ML implemented the parallel parameter sampling routines and carried out parallelized simulations.
Parallel parameter n = 8 is chosen for two reasons.
Two major paths are parallel parameter sweeps and distributed execution of individual simulations.
Table 2 Comparison with the traditional Hough transform Time & resource No constraint ρ∈ [−23, 23] of ρ Parallel parameter, n=1 Time cost 100%1000% Resource cost 100%5.66% Parallel parameter, n=8 Time cost 12.5% 12.5% Resource cost 100%5.6%%.
The time and resources required in the traditional Hough transform are compared (Table 2), with the parallel parameter n = 8.
These factors, combined with the constant number of processing nodes, imply that parallel parameter sweeps do not affect the asymptotic space requirements.
Similarly, the memory requirements for a parallel parameter sweep will be the size of a model run times the number of simultaneous runs, again with additions for job control requirements and with discounts for possible shared memory.
Scan in parallel – we used the parallel parameter scan task to perform 300,000 Monte Carlo simulations of an NF κ B signal transduction model [ 19].
Scan in parallel – we used the parallel parameter scan task to perform 1,000,000 Monte Carlo simulations of a MAPK signalling cascade model [ 20].
However, as described above, if the transformation for directly above viewpoint has been added, the posture variables can be thought of as a total of three degrees of freedom; two parallel movement parameters (x, y) and a rotation parameter (theta) on the plane.
