1. Work Stealing for Irregular Parallel Applications on Computational Grids Vladimir Janjic University of St Andrews 12th December 2011

2. November 21, 2006 In this talk…  Feudal Stealing algorithm for scheduling irregular parallel applications  Combination of Grid-GUM and Cluster-aware Random Stealing  Irregular parallel applications -- task trees highly unbalanced

3. November 21, 2006 What is work stealing?  Work Stealing -- passive, distributed, dynamic scheduling method  Idle “thieves” steal work from busy “victims”

4. November 21, 2006 To steal or not to steal?  Why stealing?  Dynamic, adaptive and “cheap”  Does not require prior knowledge of task dependencies => good for irregular applications  Inherently distributed => scalability  Why not stealing?  Not optimal  Possibly slow work distribution

5. November 21, 2006 Work stealing on Computational Grids  Grid-GUM (GpH), Satin (Java d&c), Javelin (Java), Atlas (Java)  The main problem : Steal attempts can be expensive due to high latencies  Especially for irregular applications, where all work may be concentrated on a few nodes  The main questions are where to send steal attempts and how to respond to them  Use load information (Grid-GUM)

6. November 21, 2006 Cluster-aware Random Stealing (CRS)  Local (within a cluster) and remote (outside of cluster) stealing done in parallel  Works well for regular applications on heterogeneous environments with a lot of parallelism  Not so well for irregular

7. November 21, 2006 Centralised and distributed work stealing

8. November 21, 2006 Feudal Stealing  Use the CRS algorithm as a base  Local stealing done using Random Stealing  Remote stealing done via cluster head nodes  Only head nodes (and a victim) visited  Head nodes hold load information 321 4 7 5 89 6 Cluster 0 Local load Remote Load PE Load Cl Time Load 2 2 1 1000 23 3 3 2 0 0 4 0 3 2000 0 5 7 6 0 7 0 8 1 9 5

9. November 21, 2006 Feudal Stealing

10. November 21, 2006 How is load information in head nodes obtained?  Load of nodes inside the cluster periodically sent  Load of remote clusters updated from remote-steal messages  Cluster load information attached to remote-steal messages (similar to Grid-GUM)

11. November 21, 2006 Evaluation of Feudal Work Stealing  Using simulations, on generic benchmarks for load balancing algorithms (UTS -- Unbalanced Tree Search)  For regular and less-irregular applications, performs as well as CRS and better than Grid-GUM  For highly-irregular applications, better than CRS and Grid- GUM

12. November 21, 2006 Comparison of Feudal Stealing, CRS and Grid-GUM

13. November 21, 2006 Improvements of Feudal Stealing over CRS

14. November 21, 2006 Conclusions  Feudal Stealing works well for irregular parallel applications on Computational Grids  Sacrifices some desirable features of “pure” work stealing in order to make better selection of remote targets  Tested only using simulations. Implementation in Grid-GUM under way  Tested only on artificial applications (unbalanced tree search)

15. November 21, 2006 More info  Vladimir Janjic, Load Balancing of Irregular Parallel Applications On Heterogeneous Distributed Computing Environments, PhD Thesis, University of St Andrews, 2011  Vladimir Janjic, Kevin Hammond, Think Locally Steal Globally : Using Dynamic Load Information in Work-Stealing on Computational Grids, Submitted to CCGrid 2012  Vladimir Janjic, Kevin Hammond, Feudal Work-Stealing, In preparration, planned for submission to EuroPar 2012