GraphH: High Performance Big Graph Analytics in Small Clusters

It is common for real-world applications to analyze big graphs using distributed graph processing systems. Popular in-memory systems require an enormous amount of resources to handle big graphs. While several out-of-core approaches have been proposed for processing big graphs on disk, the high disk I/O overhead could significantly reduce performance. In this paper, we propose GraphH to enable high-performance big graph analytics in small clusters. Specifically, we design a two-stage graph partition scheme to evenly divide the input graph into partitions, and propose a GAB (Gather-Apply-Broadcast) computation model to make each worker process a partition in memory at a time. We use an edge cache mechanism to reduce the disk I/O overhead, and design a hybrid strategy to improve the communication performance. GraphH can efficiently process big graphs in small clusters or even a single commodity server. Extensive evaluations have shown that GraphH could be up to 7.8x faster compared to popular in-memory systems, such as Pregel+ and PowerGraph when processing generic graphs, and more than 100x faster than recently proposed out-of-core systems, such as GraphD and Chaos when processing big graphs

Towards Scalable Visual Exploration of Very Large RDF Graphs

In this paper, we outline our work on developing a disk-based infrastructure for efficient visualization and graph exploration operations over very large graphs. The proposed platform, called graphVizdb, is based on a novel technique for indexing and storing the graph. Particularly, the graph layout is indexed with a spatial data structure, i.e., an R-tree, and stored in a database. In runtime, user operations are translated into efficient spatial operations (i.e., window queries) in the backend.Comment: 12th Extended Semantic Web Conference (ESWC 2015

Large Graph Analysis in the GMine System

Current applications have produced graphs on the order of hundreds of thousands of nodes and millions of edges. To take advantage of such graphs, one must be able to find patterns, outliers and communities. These tasks are better performed in an interactive environment, where human expertise can guide the process. For large graphs, though, there are some challenges: the excessive processing requirements are prohibitive, and drawing hundred-thousand nodes results in cluttered images hard to comprehend. To cope with these problems, we propose an innovative framework suited for any kind of tree-like graph visual design. GMine integrates (a) a representation for graphs organized as hierarchies of partitions - the concepts of SuperGraph and Graph-Tree; and (b) a graph summarization methodology - CEPS. Our graph representation deals with the problem of tracing the connection aspects of a graph hierarchy with sub linear complexity, allowing one to grasp the neighborhood of a single node or of a group of nodes in a single click. As a proof of concept, the visual environment of GMine is instantiated as a system in which large graphs can be investigated globally and locally

A Monitoring System for the BaBar INFN Computing Cluster

Monitoring large clusters is a challenging problem. It is necessary to observe a large quantity of devices with a reasonably short delay between consecutive observations. The set of monitored devices may include PCs, network switches, tape libraries and other equipments. The monitoring activity should not impact the performances of the system. In this paper we present PerfMC, a monitoring system for large clusters. PerfMC is driven by an XML configuration file, and uses the Simple Network Management Protocol (SNMP) for data collection. SNMP is a standard protocol implemented by many networked equipments, so the tool can be used to monitor a wide range of devices. System administrators can display informations on the status of each device by connecting to a WEB server embedded in PerfMC. The WEB server can produce graphs showing the value of different monitored quantities as a function of time; it can also produce arbitrary XML pages by applying XSL Transformations to an internal XML representation of the cluster's status. XSL Transformations may be used to produce HTML pages which can be displayed by ordinary WEB browsers. PerfMC aims at being relatively easy to configure and operate, and highly efficient. It is currently being used to monitor the Italian Reprocessing farm for the BaBar experiment, which is made of about 200 dual-CPU Linux machines.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 10 pages, LaTeX, 4 eps figures. PSN MOET00

GraphMP: An Efficient Semi-External-Memory Big Graph Processing System on a Single Machine

Recent studies showed that single-machine graph processing systems can be as highly competitive as cluster-based approaches on large-scale problems. While several out-of-core graph processing systems and computation models have been proposed, the high disk I/O overhead could significantly reduce performance in many practical cases. In this paper, we propose GraphMP to tackle big graph analytics on a single machine. GraphMP achieves low disk I/O overhead with three techniques. First, we design a vertex-centric sliding window (VSW) computation model to avoid reading and writing vertices on disk. Second, we propose a selective scheduling method to skip loading and processing unnecessary edge shards on disk. Third, we use a compressed edge cache mechanism to fully utilize the available memory of a machine to reduce the amount of disk accesses for edges. Extensive evaluations have shown that GraphMP could outperform state-of-the-art systems such as GraphChi, X-Stream and GridGraph by 31.6x, 54.5x and 23.1x respectively, when running popular graph applications on a billion-vertex graph