Benchmarking the Graphulo Processing Framework

TL;DR

This paper benchmarks the Graphulo processing framework, demonstrating its linear scalability with increased resources for large-scale graph operations within the context of the GraphBLAS standard.

Contribution

It provides empirical scalability results of Graphulo on large graph datasets, extending previous work and validating its efficiency with increased computational resources.

Findings

Graphulo's performance scales linearly with resources

Successful processing of graphs with up to 2^19 vertices

Effective extraction of graph subsets using TableMult

Abstract

Graph algorithms have wide applicablity to a variety of domains and are often used on massive datasets. Recent standardization efforts such as the GraphBLAS specify a set of key computational kernels that hardware and software developers can adhere to. Graphulo is a processing framework that enables GraphBLAS kernels in the Apache Accumulo database. In our previous work, we have demonstrated a core Graphulo operation called \textit{TableMult} that performs large-scale multiplication operations of database tables. In this article, we present the results of scaling the Graphulo engine to larger problems and scalablity when a greater number of resources is used. Specifically, we present two experiments that demonstrate Graphulo scaling performance is linear with the number of available resources. The first experiment demonstrates cluster processing rates through Graphulo's TableMult operator on two large graphs, scaled between $2^{17}$ and $2^{19}$ vertices. The second experiment uses TableMult to extract a random set of rows from a large graph ($2^{19}$ nodes) to simulate a cued graph analytic. These benchmarking results are of relevance to Graphulo users who wish to apply Graphulo to their graph problems.