Large Graph Processing

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A lecture for cloud computing course about large graph processing, Pregel and Mizan

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  • 1. King Abdullah University of Science and Technology CS348: Cloud Computing Large-Scale Graph Processing Zuhair Khayyat 10/March/2013
  • 2. The Importance of Graphs A graph is a mathematical structure that represents pairwise relations between entities or objects. Such as: Physical communication networks Web pages links Social interaction graphs Protein-to-protein interactions Graphs are used to abstract application-specific features into a generic problem, which makes Graph Algorithms applicable to a wide variety of applications*.*http://11011110.livejournal.com/164613.html
  • 3. Graph algorithm characteristics* Data-Drivin Computations: Computations in graph algorithms depends on the structure of the graph. It is hard to predict the algorithm behavior Unstructured Problems: Different graph distributions requires distinct load balancing techniques. Poor Data Locality. High Data Access to Computation Ratio: Runtime can be dominated by waiting memory fetches.*Lumsdaine et. al, Challenges in Parallel Graph Processing
  • 4. Challenges in Graph processing Graphs grows fast; a single computer either cannot fit a large graph into memory or it fits the large graph with huge cost. Custom implementations for a single graph algorithm requires time and effort and cannot be used on other algorithms Scientific parallel applications (i.e. parallel PDE solvers) cannot fully adapt to the computational requirements of graph algorithms*. Fault tolerance is required to support large scale processing.*Lumsdaine et. al, Challenges in Parallel Graph Processing
  • 5. Why Cloud in Graph Processing Easy to scale up and down; provision machines depending on your graph size. Cheaper than buying a physical large cluster. Can be used in the cloud as Software as a services to support online social networks.
  • 6. Large Scale Graph Processing Systems that tries to solve the problem of processing large graphs in parallel: MapReduce auto task scheduling, distributed disk based computations: Pegasus X-Rime Pregel - Bulk Synchronous Parallel Graph Processing: Giraph GPS Mizan GraphLab Asynchronous Parallel Graph Processing.
  • 7. Pregel* Graph Processing Consists of a series of synchronized iterations (supersteps); based on Bulk Synchronous Parallel computing model. Each superstep consists of: Concurrent computations Communication Synchronization barrier Vertex centric computation, the users compute() function is applied individually on each vertex, which is able to: Send message to vertices in the next superstep Receive messages from the previous superstep*Malewicz et. al., Pregel: A System for Large-Scale Graph Processing
  • 8. Pregel messaging Example 1Superstep 0 A B D C
  • 9. Pregel messaging Example 1Superstep 0 Superstep 1 22 A B A B 9 15 D C D C 47
  • 10. Pregel messaging Example 1Superstep 0 Superstep 1 22 A B A B 9 15 D C D C 47Superstep 2 -2 22, 9 A B 7 55 47 D C 15 14
  • 11. Pregel messaging Example 1Superstep 0 Superstep 1 22 A B A B 9 15 D C D C 47Superstep 2 Superstep 3 -2 22, 9 5 -2, 7 A B A B 7 55 5 98 47 D C 15 14 D C 55 14 9
  • 12. Vertexs State All vertices are active at superstep 1 All active vertices runs user function compute() at any superstep A vertex deactivates itself by voting to halt, but returns to active if it received messages. Pregel terminates of all vertices are inactive
  • 13. Pregel Example 2 Data Distribution(Hash-based partitioning) Worker 1 Worker 2 Worker 3 Computation Communication Synchronization Barrier Yes No Terminate Done?
  • 14. Pregel Example 3 Max 3 6 2 1
  • 15. Pregel Example 3 Max 3 6 2 1 6 6 2 6
  • 16. Pregel Example 3 Max 3 6 2 1 6 6 2 6 6 6 6 6
  • 17. Pregel Example 3 Max 3 6 2 1 6 6 2 6 6 6 6 6 6 6 6 6
  • 18. Pregel Example 4 Max code Vertex value classClassMaxFindVertex:publicVertex{ Edge value classpublic: Message classvirtualvoidCompute(MessageIterator*msgs){intcurrMax=GetValue(); Send current MaxSendMessageToAllNeighbors(currMax); Check messagesfor(;!msgs>Done();msgs>Next()){ and store maxif(msgs>Value()>currMax)currMax=msgs>Value(); Store new max}if(currMax>GetValue())*MutableValue()=currMax;elseVoteToHalt();}};
  • 19. Pregel Message Optimizations Message Combiners: A special function that combines the incoming messages for a vertex before running compute() Can run on the message sending or receiving worker Global Aggregators : A shared object accessible to all vertices. that is synchronized at the end of each superstep, i.e., max and min aggregators.
  • 20. Pregel Guarantees Scalability: process vertices in parallel, overlap computation and communication. Messages will be received without duplication in any order. Fault tolerance through check points
  • 21. Pregels Limitations Pregels superstep waits for all workers to finish at the synchronization barrier. That is, it waits for the slowest worker to finish. Smart partitioning can solve the load balancing problem for static algorithms. However not all algorithms are static, algorithms can have a variable execution behaviors which leads to an unbalanced supersteps.
  • 22. Mizan* Graph Processing Mizan is an open source graph processing system, similar to Pregel, developed locally at KAUST. Mizan employs dynamic graph repartitioning without affecting the correctness of graph processing to rebalanced the execution of the supersteps for all types of workloads.*Khayyat et. al., Mizan: A System for Dynamic Load Balancing in Large-scaleGraph Processing