The document discusses three projects related to analyzing large graph datasets: 1. The CASS-MT project designs software to analyze massive interaction networks using multithreaded architectures like the Cray XMT. Algorithms include betweenness centrality and dynamic clustering coefficients. 2. The Graph500 benchmark was developed to evaluate parallel architectures for data-intensive graph computations. Reference codes were provided for OpenMP and Cray XMT. 3. The STING project develops and optimizes a dynamic graph package for Intel platforms to analyze streaming graph-structured data from sources like Facebook in real-time.

1. [ CENTER FOR ADVANCED SUPERCOMPUTING SOFTWARE FOR MULTITHREADED ARCHITECTURES (CASS-MT) ] [ OBJECTIVE ] To design software for the analysis of massive-scale spatio-temporal interaction networks using multithreaded architectures such as the Cray XMT. The Center launched in July 2008 and is led by Pacific-Northwest National Laboratory. [ DESCRIPTION ] We are designing and implementing advanced, scalable algorithms for static and dynamic graph analysis, including generalized k-betweenness centrality and dynamic clustering coefficients. [ HIGHLIGHTS ] On a 64-processor Cray XMT, k-betweenness centrality scales nearly linearly (58.4x) on a graph with 16M vertices and 134M edges. Initial streaming clustering coefficients handle around 200k updates/sec on a similarly sized graph. [ FUNDING ] Pacific Northwest National Laboratory David A. Bader (PI), David Ediger, Karl Jiang, Jason Riedy Our research is focusing on temporal analysis, answering questions about changes in global properties ( e.g. diameter) as well as local structures (communities, paths). Image Courtesy of Cray, Inc.

3. Photo © Intel Photo © CTL Corp. Photo © Intel [ STING: Spatio-Temporal Interaction Networks and Graphs An open-source dynamic graph package for Intel platforms ] [ OBJECTIVE ] Develop and tune the STING package to analyze streaming, graph-structured data for Intel multi- and manycore platforms. [ DESCRIPTION ] The New York Stock Exchange generates 1.5TB of data daily. Facebook users add 30 billion pieces of linked data per month. Facebook's 500M users with 130 'friend' links per user form an ever-changing graph requiring almost 1TB of storage storing only connectivity data. Running repeated static analysis over memory interfaces like QPI cannot keep pace with the dynamic changes in the graph-structured data. STING is tackling the streaming data problem using Intel multi- and manycore platforms.. [ FUNDING ] Intel, Parallel Algorithms in Non-Numeric Computing David A. Bader (PI), Jason Riedy

4. Photo © NCSA/U. Illinoisw [ DYNAMIC GRAPH DATA STRUCTURES IN X10 ] [ OBJECTIVE ] Attack next-generation dynamic graph analysis with the next-generation parallel programming system X10. [ DESCRIPTION ] The vast amount of graph-structured data desperate for analysis swamps single memory images. Analysts in companies and elsewhere rely on simple analysis forced onto simplistic programming paradigms. X10's partitioned global address space (PGAS) model simplifies global data access while mapping onto common platforms. Georgia Tech is exploring the space of dynamic graph data structures using X10's parallel, asynchronous programming facilities. The work will be applicable anywhere X10 runs, from supercomputers to clusters to workstations. [ FUNDING ] IBM X10 Innovation Award David A. Bader (PI), Jason Riedy