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The five ALU's per lane also provide the compiler with the opportunity for instruction-level parallelism in the calculations associated with each row update, including parallel updates of as many as five posterior values for each row in Steps 2-6 of the algorithm.
In contrast to sequential updates, parallel updates are not guaranteed to converge.
While parallel updates lead to very fast implementations, they are not guaranteed to converge at all.
As seen above, initialized parallel updates lead to best results in practice.
After the initialization, the parallel updates can refine the local configuration of the variables more independently.
With parallel updates the effect of the updates propagates through the network only after one complete iteration cycle.
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A parallel updating strategy is employed to enrich the harmony memory diversity.
Each iteration of the simulation is based on a parallel update of the agents conforming local rules.
As an alternative to the sequential update of the HM, one could also apply the parallel update strategy (see [46] for more details).
In crowd simulation CA models, parallel update is generally preferred (Schadschneider et al. 2009), even if this strategy can lead to conflicts that must be solved.
Further, in our experiment, we include the parallel update algorithm initialized with our SGM approach, as described in Section 4.3, to check the effect of our initialization.
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