We finish discussing spectral partitioning, and move on to explore data organization via data clustering and latent semantic indexing. 3.1 Graph Partitioning Applications Describing a large problem in terms of a graph allows one to, within the problem's constraints, break the problem into smaller subproblems by partitioning the graph. By distributing subproblems across multiple processors, one can improve the key parameter &quot;speedup&quot;, or acceleration of the problem's computation relative to solving the problem with one processor. Parallelism's overhead prevents speedup from increasing at the same scale as one increases the number of processors allocated for the problem. Today, one might consider excellent a speedup of 500 on 1,000 processors. Graph partitioning applications arise in: ffl Scientific Simulation ffl Particle Simulation (aka &quot;N-Body &quot; Simulation) ffl Parallel Web Crawling ffl Very Large Systems Integration (VLSI) Design There exist a myriad of graph partitioning methods because different applications impose different partitioning constraints. Moreover, problems ' degrees of parallelizability range from intrinsically sequential (think depth-first search) to embarrassingly parallel (think Quicksort). Though graph partitioning research enjoyed its greatest popularity with the peak of high-performance computation, VLSI designers have contributed more techniques for graph partitioning than researchers from any other discipline
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.