Hypergraph-based optimisations for scalable graph analytics and learning

Graph-structured data has benefits of capturing inter-connectivity (topology) and hetero geneous knowledge (node/edge features) simultaneously. Hypergraphs may glean even more information reflecting complex non-pairwise relationships and additional metadata. Graph- and hypergraph-based partitioners can model workload or communication patterns of analytics and learning algorithms, enabling data-parallel scalability while preserving the solution quality. Hypergraph-based optimisations remain under-explored for graph neural networks (GNNs), which have complex access patterns compared to analytics workloads. Furthermore, special optimisations are needed when representing dynamic graph topologies and learning incrementally from streaming data. This thesis explores hypergraph-based optimisations for several scalable graph analytics and learning tasks. First, a hypergraph sampling approach is presented that supports large-scale dynamic graphs when modelling information cascades. Next, hypergraph partitioning is applied to scale approximate similarity search, by caching the computed features of replicated vertices. Moving from analytics to learning tasks, a data-parallel GNN training algorithm is developed using hypergraph-based construction and partitioning. Its communication scheme allows scalable distributed full-batch GNN training on static graphs. Sparse adja cency patterns are captured to perform non-blocking asynchronous communications for considerable speedups (10x single machine state-of-the-art baseline) in limited memory and bandwidth environments. Distributing GNNs using the hypergraph approach, compared to the graph approach, halves the running time and achieves 15% lower message volume. A new stochastic hypergraph sampling strategy further improves communication efficiency in distributed mini-batch GNN training. The final contribution is the design of streaming partitioners to handle dynamic data within a dataflow framework. This online partitioning pipeline allows complex graph or hypergraph streams to be processed asynchronously. It facilitates low latency distributed GNNs through replication and caching. Overall, the hypergraph-based optimisations in this thesis enable the development of scalable dynamic graph applications

Big Data Visualization Tools

Data visualization is the presentation of data in a pictorial or graphical format, and a data visualization tool is the software that generates this presentation. Data visualization provides users with intuitive means to interactively explore and analyze data, enabling them to effectively identify interesting patterns, infer correlations and causalities, and supports sense-making activities.Comment: This article appears in Encyclopedia of Big Data Technologies, Springer, 201

D4M 3.0: Extended Database and Language Capabilities

The D4M tool was developed to address many of today's data needs. This tool is used by hundreds of researchers to perform complex analytics on unstructured data. Over the past few years, the D4M toolbox has evolved to support connectivity with a variety of new database engines, including SciDB. D4M-Graphulo provides the ability to do graph analytics in the Apache Accumulo database. Finally, an implementation using the Julia programming language is also now available. In this article, we describe some of our latest additions to the D4M toolbox and our upcoming D4M 3.0 release. We show through benchmarking and scaling results that we can achieve fast SciDB ingest using the D4M-SciDB connector, that using Graphulo can enable graph algorithms on scales that can be memory limited, and that the Julia implementation of D4M achieves comparable performance or exceeds that of the existing MATLAB(R) implementation.Comment: IEEE HPEC 201

Benchmarking SciDB Data Import on HPC Systems

SciDB is a scalable, computational database management system that uses an array model for data storage. The array data model of SciDB makes it ideally suited for storing and managing large amounts of imaging data. SciDB is designed to support advanced analytics in database, thus reducing the need for extracting data for analysis. It is designed to be massively parallel and can run on commodity hardware in a high performance computing (HPC) environment. In this paper, we present the performance of SciDB using simulated image data. The Dynamic Distributed Dimensional Data Model (D4M) software is used to implement the benchmark on a cluster running the MIT SuperCloud software stack. A peak performance of 2.2M database inserts per second was achieved on a single node of this system. We also show that SciDB and the D4M toolbox provide more efficient ways to access random sub-volumes of massive datasets compared to the traditional approaches of reading volumetric data from individual files. This work describes the D4M and SciDB tools we developed and presents the initial performance results. This performance was achieved by using parallel inserts, a in-database merging of arrays as well as supercomputing techniques, such as distributed arrays and single-program-multiple-data programming.Comment: 5 pages, 4 figures, IEEE High Performance Extreme Computing (HPEC) 2016, best paper finalis