A Hierarchical Synchronous Parallel Model for Wide-Area Graph Analytics
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1 A Hierarchical Synchronous Parallel Model for Wide-Area Graph Analytics Shuhao Liu*, Li Chen, Baochun Li, Aiden Carnegie University of Toronto April 17, 2018
2 Graph Analytics What is Graph Analytics? 2
3 Graph Analytics What is Graph Analytics? 2
4 Graph Analytics What is Graph Analytics? Shortest Path 2
5 Graph Analytics What is Graph Analytics? Shortest Path 2
6 Graph Analytics What is Graph Analytics? Shortest Path PageRank 2
7 Graph Analytics What kind of graph are we interested in? 3
8 4
9 Large 4
10 Large Geo-Distributed 4
11 Analyze Large Data Process it in parallel (Spark, Hadoop ) 5
12 Analyze Large Data Process it in parallel (Spark, Hadoop ) Parallel graph analytics frameworks: 5
13 Analyze Large Data Process it in parallel (Spark, Hadoop ) Parallel graph analytics frameworks: State-of-the-art: Gemini [OSDI 16 ], etc. 5
14 Analyze Large Data Process it in parallel (Spark, Hadoop ) Parallel graph analytics frameworks: State-of-the-art: Gemini [OSDI 16 ], etc. Assume: 5
15 Analyze Large Data Process it in parallel (Spark, Hadoop ) Parallel graph analytics frameworks: State-of-the-art: Gemini [OSDI 16 ], etc. Assume: Data is accessible via fast network 5
16 Analyze Large Data Process it in parallel (Spark, Hadoop ) Parallel graph analytics frameworks: State-of-the-art: Gemini [OSDI 16 ], etc. Assume: Data is accessible via fast network A cluster of well-connected workers 5
17 Analyze Geo-Distributed Data 6
18 Analyze Geo-Distributed Data Reduce WAN traffic 6
19 Analyze Geo-Distributed Data Reduce WAN traffic Wide-area data analytics Placement: Iridium [SIGCOMM 15 ], etc. Workload: Clarinet [OSDI 16 ], Gaia [NSDI 17 ] 6
20 Analyze Geo-Distributed Graph Reduce WAN traffic Wide-area graph analytics Placement: Mayer et al. [ICDCS 16 ], Zhou et al. [ICDCS 17 ] Workload:? 7
21 Can we optimize the workload with the awareness of WAN transfers? 8
22 Motivating Example Connected Components: BSP in two regions WAN transfer: 9
23 Motivating Example Connected Components: BSP in two regions WAN transfer: 3 9
24 Motivating Example Connected Components: BSP in two regions WAN transfer: 3 2 9
25 Motivating Example Connected Components: BSP in two regions WAN transfer:
26 Motivating Example Connected Components: BSP in two regions WAN transfer:
27 Motivating Example Connected Components: sync locally -> globally WAN transfer: 10
28 Motivating Example Connected Components: sync locally -> globally WAN transfer: 10
29 Motivating Example Connected Components: sync locally -> globally WAN transfer: 1 10
30 Motivating Example Connected Components: sync locally -> globally WAN transfer: 1 10
31 HSP: Design Two modes of synchronization: local + global Local synchronization Keep mirrored vertices static, compute locally without WAN communication Global synchronization 11
32 HSP: Analysis Convergence We have proven HSP has the same convergence guarantee as BSP WAN traffic / # Global Synchronizations We have proven HSP has a much higher rate of convergence with the same amount of WAN traffic (if BSP converges linearly or super-linearly) 12
33 Synchronization Models 13
34 Synchronization Models Convergence Volume of WAN Traffic 13
35 Synchronization Models BSP Convergence GraphLab Graph++ GraphUC Volume of WAN Traffic 13
36 Synchronization Models BSP Convergence Graph++ GraphLab GraphUC Volume of WAN Traffic 13
37 Synchronization Models HSP BSP Convergence Graph++ GraphLab GraphUC Volume of WAN Traffic 13
38 Proof-of-Concept PageRank example DC A DC B kx (k) x k BSP HSP Local updates Global updates # Global Synchronizations 14
39 Implementation in GraphX Input Graph Accumulator c = 0 Graph Partition Launch n DC Manager Threads Mode Select Local 1 Local Update Global # updates = diameter? Yes c = c+1 No 1 Global Update Converged? Yes c = c+n No No Converged? Yes No c > n Yes DC Manager Thread Output Graph Join n Threads 15
40 Experimental Results 16
41 Experimental Results 16
42 Experimental Results 16
43 Experimental Results 16
44 Experimental Results 16
45 Experimental Results Monetary cost 17
46 HSP: Takeaways HSP = BSP + local mode WAN efficiency: faster && cheaper Correctness: strong convergence guarantee Transparency: independent from apps 18
47 Thanks! Q & A 19
48 HSP: Design Switch local -> global When all local partitions have run d iterations When a local partition that has already converged Run one global iteration and switch back to local mode 20
49 Runtime 21
50 Experimental Results Rate of convergence kx (k) x (k 1) k BSP HSP kx (k) x (k 1) k BSP HSP (a) # Global Synchronizations (b) PageRank Execution Time (s) 22
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