Comparing pregel related systems
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The document summarizes preliminary results from a project comparing the performance of open source implementations of Pregel and related graph processing systems (Hama, Giraph, GPS) on single-source shortest path (SSSP) and PageRank algorithms. Initial results show that Hama does not scale well to larger graphs, while Giraph and GPS scale better. Further analysis of memory usage, network traffic, additional systems like GraphLab and Signal/Collect, and using Green-Marl to generate code for Giraph and GPS is still in progress.
![In-Progress
● Output validation
● Memory footprint
● Network utilization (num. messages)
● GraphLab and Signal/Collect
● Green-Marl?
○ (DSL) → [Compiler] → (Giraph, GPS)](https://image.slidesharecdn.com/comparingpregelrelatedsystems-140908183405-phpapp01/85/Comparing-pregel-related-systems-20-320.jpg)
Comparing pregel related systems
- 1. Comparison and Evaluation of Open Source Implementations of Pregel and Related Systems December 2, 2013 Joshua Woo, Prashant Raghav, Vishnu Prathish David R. Cheriton School of Computer Science University of Waterloo
- 2. Outline ● Motivation ● Our Project ● Setup ● Preliminary Results ● Preliminary Analysis ● In-Progress ● References
- 3. Motivation Recall: Pregel ● Large-scale graph processing system ● Fault-tolerant framework for graph algorithms ● MapReduce for graph operations? ● Vertex-centric model (“think like a vertex”)
- 4. Motivation ● Pregel is proprietary ● Many open source graph processing systems ○ Pregel clones ○ Pregel-inspired ○ BSP
- 5. Motivation ● Apache Hama ● Signal/Collect ● Apache Giraph ● GPS ● GraphLab ● Phoebus ● GoldenOrb ● HipG ● Mizan
- 6. Motivation System Impl. Language Type Apache Hama Java Pure BSP framework Signal/Collect Scala Pregel inspired Apache Giraph Java Pregel clone GPS Java Advanced Pregel clone GraphLab C++ Pregel inspired Phoebus Erlang Pregel clone GoldenOrb Java Pregel clone HipG Java Advanced Pregel clone Mizan C++ Advanced Pregel clone
- 7. Motivation ● How do these systems compare? ○ In terms of performance (runtime)? ○ In terms of memory footprint? ○ In terms of network utilization (num. messages)? ○ Variables: ■ Algorithm ■ Graph size (number of vertices) ■ Cluster size
- 8. Our Project ● Compare at least 3 systems ○ Apache Hama - general BSP framework ○ Apache Giraph - Hadoop Map-only job, Facebook ○ GPS - +dynamic repartitioning, +multi vertex-centric ○ Signal/Collect - +edges, +async computations ○ GraphLab ○ Mizan
- 9. Our Project ● Measure the runtime of at least two algorithms on each system ○ PageRank ■ Fixed number of supersteps = 30 ○ Single Source Shortest Path (SSSP) ○ k-means clustering
- 10. Setup ● Experiments on AWS ○ Ubuntu 12.04 m1.medium EC2 instances ■ 2 ECUs, 1 vCPU, 3.7 GiB memory, moderate network performance ■ 8 GiB EBS volume per instance ○ Cluster sizes: ■ Single-node cluster ■ 4-node cluster ■ 8-node cluster
- 11. Setup ● Experiments on AWS ○ 5 runs per dataset per algorithm per cluster ■ 35 runs per algorithm per cluster ■ 70 runs per cluster ■ 140 runs in total (single-node, 4-node) ● TODO: another 70 runs (8-node)
- 12. Setup ● Dataset ○ 7 datasets ■ tinyEWD: 8 vertices 15 edges ■ mediumEWD: 250 vertices 2,546 edges ■ 1000EWD: 1,000 vertices 16,866 edges ■ rome99: 3,353 vertices 8,870 edges ■ 10000EWD: 10,000 vertices 16,866 edges ■ NYC: 264,346 vertices 733,846 edges ■ largeEWD: 1,000,000 vertices 15,172,126 edges ○ Source: http://algs4.cs.princeton.edu/44sp/
- 13. Setup ● Systems ○ Hama ■ Hadoop 1.03.0 ■ Hama 0.6.3 ○ Giraph ■ Hadoop 0.20.203rc1 ■ Giraph (trunk@37bc2c80564b45d7e4ce95db76f5411a6b8bdb3a) ○ GPS ■ Hadoop 0.20.203rc1 ■ GPS (trunk@Revision 112)
- 14. Setup ● Input Graph ○ Source files converted into format suitable for each system ■ Time for this conversion excluded from results: ● Conversion done before algorithms are run (pre-processing?) ● Negligible for largeEWD (1,000,000 vertices, 15,172,126 edges)
- 15. Preliminary Results Average SSSP runtime on 4-node cluster (in seconds) Dataset Hama Giraph GPS tinyEWD 14.17 41.60 14.40 mediumEWD 16.36 44.00 36.00 1000EWD 18.06 48.80 46.60 rome99 22.95 66.00 50.00 10000EWD 25.32 67.40 55.00 NYC 165.01 267.00 310.00 largeEWD 6,109.20 602.80 618.70
- 16. Preliminary Results SSSP runtime vs. graph size (num. vertices)
- 17. Preliminary Results Average PageRank (30 supersteps) runtime on 4-node cluster (in seconds) Dataset Hama Giraph GPS tinyEWD 29.36 49.40 58.57 mediumEWD 30.26 53.40 60.42 1000EWD 37.86 54.60 61.03 rome99 29.35 56.20 61.80 10000EWD 302.33 61.80 64.80 NYC 1,001.24 134.40 68.69 largeEWD Failed 2,100.00 1,213.56
- 18. Preliminary Results PageRank runtime vs. graph size (num. vertices)
- 19. Preliminary Analysis ● A point of resource crunch ○ No significant change in performance until a point ● Hama does not scale well (vertices ~10^4) ● Giraph and GPS scale better ● In general, PageRank runtime > SSSP runtime ● GPS input reader does not guarantee true partitioning for large datasets ● Which ‘knobs’ to keep constant? - Optimization vs. Comparability
- 20. In-Progress ● Output validation ● Memory footprint ● Network utilization (num. messages) ● GraphLab and Signal/Collect ● Green-Marl? ○ (DSL) → [Compiler] → (Giraph, GPS)
- 21. Questions?
- 22. Extras
- 23. Preliminary Results Number of supersteps for SSSP Dataset Hama Giraph GPS tinyEWD 10 7 7 mediumEWD 16 13 18 1000EWD 27 25 23 rome99 105 102 18 10000EWD 85 80 64 NYC 671 905 438 largeEWD 806 670 730
- 24. Preliminary Results Number of supersteps for SSSP
- 25. Really, really Preliminary PageRank runtime (in seconds) on GPS: native vs. Green-Marl generated Dataset Native Green-Marl generated tinyEWD 58.57 60.20 mediumEWD 60.42 60.11 1000EWD 61.03 62.30 rome99 61.80 62.32 10000EWD 64.80 65.78 NYC 68.69 71.34 largeEWD 1,213.56 -
- 26. Really, really Preliminary PageRank runtime (in seconds) on GPS: native vs. Green-Marl generated
- 27. References ● Our Project Proposal ● http://algs4.cs.princeton.edu/44sp/ ● https://github.com/apache/hadoop-common ● https://github.com/apache/giraph ● https://subversion.assembla.com/svn/phd-projects/ gps/trunk/ ● http://ppl.stanford.edu/main/green_marl.html