Data flow programming of computing apparatus with vector estimation-based graph partitioning

Abstract

In various embodiments, a spectral graph partitioner ('SP') of a graph partitioning system ('GPS') may partition a data flow graph associated with a program into a plurality of subgraphs to be used to perform analysis or debugging. The SP may generate estimated eigenvectors for a matrix representing the graph through minimization of a function on the vectors. The SP may generate multiple eigenvectors to perform the clustering in a multi-dimensional space described by the eigenvectors. The SP may refine the clustering by repeating generation of eigenvectors to describe higher-dimensional spaces and perform further clustering. The SP may also determine quality metrics for the clusters and may stop refinement based on the quality metrics. The GPS may select between utilizing the SP or utilizing one or more other partitioners based on various factors such as, for example, graph size or quality metrics. Other embodiments may be described and/or claimed.