An all-device (i.e. GPU) computational approach is adopted where the entire computation is performed on the GPU device, leaving the CPU idle during all stages of the computation, including particle moving, indexing, particle collisions and state sampling.
New aspects of the method are the combination of nonlinear Gauss Seidel smoothing on the finest grid with linear coarse-grid corrections, and local damping in the initial stages of the computation, to keep the solution stable; the damping needed is estimated with the nonlinear smoother.
When there is insufficient space to store all intermediate results in high-speed memory (e.g. cache) existing approaches store selected stages of the computation, and recompute missing values from these checkpoints on an as-needed basis.
When there is not enough space to store all intermediate results in high-speed memory, checkpointing strategies are employed, whereby selected stages of the computation are stored, and missing information is recomputed from these checkpoints on an as-needed basis.
A previous stage to the computation of intensity statistics is a region segmentation of every B-scan according to the intensity variation along the vertical direction.
The paper describes a parallel architecture in the form of a systolic automaton consisting of three distinct stages for the computation of the model parameters of an autoregressive (AR) signal.
Unless one assumes that relative position is implicitly encoded in patterns of activation within early topographic maps [3] one must hypothesise the existence of a second stage to the computation that either integrates information from low level filters [14], [15], [9], [16], [13] or endogenously generates 'virtual contours' between the stimulus elements [3], [16].
For completeness, we repeat this analysis here for Stage 2 of the computation, where the time complexity of Stage 1 can be inferred similarly.
Monte Carlo simulation is used at all stages of the computations, from the estimation of the system unreliability to that of the stochastic quasi-gradient.
During the first stage of the iterative computation, the suboptimal algorithm estimates S m i under MMSE criterion.
