Introduction to STINGER
- 2. Contributors • David Bader • David Ediger • Rob McColl • Jason Riedy • Kamesh Madduri • Jason Poovey
- 3. Outline • Motivation • Dynamic Graph Basics • What is STINGER? • What can STINGER do? • Why STINGER?
- 4. Big Data problems need Graph Analysis Health Care • Finding outbreaks, population epidemiology Social Networks • Advertising, searching, grouping, influence Intelligence • Decisions at scale, regulating algorithms Systems Biology • Understanding interactions, drug design Power Grid • Disruptions, conversion Simulation • Discrete events, cracking meshes
- 5. Graphs are pervasive • Graphs: things and relationships • Different kinds of things, different kinds of relationships, but graphs provide a framework for analyzing the relationships. • New challenges for analysis: data sizes, heterogeneity, uncertainty, data quality. Astrophysics Bioinformatics Social Informatics Problem: Outlier detection Problem: Problem: Emergent behavior, Challenges: Massive data Identifying target proteins information spread sets, temporal variation Challenges: Challenges: New analysis, Graph Problems: matching, Data heterogeneity, quality data uncertainty, scale clustering Graph Problems: Graph Problems: clustering, Centrality, clustering flows, shortest paths
- 6. Data rates and volumes are immense • Facebook: • ~1 billion users • average 130 friends • 30 billion pieces of content shared / month • Twitter: • 500 million active users • 340 million tweets / day • Internet – 100s of exabytes / year • 300 million new websites per year • 48 hours of video to You Tube per minute • 30,000 YouTube videos played per second
- 7. Our focus is streaming graphs • As relationships change • Edges (relationships) are inserted, updated, and removed • New vertices (things) join and leave the network • What are the effects? • On information flow • On community structure z x y • On the integrity of data and structure • Which actors and relationships are… • The key players and influencers in the change? • The anomalies and threats?
- 8. What is STINGER? Spatio-Temporal Interaction Networks and Graphs Extensible Representation D. A. Bader, J. Berry, A. Amos-Binks, D. Chavarr´ıa-Miranda, C. Hastings, K. Madduri, S. C. Poulos • A scalable, high performance in-memory dynamic graph data structure • Stores semantic and temporal information. • Designed to be flexible and extendable. • Be useful for the entire “large graph” community. • Permit good performance: No single structure is optimal for all. • Assume globally addressable memory access. • Support multiple, parallel readers and a single parallel writer. • A software suite for dynamic graph analysis • Targets large shared-memory x86 and the Cray XMT • Written in C with OpenMP and XMT pragma support for parallelism
- 9. As a data structure • Fast insertions, deletions, and updates: A data structure that grows and changes at the speed of the data. • Edge and vertex types and weights: Represent complex relationships and multiple simultaneous networks. • Filtering traversal mechanisms: Traverse serially or in parallel on specific edge types, time ranges, vertex sets, etc. • Experimental workflow server: Multiple data streams and analytics with one persistent data structure. • Experimental Java and Python bindings: Use efficiency-oriented languages without sacrificing performance- oriented results.
- 10. As an analysis package • Streaming edge insertions and deletions: Performs new edge insertions, updates, and deletions in batches or individually. • Streaming clustering coefficients: Tracks the local and global clustering coefficients of a graph under both edge insertions and deletions. • Streaming connected components: Accurately tracks the connected components of a graph with insertions and deletions. • Streaming community detection: Track and update the community structures within the graph as they change. • Parallel agglomerative clustering: Find clusters that are optimized for a user-defined edge scoring function. • Streaming Betweenness Centrality: Find the key points within information flows and structural vulnerabilities. • K-core Extraction: Extract additional communities and filter noisy high-degree vertices. • Classic breadth-first search: Performs a parallel breadth-first search of the graph starting at a given source vertex to find shortest paths.
- 11. How is the graph stored?
- 12. What can STINGER represent? • Nearly any set of relationships • Healthcare • Social Networks • Intelligence • Systems biology • Power grid • Travel networks • Example: Twitter • Users, hashtags, tweets as vertex types • Authorship, retweet, mentions, follows / followed by edge types • Example: Work Environment • Users, PCs, printers, emails, URLs, files, etc. as vertex types • Email alias, from, to, access, logon/off, print, IM, etc. as edge types
- 13. What can STINGER do? • Optimized to update at rates of over 3 million edges per second on graphs of one billion edges • D. Ediger, R. McColl, J. Riedy, and D.A. Bader, "STINGER: High Performance Data Structure for Streaming Graphs,'' The IEEE High Performance Extreme Computing Conference (HPEC), Waltham, MA, September 20- 22, 2012. Best Paper Award. RMAT – Recursive MATrix graph generator. RMAT(N) indicates 2^N vertices.
- 14. What can STINGER do? • Maintaining connected components in a graph of half a billion edges • Up to 1.26 million updates per sec. • 137x faster than recomputing. • Scalable parallel streaming community detection • Built on parallel insert / delete mechanisms. • Streaming approximate betweenness • Used to analyze influencers on Twitter during Hurricane Sandy over time.
- 15. What does STINGER not do? • Does not provide all ACID properties • Why: Not intended to be the backing data store. • Why: Allows for greater ingest and processing speeds. • Alternative: Back STINGER ingest with an ACID DB • Alternative: STINGER does provide consistency, partial isolation • No text base query language – for now • Why: Currently, no language is general enough to describe most or all queries • Alternative: Filtering traversal APIs, unlimited query flexibility through code • Alternative: Productivity language bindings (Python, Java) • No distributed / Hadoop-like cluster support • Why: Good fit for ingest, but poor for streaming analysis, random access is too slow • Alternative: Larger shared memory systems such as the Cray XMT and SGI UV systems • Alternative: Processing billion-edge graphs in shared memory on affordable Intel servers • Alternative: Extract key portions of the graph from a larger data store and perform fast in- memory processing in STINGER
- 16. What sizes, performance can it handle? Server 4x Opteron 6282 256GB DDR3 Desktop (Intel Core i7-2600 16GB DDR3) Connected Updates V E Config Size (GB) Connected Updates Components (s) per Sec. V E Config Size (GB) Components (s) per Sec. 16M 512M 25-14 60GB 13.7 696K 1M 8M 22-14 1.184 0.316 2.7M 16M 256M 25-14 24.6GB 9.82 2.1M 2M 16M 22-14 2.384 0.75 2.3M 4M 33M 22-14 4.768 2 2.3M Cray XMT2 – 64 Processors 2TB DDR2 8M 67M 24-14 9.536 5.36 0.85M Connected Updates V E Config Size (GB) Components (s) per Sec. 4M 67M 24-14 7.984 3 1.38M 67M 512M 28-32 86GB 13.8 3.3M 4M 134M 24-14 14.336 5.7 0.8M 268M 4.3B 28-32 312GB 52.3 2.34M • The only limitation on size is system memory • Billions of vertices and edges are possible • V vertices and E edges in each graph • E counts are undirected • STINGER stores both directions • Config is STINGER-specific parameters
- 17. Why not existing technologies? • Traditional SQL databases • Not structured to do any meaningful graph queries with any level of efficiency or timeliness • Graph databases - mostly on-disk • Distributed disk can keep up with storing / indexing, but is simply too slow at random graph access to process on as the graph updates • Hadoop and HDFS-based projects • Not really the right programming model for many structural queries over the entire graph, random access performance is poor • Smaller graph libraries, processing tools • Can't scale, can't process dynamic graphs, frequently leads to impossible visualization attempts
- 18. Who is GTRI? • Georgia Tech Research Institute • Largest research entity at Georgia Institute of Technology • One of the world's premier university-based applied R&D organizations for 75 years • Non-profit with over 1,600 employees and 21 locations world-wide • Over $240 million per year of government and industry contracts • Innovative Computing Division of the Cyber Technology and Information Security Lab • Dedicated to the application of practical HPC expertise and cutting-edge fundamental research to solve real-world problems • Experts in high-performance computing, algorithms, and big data
- 19. How can I start using STINGER? • Information, code, help • http://cc.gatech.edu/stinger • robert.mccoll@gtri.gatech.edu • Together, GTRI and Georgia Tech can offer • Consulting Understand how your organization can benefit from graph analytics. • Training Learn how to use graph analysis and apply STINGER to your data. • Implementation Customize and extend STINGER to suit your needs using our experts. • Research Expertise Connect with researchers on the cutting edge of big data to develop novel solutions to your open problems.