Efficient Parallel Graph Algorithms in Python
نویسندگان
چکیده
Domain experts in a variety of fields utilize large-scale graph analysis; however, creating high-performance parallel graph applications currently involves expertise in both graph theory and parallel programming which might not be available to the domain specialist. This project explores methods for bringing efficient parallel performance to graph applications written in Python using selective embedded just-in-time specialization (SEJITS). The Knowledge Discovery Toolbox (KDT) is a tool for analyzing graph data on distributed systems. KDT provides a high-level interface for analysis in Python and graph algorithm building blocks in C++. Users of the KDT have access to operations on matrix and vector elements which are implemented in the efficiency layer. Combination of these operations forms the computational core of many graph applications. A method was developed to extend the KDT with new operators written in Python using a Python SEJITS implementation, Asp.
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