Shear-sort opened new avenues in the research of sorting techniques for mesh-connected processor arrays. The algorithm is extremely simple and converges to a snake-like sorted sequence with a time complexity which is suboptimal by a logarithmic factor. The techniques used for analyzing shear-sort have been used to derive more efficient algorithms, which have important ramifications both from practical and theoretical viewpoints. Although the algorithms described apply to any general two-dimensional computational model, the focus of most discussions is on mesh-connected computers which are now commercially available. In spite of a rich history of O (n) sorting algorithms on an n x n SIMD mesh, the constants associated with the leading term (i.e., n) are fairly large. This had led researchers to speculate about the tightness of the lower bound. The work in this paper sheds some more light on this problem as a 4n-step algorithm is shown to exist for a model slightly more powerful than the conventional SIMD model. Moreover, this algorithm has a running time of 3n steps on the more powerful MIMD model, which is "truly" optimal for such a model.
We investigate the use of runtime measurements to improve job scheduling on a parallel machine. Emphasis is on gang scheduling based strategies. With the information gathered at runtime, we define a task classification scheme based on fuzzy logic and Bayesian estimators. The resulting local task classification is used to provide better service to I/O bound and interactive jobs under gang scheduling. This is achieved through the use of idle times and also by controlling the spinning time of a task in the spin block mechanism depending on the node's workload. Simulation results show considerable improvements, in particular for I/O bound workloads, in both throughput and machine utilization for a gang scheduler using runtime information compared with gang schedulers for which this type of information is not available.
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