http://www.logos.ic.i.u-tokyo.ac.jp/~kay/papers/ccgrid2008_stable_broadcast.pdf

A Stable Broadcast Algorithm Kei Takahashi Hideo Saito Takeshi Shibata Kenjiro Taura (The University of Tokyo, Japan) CCGrid 2008 - Lyon, France

[object Object],[object Object],[object Object],Broadcasting Large Messages Data Data Data Data Data < A Stable Broadcast Algorithm > Kei Takahashi, Hideo Saito, Takeshi Shibata and Kenjiro Taura

[object Object],Problem of Broadcast S D S D 100 25 25 25 25 < A Stable Broadcast Algorithm > Kei Takahashi, Hideo Saito, Takeshi Shibata and Kenjiro Taura

[object Object],[object Object],Problem of Slow Nodes    10 10 100  100 100 10 10

[object Object],[object Object],[object Object],[object Object],Contributions

[object Object],[object Object],[object Object],[object Object],Contributions (cont.)

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Agenda < A Stable Broadcast Algorithm > Kei Takahashi, Hideo Saito, Takeshi Shibata and Kenjiro Taura

[object Object],[object Object],Problem Settings < A Stable Broadcast Algorithm > Kei Takahashi, Hideo Saito, Takeshi Shibata and Kenjiro Taura

[object Object],[object Object],[object Object],[object Object],Problem Settings (cont.) (Message Size) (Bandwidth) 50msec 1Gbps 1GB 99%

[object Object],[object Object],[object Object],[object Object],Problem Settings (cont.) 100 10 80 40 30

[object Object],[object Object],[object Object],[object Object],Evaluation of Broadcast

[object Object],[object Object],Definition of Stable Broadcast D0 D1 D2 D3 100 10 120 100 D2 120 Single Transfer Broadcast

[object Object],[object Object],Properties of Stable broadcast

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Single-Tree Algorithms Flat Tree Random Pipeline Dijkstra Depth-First (FPFR) < A Stable Broadcast Algorithm > Kei Takahashi, Hideo Saito, Takeshi Shibata and Kenjiro Taura

[object Object],[object Object],[object Object],[object Object],FPFR Algorithm [†] [†] Izmailov et al. Fast Parallel File Replication in Data Grid. (GGF-10, March 2004.)

[object Object],[object Object],[object Object],[object Object],Tree constructions in FPFR Bottleneck First Tree (T1) V1 V2 Second Tree (T2)

[object Object],[object Object],Data transfer with FPFR T1: Sends the former part T2: sends the latter part [†] den Burger et al. Balanced Multicasting: High-throughput Communication for Grid Applications (SC ‘2005) V1 V2

[object Object],[object Object],Problems of FPFR Bottleneck    

[object Object],[object Object],[object Object],Our Algorithm V V < A Stable Broadcast Algorithm > Kei Takahashi, Hideo Saito, Takeshi Shibata and Kenjiro Taura

[object Object],[object Object],[object Object],[object Object],Tree Constructions T3: Third Tree (Partial Tree) S A B C T1: First Tree (Spanning) S A B C T2: Second Tree (Partial Tree) S A B C V1 V2 V3 Throughput of T1

[object Object],[object Object],[object Object],[object Object],[object Object],Data Transfer A B S C T1 T2 T3 (V1) (V2) (V3) A B S C T1 T2 A B S C T1

[object Object],[object Object],[object Object],[object Object],[object Object],Properties of Our Algorithm

[object Object],[object Object],[object Object],[object Object],[object Object],(1) Simulations < A Stable Broadcast Algorithm > Kei Takahashi, Hideo Saito, Takeshi Shibata and Kenjiro Taura … … … … 110 nodes 81 nodes 36 nodes 4 nodes

Compared Algorithms Flat Tree Random Dijkstra Depth-First (FPFR) … and OURS

[object Object],[object Object],[object Object],Result of Simulations 100 100 100 1000 1000 1000

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Result of Simulations (cont.)

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],(2) Real Machine Experiment

Theoretical Maximum Aggregate Bandwidth ,[object Object],[object Object],D0 D1 D2 D3 100 10 120 100

[object Object],[object Object],[object Object],Evaluation of Aggregate Bandwidth 700 700 900

[object Object],[object Object],[object Object],Evaluation of Stability Slow

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Conclusion < A Stable Broadcast Algorithm > Kei Takahashi, Hideo Saito, Takeshi Shibata and Kenjiro Taura

[object Object],[object Object],Future Work < A Stable Broadcast Algorithm > Kei Takahashi, Hideo Saito, Takeshi Shibata and Kenjiro Taura

[object Object],[object Object],Future work

All the graphs 0 10 20 30 40 50 60 70 80 10 50 100 R e l a t i v e P e r f o r m a n c e Number of Destinations (a) [Low Bandwidth Variance] (Symmetric) Ours Depthfirst Dijkstra Random (best) Random (avg) 0 5 10 15 20 25 30 35 40 10 50 100 R e l a t i v e P e r f o r m a n c e Number of Destinations (g) [Random Bandwidth among Clusters ] (Symmetric) Ours Depthfirst Dijkstra Random (best) Random (avg) 0 5 10 15 20 25 30 35 10 50 100 R e l a t i v e P e r f o r a m a n c e Number of Destinations Ours Depthfirst Dijkstra Random (best) Random (avg) (h) [Random Bandwidth among Clusters ] (Asymmetric) 0 10 20 30 40 50 60 70 10 50 100 R e l a t i v e P e r f o r m a n c e Number of Destinations (b) [Low Bandwidth Variance] (Asymmetric) Ours Depthfirst Dijkstra Random (best) Random (avg) 0 5 10 15 20 25 30 35 40 45 10 50 100 R e l a t i v e P e r f o r m a n c e Number of Destinations (c) [High Bandwidth Variance] (Symmetric) Ours Depthfirst Dijkstra Random (best) Random (avg) 0 5 10 15 20 25 10 50 100 R e l a t i v e P e r f o r m a n c e Number of Destinations (d) [High Bandwidth Variance] (Asymmetric) Ours Depthfirst Dijkstra Random (best) Random (avg) 0 5 10 15 20 25 30 35 40 45 10 50 100 R e l a t i v e P e r f o r m a n c e Number of Destinations (e) [Mixed Fast and Slow links ] (Symmetric) Ours Depthfirst Dijkstra Random (best) Random (avg) 0 2 4 6 8 10 12 14 16 18 20 10 50 100 R e l a t i v e P e r f o r m a n c e Number of Destinations (f) [Mixed Fast and Slow links ] (Asymmetric) Ours Depthfirst Dijkstra Random (best) Random (avg) 0 2 4 6 8 10 12 (1,10) (2,20) (3,30) (4,40) (5,50) R e l a t i v e P e r f o r m a m Ours Depthfirst Dijkstra (# of srcs, # of dests) (i) [Mulrtiple Souces] (Low Bandwidth Variance, Symmetric) Random (best) Random (avg)

[object Object],[object Object],Broadcast with BitTorrent [†] [†] Wei et al. Scheduling Independent Tasks Sharing Large Data Distributed with BitTorrent. (In GRID ’05) Transfer tree snapshot Bottleneck Link

[object Object],[object Object],[object Object],Simulation 1 1000 1000 100~1000 100~1000

[object Object],[object Object],Topology-unaware pipeline Bottleneck

[object Object],[object Object],[object Object],[object Object],Depth-first Pipeline Slow Node [†] Shirai et al. A Fast Topology Inference - A building block for network-aware parallel computing. (HPDC 2007)

[object Object],[object Object],[object Object],[object Object],Dijkstra Algorithm [†] [†] Wang et al. A novel data grid coherence protocol using pipeline-based aggressive copy method. (GPC, pages 484–495, 2007) Bottleneck Link

http://www.logos.ic.i.u-tokyo.ac.jp/~kay/papers/ccgrid2008_stable_broadcast.pdf

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http://www.logos.ic.i.u-tokyo.ac.jp/~kay/papers/ccgrid2008_stable_broadcast.pdf