The computation in different GPU compute blocks can be executed in parallel.
Because of the hardware limitation, in this work we adopt a coarse-grained model and distribute the computation in the cluster computing environment to achieve parallelism.
After investigating the performance of the proposed algorithms (GA-PSO and iGA-PSO) running in a sequential manner for gene network modeling, in the second phase, we conducted two suites of experiments to examine how our iGA-PSO method can be performed in a parallel computing environment to speed up the computation in practice.
While processing for Network 1, the asynchronous model could not finish the computation in 1 h whereas the combined method computed the attractors in 12 min and for the T-cell receptor network, the performance almost doubled.
We follow an approach to data analytics where the computational complexity is fundamentally reduced by performing the majority of the computation in an approximated or even stochastic framework while the high precision solution is guaranteed by an iterative refinement process.
Methods such as using GPU to accelerated the computation in [23] can easily be reduced (also by improving the computer in terms of CPU, memory and operating system), but a better result is still a better result whether it takes 1 hour or 10 minutes.
Note also that the matrix I−Φ ⊤−1 Φ and the vector Φ ⊤−1 A N ξ in (13) can be computed before the iterations in (12), and hence the computation in (12) is very simple.
In order to clarify the analysis, the approach distinguishes three independent levels: the computational theory itself, the algorithms employed, and the underlying implementation of the computation, in this case through the human neural mechanisms.
To ensure the computation in real time, tasks of the application are distributed using parallel computing.
argument this will execute the computation in a fresh custodian, and keep track of all threads under this custodian.
The synchronization among the two tools is granted through the computation in real time of the simulation model.
