Recent Updates on IBM System G — GraphBIG and Temporal Data by Yinglong Xia

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Recent Updates on IBM System G
— GraphBIG and Temporal Data
Yinglong Xia
IBM T.J. Watson Research Center
Yorktown Heights, NY 10598

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IBM T.J. Watson Research Center

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Using LDBC-SNB for GraphBIG
• GraphBIG = Graph Benchmark Suite from IBM System G and GaTech HPArch
• A wide selection of workloads from both CPU and GPU
• Workload ranging from graph traversal to Gibbs Sampling on Bayesian Network
• Illustrating processor architecture impact using h/w performance counter
• Fix input data and implementation
• Show performance profiling at processor architecture level

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Beyond the Benchmarking for Graph DBs
• Graph computing was barely considered in architecture design
• Increasing motivation due to popularity of graph analytics
• Impact of architecture requires fixed input data and analytic implementation

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Demanding Graph
• Interactions of entities in many big data applications are naturally modeled by property graphs
• Evolution of graph structure and properties over time usually provides useful information, which needs
to be maintained for query or analytics
• Graph analytics market grows increasingly fast as well as the graph data size and complexity, but
near real time response is typically required
Xiaoyan Fu, Seok-Hee Hong, Nikola S. Nikolov, Xiaobin Shen, Ying Xin Wu and Kai Xu,
Visualization and Analysis of Email Networks, Proceedings of APVIS 2007, IEEE, pp.1-8, 2007.

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Use Case: Forensic Analysis on Individual Status
• Recover the dynamics of individual status
• Evaluate status measures, anomalies, etc.
• Propagate known status measures
• Estimate labels for each person at each time stamp
• Aggregate the received measures
Chain Graph: A collection of graphs on
contiguous time steps

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Use Case: Bitemporal Data Exploration
• Support the valid dimension and the transaction dimension
• Audit trail of what you know and when did you know
• History of how history from a business perspective was stored in the
database
http://bitemporalmodeling.com/temporal-data-blog/

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Graph Data Management
SparkseeNeo4j
Titan

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Organization of Graph Store

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Organize Temporal Graph Data Name Default Value
vertex_history Disabled
num_vertex_property_bundles 0
edge_history Disabled
num_edge_property_bundles 0
… …
Flag
(uint8)
inEdge
(uint64)
inEdge Count
(uint16)
outEdge
(uint64)
outEdge Count
(uint16)
Property
(uint64)
Property Count
(unit64)
History
(unit64)
…
…
…
Vertex Record Table
inEdge List
Flag Property Property
Count
History …
…
…
Prev Edge_list_buffer<EID,VID,LID>
…
…
Edge Record Table
Accessed Vertex
Record by VID *
izeof (VtxRec)
inEdgeCount * sizeof (<EID,VID,LID>)
point to the buffer end
Accessed Edge
Record by EID *
izeof (EdgeRec)
Vertex Property Table
Prev property_buffer
…
…
PropertyCount
point to the buffer end
Edge Property Table
Prev property_buffer
…
…
Name Default
Value
min_VID 0
max_VID
min_EID 0
Max_EID
… …
Local Configuration

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Pointer Jumping in Temporal Graph Inference
• Converting a temporal graph into tridiagonal system
• Forward Gaussian elimination by propagation
• Backward substitution to produce solutions
• A Parallel Solution to Thomas Algorithm
• Apply pointer jumping to Thomas algorithm
• Logarithmic speedup
parallel solution to solve a tridiagonal linear system
• Converting a chain graph into tridiagonal system
• Forward Gaussian elimination by propagation
• Backward substitution to produce solutions
• A Parallel Solution to Thomas Algorithm
• Apply pointer jumping to Thomas algorithm
• Logarithmic speedup
• Propagate belief among vertices within and cross
time stamps
Speedup wrt Gaussian
Elimination: T3 / logT

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Comments and Questions?