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Our objective is to build a fast haplotype assembly model where computationally intensive tasks are executed before execution of the iterative process.
Shifters included with the engine core are not general purpose shifters that accept arbitrary inputs; they are used only in the execution of the iterative CORDIC algorithm and are controlled by a counter attached with the instruction decoder.
It sorts all fragments based on their positions before execution of the iterative module.
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For our approach, the execution time of the iterative process has been measured.
The execution of the distributed iterative algorithm is illustrated as follows.
In this paper we propose a new approach that accelerates the execution of iterative algorithms by allowing iterations to execute in an asynchronous manner.
The asynchronous execution of incremental iterative computation significantly enhances the overall performance of iiHadoop; however, this approach suffers from some limitations.
The iterative execution of the PTAS is represented by the box labeled "Solve GOP level CADF problem" in the flowchart of Figure 4. Using this optimal subset of frames and the transmit powers, the RBS then updates the target SINRs for all users from their previous values used during the last execution of the outermost loop.
This paper introduces a workflow model targeted to provide natural automation and distributed execution of complex iterative computation processes, where the calculation chain contains multiple task-specific software applications which exchange files during the process.
The result of the iterative execution is a list of source attributes that correspond to properties of the concepts specified in the query.
To support asynchronous execution of iterative algorithms, we assume that the termination condition will not be satisfied at every iteration; therefore, the next iteration can start even if the previous iteration is still running.
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