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Our work demonstrates that data flow computing engines are promising candidates to make contributions to the development of geoscience applications.
In recent years, data flow computing engines based on reconfigurable computing systems such as FPGAs have been introduced into HPC area, and start to show some inspiringly good results in many important applications.
Other authors have addressed the parallelization of optical flow computing systems.
This article focuses on the parallelization of an optical flow computing system based on the well-known Lucas-Kanade algorithm [22, 23].
Reference [32] applies MapReduce programming to parallel power flow computing using the component averaging (CAV) iterative method and Jacobi iterative method.
Since ABP must remain above this critical value in order to prevent injuries from insufficient blood flow, computing this quantity on a personalized basis would help improve post-operative outcomes.
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The propagation of the generated acoustic wave is simulated with a mean flow at rest and with a steady turbulent mean flow computed by RANS.
Figure 3 Optical flow computed with pyramidal Lucas Kanade.
Figure 3 shows the optical flow computed for the image of Figure 1a.
Motion is modeled by means of optical flow, computed for different directions and scales.
Constraint 2 enforces that the delay of the flow computed via Equation 17 respects the delay required by the new flow.
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