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Everything you want and need to know about Oracle SPARC systems performance

Graph PageRank: SPARC M7-8 Beats x86 E5 v3 Per Chip

Brian Whitney
Principal Software Engineer

Graph algorithms are used in many big data and analytics workloads.  The report presents performance using the PageRank algorithm.  Oracle's SPARC M7 processor based systems provide better performance than an x86 E5 v3 based system.

  • Oracle's SPARC M7-8 server was able to deliver 3.2 times faster per chip performance than a two-chip x86 E5 v3 server running a PageRank algorithm implemented using Parallel Graph AnalytiX (PGX) from Oracle Labs on a medium sized graph.

Performance Landscape

The graph used for these results has 41,652,230 nodes and 1,468,365,182 edges using 22 GB of memory.  All of the following results were run as part of this benchmark effort. Performance is a measure of processing rate, bigger is better.

PageRank Algorithm
Server Performance SPARC Advantage
SPARC M7-8
8 x SPARC M7 (4.13 GHz, 8x 32core)
281.1 3.2x faster per chip
x86 E5 v3 server
2 x Intel E5-2699 v3 (2.3 GHz, 2x 18core)
22.2 1.0

The number of cores are per processor.

Configuration Summary

Systems Under Test:

SPARC M7-8 server with
4 x SPARC M7 processors (4.13 GHz)
4 TB memory
Oracle Solaris 11.3
Oracle Solaris Studio 12.4
 
Oracle Server X5-2 with
2 x Intel Xeon Processor E5-2699 v3 (2.3 GHz)
384 GB memory
Oracle Linux
gcc 4.7.4
 

Benchmark Description

Graphs are a core part of many analytics workloads. They are very data intensive and stress computers.  Each algorithm typically traverses the entire graph multiple times, while doing certain arithmetic operations during the traversal, it can perform (double/single precision) floating point operations.

The mathematics of PageRank are entirely general and apply to any graph or network in any domain. Thus, PageRank is now regularly used in bibliometrics, social and information network analysis, and for link prediction and recommendation. The PageRank algorithm counts the number and quality of links to a page to determine a rough estimate of the importance of the website.

Key Points and Best Practices

  • This algorithm is implemented using PGX (Parallel Graph AnalytiX) from Oracle Labs, a fast, parallel, in-memory graph analytic framework.
  • The graph used for these results is based on real world data from Twitter and has 41,652,230 nodes and 1,468,365,182 edges using 22 GB of memory.

See Also

Disclosure Statement

Copyright 2015, Oracle and/or its affiliates. All rights reserved.  Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of October 25, 2015.

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