SC13 Research Highlight: Large Graph Processing Without the Overhead

By Dr. Ling Liu and Kisung Lee

November 16, 2013

Many real world information networks consist of millions or billions of vertices representing heterogeneous entities and billions or trillions of edges representing heterogeneous types of relationships among entities.

Image Source: Max Delbrück Center for Molecular Medicine

For example, the crawled Web graph is estimated to have more than 20 billions of pages (vertices) with 160 billions hyperlinks (edges). Facebook user community exceeds 1 billion users (vertices) with more than 140 billion friendship relationships (edges) in 2012.  The billion triple challenges from the Semantic Web community have put forward large collection of RDF datasets with hundreds of millions of vertices and billions of edges.

As the size and variety of information networks continue to grow in many science and engineering domains, graph computations often exceed the processing capacity of conventional hardware, software systems and tools for a number of reasons. First, graph data often exhibits higher data correlations through both direct and indirect edges and such high correlation tends to generate large size of intermediate results during graph computations. When the size of intermediate results exceeds the available memory, the out of memory problem is unavoidable. Second, the graph datasets are growing in volume, variety and velocity. The bigger the size of the graphs gets, the worse the performance will be for most of the graph computations. One open challenge in this space is how to effectively partition a large graph to enable efficient parallel processing of complex graph operations.

One of the papers to be presented at the ACM/IEEE SC13 conference, titled “Efficient data partitioning model for heterogeneous graphs in the Cloud”, proposes a flexible graph partitioning framework, called VB-partitioner. This work is co-authored by the doctorate student Kisung Lee and Prof. Dr. Ling Liu from the school of Computer Science at Georgia Institute of Technology. To make parallel graph computations highly efficient, an important design goal of VB-Partitioner is to devise graph partitioning techniques that can effectively minimize the inter-partition communication overhead and maximize the intra-partition computation capacity (local processing).

Concretely, the first prototype of the VB-Partitioner focuses on efficient processing of graph queries, namely finding all the subgraphs matching a given subgraph pattern. VB-Partitioner partitions a large graph in three steps.

  • First, it constructs three types of Vertex Blocks (in-VBs, out-VBs and bi-VBs) to capture the general graph processing locality.  Each vertex block has an anchor vertex.
  • Second, it constructs three types of k-hop Extended Vertex Blocks (in-EVBs, out-EVBs and bi-EVBs) to distribute vertex blocks with better query locality. Each EVB has one anchor vertex. It achieves query locality by employing controlled edge replication. The setting of k is determined by the radius of frequent query graphs in order to ensure that most frequently requested queries can be processed in parallel at all partitions with minimized inter-partition communication overhead.
  • Third, it partitions a graph by grouping its vertex blocks and EVBs to maximize parallelism in graph processing while ensuring load balance, controlled edge replication and fast grouping.

Four techniques are considered and compared in the context of grouping and placement of VBs and EVBs to partitions: random grouping, hash-based grouping, min-cut based grouping and high degree vertex-based grouping.  As an integral part of the VB-Partitioner, a data partition-aware query partitioning model is also developed to handle the cases where the radius of a query is larger than k. The experimental results reported in the paper demonstrate the effectiveness of VB-Partitioner in terms of query processing efficiency, data loading time and partition distribution balance.

Graph computations can be broadly classified into two categories, graph queries that find matching subgraphs of a given pattern and iterative graph algorithms that find clusters, orderings, paths or correlation patterns. The former targets at subgraph matching problems over large static graphs and the later targets at graph inference kernels that traverse the graph by iteratively updating the weight of vertices or edges, such as PageRank, shortest path algorithms, spanning tree algorithms, topological sort, and so forth.

Although the first generation of the VB-Partitioner is tailored primarily for efficient parallel processing of graph queries, the ongoing work on VB-Partitioner includes exploring the feasibility and effectiveness of VB-Partitioner in the context of iterative graph algorithms. For example, to minimize inter-partition communications and maximize parallelism in graph computation, it is crucial to optimize the shared memory by minimizing parallel overhead of synchronization barriers. It is equally important to optimize the distributed memory by bounding message buffer sizes, bundling messages, overlapping communication with computation to amortize the overhead of barriers.

image1
Image Source: Giot et al., “A Protein Interaction Map of Drosophila melanogaster”, Science 302, 1722-1736, 2003.”

In addition to exploring parallel computation opportunities through graph partitioning using multi-threads, multi-cores, multiple workers, one can also exploit and combine with other performance optimization techniques to scale large graph analytics. Example techniques include

  • Compression to provide compact storage and in-memory processing,
  • Data placements on disk and in memory to balance computation with storage, and to maximize sequential access and minimize random access,
  • Indexing at vertex and/or edge level to utilize sequential access and minimize unnecessary random access,
  • Caching at vertex, edge or query level to gain performance for repeated access.

Please come hear more on Tuesday, November 19, 2013 10:30AM – 11:00AM (Location: Room 205/207)

http://sc13.supercomputing.org/schedule/event_detail.php?evid=pap708

About the Authors

LingLing Liu is a Professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in Distributed Data Intensive Systems Lab (DiSL), examining various aspects of large scale data intensive systems. Prof. Ling Liu is an internationally recognized expert in the areas of Cloud computing, Distributed Computing, Big Data technologies, Database systems and Service oriented computing. Prof. Liu is a recipient of IEEE Computer Society Technical Achievement Award in 2012. Currently Prof. Liu is the editor in chief of IEEE Transactions on Service Computing, and serves on the editorial board of half dozen international journals, including Journal of Parallel and Distributed Computing (JPDC), ACM Transactions on Internet Technology (TOIT), ACM Transactions on Web (TWEB). Dr. Liu’s current research is primarily sponsored by NSF, IBM, and Intel.

 

luiKisung Lee is a Ph.D student in the School of Computer Science at Georgia Tech. He received his BS and MS degree in computer science from KAIST in 2005 and 2007 respectively. He had worked for ETRI as a researcher from 2007 to 2010. He is conducting research in distributed and parallel processing of big data in the Cloud, mobile computing and social network analysis.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

HOKUSAI’s BigWaterfall Cluster Extends RIKEN’s Supercomputing Performance

February 21, 2018

RIKEN, Japan’s largest comprehensive research institution, recently expanded the capacity and capabilities of its HOKUSAI supercomputer, a key resource managed by the institution’s Advanced Center for Computing and C Read more…

By Ken Strandberg

Neural Networking Shows Promise in Earthquake Monitoring

February 21, 2018

A team of Harvard University and MIT researchers report their new neural networking method for monitoring earthquakes is more accurate and orders of magnitude faster than traditional approaches. Read more…

By John Russell

HPE Wins $57 Million DoD Supercomputing Contract

February 20, 2018

Hewlett Packard Enterprise (HPE) today revealed details of its massive $57 million HPC contract with the U.S. Department of Defense (DoD). The deal calls for HPE to provide the DoD High Performance Computing Modernizatio Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Experience Memory & Storage Solutions that will Transform Your Data Performance

High performance computing (HPC) has revolutionized the way we harness insight, leading to a dramatic increase in both the size and complexity of HPC systems. Read more…

Topological Quantum Superconductor Progress Reported

February 20, 2018

Overcoming sensitivity to decoherence is a persistent stumbling block in efforts to build effective quantum computers. Now, a group of researchers from Chalmers University of Technology (Sweden) report progress in devisi Read more…

By John Russell

HOKUSAI’s BigWaterfall Cluster Extends RIKEN’s Supercomputing Performance

February 21, 2018

RIKEN, Japan’s largest comprehensive research institution, recently expanded the capacity and capabilities of its HOKUSAI supercomputer, a key resource manage Read more…

By Ken Strandberg

Neural Networking Shows Promise in Earthquake Monitoring

February 21, 2018

A team of Harvard University and MIT researchers report their new neural networking method for monitoring earthquakes is more accurate and orders of magnitude faster than traditional approaches. Read more…

By John Russell

Fluid HPC: How Extreme-Scale Computing Should Respond to Meltdown and Spectre

February 15, 2018

The Meltdown and Spectre vulnerabilities are proving difficult to fix, and initial experiments suggest security patches will cause significant performance penal Read more…

By Pete Beckman

Brookhaven Ramps Up Computing for National Security Effort

February 14, 2018

Last week, Dan Coats, the director of Director of National Intelligence for the U.S., warned the Senate Intelligence Committee that Russia was likely to meddle in the 2018 mid-term U.S. elections, much as it stands accused of doing in the 2016 Presidential election. Read more…

By John Russell

AI Cloud Competition Heats Up: Google’s TPUs, Amazon Building AI Chip

February 12, 2018

Competition in the white hot AI (and public cloud) market pits Google against Amazon this week, with Google offering AI hardware on its cloud platform intended Read more…

By Doug Black

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. Read more…

By Tiffany Trader

The Food Industry’s Next Journey — from Mars to Exascale

February 12, 2018

Global food producer and one of the world's leading chocolate companies Mars Inc. has a unique perspective on the impact that exascale computing will have on the food industry. Read more…

By Scott Gibson, Oak Ridge National Laboratory

Singularity HPC Container Start-Up – Sylabs – Emerges from Stealth

February 8, 2018

The driving force behind Singularity, the popular HPC container technology, is bringing the open source platform to the enterprise with the launch of a new vent Read more…

By George Leopold

Inventor Claims to Have Solved Floating Point Error Problem

January 17, 2018

"The decades-old floating point error problem has been solved," proclaims a press release from inventor Alan Jorgensen. The computer scientist has filed for and Read more…

By Tiffany Trader

Japan Unveils Quantum Neural Network

November 22, 2017

The U.S. and China are leading the race toward productive quantum computing, but it's early enough that ultimate leadership is still something of an open questi Read more…

By Tiffany Trader

AMD Showcases Growing Portfolio of EPYC and Radeon-based Systems at SC17

November 13, 2017

AMD’s charge back into HPC and the datacenter is on full display at SC17. Having launched the EPYC processor line in June along with its MI25 GPU the focus he Read more…

By John Russell

Researchers Measure Impact of ‘Meltdown’ and ‘Spectre’ Patches on HPC Workloads

January 17, 2018

Computer scientists from the Center for Computational Research, State University of New York (SUNY), University at Buffalo have examined the effect of Meltdown Read more…

By Tiffany Trader

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit a Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Fast Forward: Five HPC Predictions for 2018

December 21, 2017

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Read more…

By John Russell

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. Read more…

By Tiffany Trader

Leading Solution Providers

Chip Flaws ‘Meltdown’ and ‘Spectre’ Loom Large

January 4, 2018

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. T Read more…

By Tiffany Trader

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC thinking of booth visitors. Attendees of tutorials, workshops and other technical sessions will be inundated with requests for feedback. Read more…

By Andrew Jones

How Meltdown and Spectre Patches Will Affect HPC Workloads

January 10, 2018

There have been claims that the fixes for the Meltdown and Spectre security vulnerabilities, named the KPTI (aka KAISER) patches, are going to affect applicatio Read more…

By Rosemary Francis

GlobalFoundries, Ayar Labs Team Up to Commercialize Optical I/O

December 4, 2017

GlobalFoundries (GF) and Ayar Labs, a startup focused on using light, instead of electricity, to transfer data between chips, today announced they've entered in Read more…

By Tiffany Trader

Tensors Come of Age: Why the AI Revolution Will Help HPC

November 13, 2017

Thirty years ago, parallel computing was coming of age. A bitter battle began between stalwart vector computing supporters and advocates of various approaches to parallel computing. IBM skeptic Alan Karp, reacting to announcements of nCUBE’s 1024-microprocessor system and Thinking Machines’ 65,536-element array, made a public $100 wager that no one could get a parallel speedup of over 200 on real HPC workloads. Read more…

By John Gustafson & Lenore Mullin

Flipping the Flops and Reading the Top500 Tea Leaves

November 13, 2017

The 50th edition of the Top500 list, the biannual publication of the world’s fastest supercomputers based on public Linpack benchmarking results, was released Read more…

By Tiffany Trader

V100 Good but not Great on Select Deep Learning Aps, Says Xcelerit

November 27, 2017

Wringing optimum performance from hardware to accelerate deep learning applications is a challenge that often depends on the specific application in use. A benc Read more…

By John Russell

SC17: Singularity Preps Version 3.0, Nears 1M Containers Served Daily

November 1, 2017

Just a few months ago about half a million jobs were being run daily using Singularity containers, the LBNL-founded container platform intended for HPC. That wa Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Share This