Tran Nam-Luc – Stale Synchronous Parallel Iterations on Flink

Stale Synchronous Parallel Iterations on Flink

TRAN Nam-Luc / Engineer @EURA NOVA Research & Development

FLINK FORWARD 2015BERLIN, GERMANYOCTOBER 2015

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How not to synchronize workers

Worker 1

Worker 2Worker 3

Worker 4

Worker 6

Worker 5

Worker 7

Worker 8

Worker 9

Worker 10

STRAGGLER

Bulk Synchronous Parallelism synchronizes threads after each iteration.

THE BIG PICTURE

4

There are always stragglers in a cluster.

In large clusters, that causes a lot of workers waiting !

Gonna dig me a hole (gonna dig me a hole),

Gonna put a nerd in it (gonna put a nerd in it),

Gonna take a firecracker (gonna take a firecracker)…

Worker 1

Worker 2

Worker 3

CONTRIBUTION

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1. STALE SYNCHRONOUS PARALLEL ITERATIONS

Tackling the straggler problem within Flink

2. DISTRIBUTED FRANK-WOLFE ALGORITHM

Applied on LASSO regression, as use case

PART 1: STALE SYNCHRONOUS PARALLEL ITERATIONS ON FLINK

There are stragglers in distributed processing frameworks …

→ Hardware heterogeneity→ Skewed data distribution→ Garbage collection

8

THE STRAGGLER PROBLEM

Iteration time

Not predictableCostly to reschedule !

… especially in the context of data center operating systems:

Distribution of iterative-convergent algorithms:

9

BULK VS STALE SYNCHRONOUS

STALE STALE

Classic

Explicit synchronization

barrier

10

PARAMETER SERVER

STALE STALE

Explicit synchronization

barrier

How to keep workers up-to-date?

x

x

x

Parameter server

1. SSP iteration control model

2. Parameter server

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INTEGRATION WITH FLINK

What does Flink need to enable SSP?

if clocki <= cluster-wide clock + staleness

do iteration

++clocki , then send to clock

i synchronization sink

else wait until clocki <= cluster-wide clock + staleness

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ITERATION CONTROL MODEL IN FLINK

Worker pi

Clock Synchronization Sink

clocki

cluster-wide clock

store clocki in C

cluster-wide clock = min(C)

broadcast cluster-wide clock if changed


ClockEvent

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IterationHead

worker done

worker done

worker done

IterationIntermediate

IterationTail

backchannel

IterationHead


IterationTail

backchannel

IterationHead


IterationTail

backchannel

all workers done

all workers done

all workers done

IterationSynchronizationTask


ClockEvent

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IterationHead

Clock pi


IterationTail

backchannel

IterationHead


IterationTail

backchannel

IterationHead


IterationTail

backchannel

ClockSynchronizationTask

cluster-wide clock

Clock pi

Clock pi

cluster-wide clock

cluster-wide clock

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SuperstepBarrier

IterationHeadPACTTask

SyncEventHandler

IterationSynchronizationTask SSPIterationHeadPACTTask

ClockHolder

ClockSyncEventHandler

ClockSynchronizationTask

BULK SYNCHRONOUS PARALLEL

Convergence determined at synchronization barrier

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CONVERGENCE CHECK

STALE SYNCHRONOUS PARALLEL

Convergence reached when no more worker can improve the solution

dataSet.Iterate(nIterations)

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STALE SYNCHRONOUS API

dataSet.IterateWithSSP(nIterations, staleness)

Simple APIRichMapFunctionWithParameterServer extends RichMapFunction {

update(id, clock, parameter)

get(id)

}

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PARAMETER SERVER

DATA GRID

SHARED MODEL

Worker Worker Worker Worker

Architecture

PART 2: DISTRIBUTED FRANK-WOLFE ALGORITHM

Solving the current optimization problem:

Distributed version (Bellet et al. 2015):

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DISTRIBUTED FRANK-WOLFE ALGORITHM

Linear combination of atoms

sparse coefficients


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Ato

m 1

Ato

m 2

Ato

m 3

Ato

m 4

...

Ato

m n

W1 W2 W3

Linear combination of atoms

sparse coefficients


22


Ato

m 1

Ato

m 2

Ato

m 3

Ato

m 4

...

Ato

m n

W1 W2 W3


23


Ato

m 1

Ato

m 2

Ato

m 3

Ato

m 4

...

Ato

m n

W1 W2 W3

1. Local selection of atoms


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Ato

m 1

Ato

m 2

Ato

m 3

Ato

m 4

...

Ato

m n

W1 W2 W3

2. Global consensus


25


Ato

m 1

Ato

m 2

Ato

m 3

Ato

m 4

...

Ato

m n

W1 W2 W3

3. α Coefficients update

Stale synchronous version:

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Ato

m 1

Ato

m 2

Ato

m 3

Ato

m 4

...

Ato

m n

W1 W2 W3

1. Get α coefficients from parameter server

Parameter Server


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Ato

m 1

Ato

m 2

Ato

m 3

Ato

m 4

...

Ato

m n

W1 W2 W3

2. Local selection of atoms

Parameter Server


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Ato

m 1

Ato

m 2

Ato

m 3

Ato

m 4

...

Ato

m n

W1 W2 W3

3. Compute α coefficients from locally selected atoms

Parameter Server


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Ato

m 1

Ato

m 2

Ato

m 3

Ato

m 4

...

Ato

m n

W1 W2 W3

4. Update α coefficients to parameter server

Parameter Server


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Ato

m 1

Ato

m 2

Ato

m 3

Ato

m 4

...

Ato

m n

W1 W2 W3

Repeat while within staleness bounds

Parameter Server

See our full paper for

� full implementation details� properties � application to LASSO REGRESSION� convergence proof

N-L Tran, T Peel, S Skhiri, Distributed Frank-Wolfe under Pipelined Stale Synchronous Parallelism, proceedings of IEEE BigData 2015, Santa Clara, November 2015


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Application on LASSO regression

Random sparse 1.000 x 10.000 matrices

Sparsity ratio = 0,001

Generated load: at any time, 1 random node under 100% load during 12 seconds

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EXPERIMENTS

5 nodes, 2 Ghz, 3Gb RAM

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RESULTS

Convergence of the objective function

Stragglers in a cluster are an issue.

Mitigate them with Stale Synchronous Parallel Iterations.

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RECAP

Pull request #967

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WANNA TRY IT OUT?

Stale Synchronous Parallel iterations + API

Pull request #1101

Frank-Wolfe algorithm + LASSO regression

THANK YOU!Do you have any questions?

[email protected]

AGENDA

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1. STALE SYNCHRONOUS PARALLEL ITERATIONS∙ The straggler problem∙ BSP vs SSP∙ Integration with Flink∙ Iteration control model∙ API

2. DISTRIBUTED FRANK-WOLFE ALGORITHM∙ Problem statement∙ Application: LASSO regression∙ Experiments

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RESULTS

Sparsity of the coefficients

The parameter server keeps track of the intermediate results

→ Key-object store→ Distributed, with local caching

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PARAMETER SERVER

Tran Nam-Luc – Stale Synchronous Parallel Iterations on Flink

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