Sherlock: Unlocking the Secrets of Big Data

Protein-protein interactions in yeast, forming a relatively small graph of only 7,182 edges, illustrate the complexity of problems in graph analytics. (See Vladimir Batagelj & Andrej Mrvar (2006): Pajek datasets)

Computational analysis that discovers underlying patterns in “big data” can open many doors to understanding, such as how genes work, the dynamics of social networks, and the source of breaches in computer security. With this kind of analysis, based on a mathematical approach called “graph theory,” interconnected webs of information can be represented as graphs, wherein nodes represent data elements and edges represent relationships among them.

Such graphs produced from real-world data can be huge, containing billions or trillions of edges. Even more challenging, these graphs typically can’t be partitioned; their high connectivity prevents dividing them into subgraphs that can be practically mapped onto distributed-memory computers. “Graph analytics are notoriously difficult,” says Nick Nystrom, PSC’s director of strategic applications, “because following unpredictable paths from node-to-node is rate-limited by latencies to remote and local memory, which has drastically limited the graph problems that can be tackled.”

To break the barrier blocking large-scale graph analytics, PSC this year introduced Sherlock, a unique supercomputer specialized for complex analytics on big data, which will be used for pilot projects by the national research community.

Sherlock: The Details 

Acquired through NSF’s Strategic Technologies for Cyberinfrastructure program, Sherlock is a YarcData uRiKA (“Universal RDF Integration Knowledge Appliance”) data appliance. It features massive multi-threading, shared memory, and hardware optimizations to enable exceptionally efficient execution of graph algorithms. Sherlock contains 32 next-generation Cray XMT nodes. Aggregate shared memory is one terabyte, which can accommodate a graph of approximately 10 billion edges. 

PSC customized Sherlock via additional Cray XT5 nodes having AMD Opteron processors to add valuable support for heterogeneous applications that use the XMT nodes as accelerators for graph-based algorithms. This heterogeneous capability will enable an even broader class of applications, for example in genomics, astrophysics, and other types of analysis of complex networks. 

Sherlock runs an enhanced suite of familiar semantic web software for easy access to powerful analytic functionality, using the Resource Description Framework (RDF) as a very general and expressive data format. Sherlock also supports common programming languages such as C, C++, Java, Fortran, and scripting languages. 

© Pittsburgh Supercomputing Center, Carnegie Mellon University, University of Pittsburgh
300 S. Craig Street, Pittsburgh, PA 15213 Phone: 412.268.4960 Fax: 412.268.5832

This page last updated: October 28, 2012