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.

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.

Abstract: Technologies for automatic graph partitioning include a computing device that approximates a vertex centrality weight for each vertex of a graph and then approximates, based on the approximate vertex centrality weight, an approximate edge centrality value for each edge of the graph. The computing device may repeatedly delete an edge having the highest edge centrality value and test if the graph has been disconnected. If the graph is disconnected, the computing device calculates a cluster quality metric. If the cluster quality does not decrease, the computing device realizes a new clustering of the graph based on the disconnected partitions. If the cluster quality metric decreases, the computing device reintroduces a deleted edge. The computing device recalculates the approximate vertex centrality weights and edge centrality values after reintroducing a deleted edge, deleting a predefined number of edges, or realizing a new clustering. Other embodiments are described and claimed.