![]() ![]() Finally, to further understand the complex interaction with the underlying architecture, we present an analysis using performance counters that quantifies the improvement in hardware behavior in the context our SEJITS methodology. We also present a new Roofline model for graph traversals and show that we achieve performance that is significantly closer to the bounds suggested by the Roofline. We increase the system's flexibility by developing techniques that provide users with the ability to define new vertex and edge types from Python. We evaluate our approach by comparing it with the high-performance Combinatorial BLAS engine and show that our approach combines the benefits of programming in a high-level language with executing in a low-level parallel environment. In this work, we use the Selective Embedded Just-In-Time Specialization (SEJITS) approach to automatically translate semiring operations and filters defined by programmers into a lower-level efficiency language, bypassing the upcall into Python. While the high-level language for this customization enables domain scientists to productively express their graph analytics requirements, the customized queries perform poorly due to the overhead of having to call into the Python virtual machine for each vertex and edge. The more » user can also customize existing graph algorithms by writing filters. ![]() In KDT, the user can write custom graph algorithms by specifying operations between edges and vertices ( semiring operations). Previous investigations have developed Knowledge Discovery Toolbox (KDT), a sophisticated Python library for parallel graph computations. Thus, the computation must view the graph through a filter that passes only those individual vertices and edges of interest. In a semantic graph, vertices and edges carry attributes of various types and the analytic queries typically depend on the values of these attributes. « lessĮxecution of complex analytic queries on massive semantic graphs is a challenging problem in big-data analytics that requires high-performance parallel computing. We also showed that with small modification, ABC can be adapted to algebraicly compute other centrality measures such as percolation centrality. We did scalability experiments on parallel ABC and showed its total speedup. We evaluated the performance of ABC using 2-tuple geodetic semiring as compared to NetworkX, a common python package for graph algorithms. We used numba to optimize and parallelize ABC. This allows us to avoid computational complexity of ABC implementation using 3-tuple geodetic semiring. We also proposed an alternative version of ABC using the usual geodetic semiring with 2-tuple where we used a simple way to construct more » shortest path tree after computing shortest path distances in the usual geodetic semiring. Using the 3-tuple geodetic semiring, Dijkstra's and Brandes' algorithm, we wrote more concise and general algebraic betweenness centrality (ABC) algorithm which is valid for weighted and directed graphs. We proposed 3-tuple geodetic semiring as an extension to the usual geodetic semiring with 2-tuples. We aimed for algebraic betweenness centrality that can be parallelized easily. Particularly, we focused on Brandes' algorithm. We discuss some of our recent work studying synchronization of coupled oscillators to demonstrate how NetworkX enables research in the field of computational = ,Ībstract In this paper, we explored different ways to write the algebraic version of betweenness centrality algorithm. ![]() The ease-of-use and flexibility of the Python programming language together with connection to the SciPy tools make NetworkX a powerful tool for scientific computations. NetworkX can read and write various graph formats for eash exchange with existing data, and provides generators for many classic graphs and popular graph models, such as the Erdoes-Renyi, Small World, and Barabasi-Albert models, are included. In addition to the basic data structures many graph algorithms are implemented for calculating network properties and structure measures: shortest paths, betweenness centrality, clustering, and degree distribution and many more. The nodes in NetworkX graphs can be any (hashable) Python object and edges can contain arbitrary data this flexibility mades NetworkX ideal for representing networks found in many different scientific fields. The core package provides data structures for representing many types of networks, or graphs, including simple graphs, directed graphs, and graphs with parallel edges and self loops. NetworkX is a Python language package for exploration and analysis of networks and network algorithms.
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