The experiments further demonstrate the scalability of our package with the observation shown in Table 1 that the more processors used, the faster it is.
This is different for 0.3 ≤ ϵ ≤ 0.7, because assigning more processors to the cell with lower service demand decreases the overall blocking probability.
That is, while the problem is intractable, it is feasible to find useful (mathsf {GPARs}) in real-life graphs by leveraging parallel computing, provided that we can employ more processors when the graphs grow big.
That is, the more processors are used, the less time it takes to compute ({mathsf {Vio}} (varSigma, G)).
As remarked in Sect. 1, not all parallel algorithms have the property that the more processors (resources) are used, the faster their computations get.
More processors can decrease the efficiency because the probability of inconsistency increases.
That is, when G grows big, we add more processors and parallelize the computation of Q(G), to make the computation scale with G. Based on this assumption, several parallel graph query systems have been developed, e.g., Pregel [52], GraphLab [99], GraphX [36], Giraph [34], Giraph++ [104], Blogel [106] and Trinity [105].
So what if people were to install a rack or two of servers in their home — the bigger the home, the more processors?
In Scenario 3, the scale of CA is larger and many more processors are used, the three-layered dynamic scheduling is preferred, because it guarantees workload balancing and limits the synchronization penalties within an acceptable amount.
As expected, only a limited increase in performance could be achieved by adding more processors due to the communication overhead.
