Benchmarking the Graphulo Processing Framework

09/27/2016
by   Timothy Weale, et al.
0

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 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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/11/2018

A tool framework for tweaking features in synthetic datasets

Researchers and developers use benchmarks to compare their algorithms an...
research
12/10/2018

Scaling-Up In-Memory Datalog Processing: Observations and Techniques

Recursive query processing has experienced a recent resurgence, as a res...
research
06/03/2018

Scaling Up Large-Scale Graph Processing for GPU-Accelerated Heterogeneous Systems

Not only with the large host memory for supporting large scale graph pro...
research
06/22/2016

From NoSQL Accumulo to NewSQL Graphulo: Design and Utility of Graph Algorithms inside a BigTable Database

Google BigTable's scale-out design for distributed key-value storage ins...
research
09/09/2022

PGAbB: A Block-Based Graph Processing Framework for Heterogeneous Platforms

Designing flexible graph kernels that can run well on various platforms ...
research
08/09/2017

D4M 3.0: Extended Database and Language Capabilities

The D4M tool was developed to address many of today's data needs. This t...
research
11/16/2020

A Large-Scale Database for Graph Representation Learning

With the rapid emergence of graph representation learning, the construct...

Please sign up or login with your details

Forgot password? Click here to reset