
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