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12.5.4 Efficiency of Parallel Implementation

Since our objective is to compute the flow around a cylinder, the efficiency of the parallel implementation was tested on such a problem. The region for which is uniformly covered with N particles. The parallel efficiency is shown on Figure 12.19 as a function of the hypercube size. The parallel implementation is fairly robust: The parallel efficiency, , remains larger than . The number of vortices per processor was kept roughly constant at 1500 even if the parallel efficiency is not a strong function of the size of the problem. It is, however, much more sensitive to the quality of the domain decomposition. The fast parallel algorithm performs better when all the subdomains have approximately the same squarish shape or in other words, when the largest group assigned to a processor is as compact as possible.

Figure 12.19: Parallel Efficiency of the Fast Algorithm

The results of Figure 12.19 were obtained at early times when the Lagrangian particles are still distributed evenly around the cylinder which makes the domain decomposition an easier task. At later times, the distribution of the vortices does not allow the decomposition of the domain in groups having approximately the same radius and the same number of vortices. Some subdomains cover a larger region of space and as a result, the efficiency drops to approximately 0.6. This is mainly due to the fact that more processors end up in the near field of a processor responsible for a large group; the request lists are longer and more data has to be moved between processors.

The sources of overhead corresponding to Figure 12.19 are shown on Figure 12.20 normalized with the useful work. Load imbalance, the largest overhead contributor, is defined as the difference between the maximum useful work reported by a processor and the average useful work per processor. Further, the extra work includes the time spent making a copy of one's own data structure, the time required to absorb the returning information, and the work that was duplicated in all processors, namely, the search for acceptable interactions in the upper portion of the tree and the subsequent creation of the request lists. The remaining overhead has been lumped under communication time although most of it is probably idle time (or synchronization time) that was not included in the definition of load imbalance.

Figure 12.20: Load Imbalance (solid), Communication and Synchronization Time (dash), and Extra Work (dot-dash) as a Function of the Number of Processors

We expected that as P increases, the near field of a processor would eventually contain a fixed number of neighboring processors. The number of messages and the load imbalance would then reach an asymptote and the loss of efficiency would be driven by the much smaller communication and extra times. However, this has yet to happen at 32 processors and the communication time is already starting to make an impact.

Nevertheless, the fast algorithm, its reasonably efficient parallel implementation and the speed of the Mark III have made possible simulations with as many as 80,000 vortex particles.

next up previous contents index
Next: 12.5.5 Results Up: Fast Vortex Algorithm Previous: 12.5.3 Hypercube Implementation

Guy Robinson
Wed Mar 1 10:19:35 EST 1995