Carnegie Mellon

GraphLabA New Framework for

Parallel Machine Learning

Yucheng LowAapo Kyrola Carlos Guestrin

Joseph GonzalezDanny BicksonJoe Hellerstein

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Exponential Parallelism

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Sequential P

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Constant SequentialPerformance

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Exponentially Increasing Parallel Performance

Release Date

13 Million Wikipedia Pages3.6 Billion photos on Flickr

Parallel Programming is Hard

Designing efficient Parallel Algorithms is hard

Race conditions and deadlocksParallel memory bottlenecksArchitecture specific concurrencyDifficult to debug

ML experts repeatedly address the same parallel design challenges

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Avoid these problems by using high-level abstractions.

Graduate students

CPU 1 CPU 2 CPU 3 CPU 4

MapReduce – Map Phase

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Embarrassingly Parallel independent computation

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No Communication needed

CPU 1 CPU 2 CPU 3 CPU 4

MapReduce – Map Phase

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Embarrassingly Parallel independent computation

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21.3

25.8

24.1

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No Communication needed

CPU 1 CPU 2 CPU 3 CPU 4

MapReduce – Map Phase

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Embarrassingly Parallel independent computation

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42.3

21.3

25.8

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67.5

14.9

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24.1

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No Communication needed

CPU 1 CPU 2

MapReduce – Reduce Phase

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12.9

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Fold/Aggregation

Related Data

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Interdependent Computation: Not MapReduceable

Parallel Computing and ML

Not all algorithms are efficiently data parallel

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Data-Parallel Complex Parallel Structure

CrossValidation

Feature Extraction

BeliefPropagation

SVM

KernelMethods

Deep BeliefNetworks

NeuralNetworks

Tensor Factorization

Sampling

Lasso

Common Properties

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1) Sparse Data Dependencies

2) Local Computations

3) Iterative Updates

• Sparse Primal SVM• Tensor/Matrix Factorization

• Expectation Maximization• Optimization

• Sampling• Belief Propagation

Operation A

Operation B

Gibbs Sampling

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X4 X5 X6

X9X8

X3X2X1

X7

1) Sparse Data Dependencies

2) Local Computations

3) Iterative Updates

GraphLab is the SolutionDesigned specifically for ML needs

Express data dependenciesIterative

Simplifies the design of parallel programs:

Abstract away hardware issuesAutomatic data synchronizationAddresses multiple hardware architectures

Implementation here is multi-coreDistributed implementation in progress

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Carnegie Mellon

GraphLab

A New Framework for Parallel Machine

Learning

GraphLab

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Data Graph Shared Data Table

Scheduling

Update Functions and Scopes

GraphLabModel

Data Graph

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A Graph with data associated with every vertex and edge.

:Data

x3: Sample valueC(X3): sample counts

Φ(X6,X9): Binary potential

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X2

X3

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Update Functions

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Update Functions are operations which are applied on a vertex and transform the data in the scope of the vertex

Gibbs Update: - Read samples on adjacent vertices - Read edge potentials - Compute a new sample for the current vertex

Update Function Schedule

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e f g

kjih

dcbaCPU 1

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Update Function Schedule

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e f g

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dcbaCPU 1

CPU 2

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Static ScheduleScheduler determines the

order of Update Function Evaluations

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Synchronous Schedule: Every vertex updated simultaneously

Round Robin Schedule: Every vertex updated sequentially

Converged Slowly ConvergingFocus Effort

Need for Dynamic Scheduling

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Dynamic Schedule

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e f g

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dcbaCPU 1

CPU 2

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Dynamic ScheduleUpdate Functions can insert new tasks into

the schedule

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FIFO Queue Wildfire BP [Selvatici et al.]

Priority Queue Residual BP [Elidan et al.]

Splash Schedule Splash BP [Gonzalez et al.]

Obtain different algorithms simply by changing a flag!

--scheduler=fifo --scheduler=priority --scheduler=splash

Global Information

What if we need global information?

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Sum of all the vertices?

Algorithm Parameters?

Sufficient Statistics?

Shared Data Table (SDT)Global constant parameters

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Constant:Total # Samples

Constant: Temperature

Accumulate Function:

Sync OperationSync is a fold/reduce operation over the graph

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Sync!

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Apply Function:

AddDivide by |

V|9222

Accumulate performs an aggregation over verticesApply makes a final modification to the accumulated dataExample: Compute the average of all the vertices

Shared Data Table (SDT)Global constant parametersGlobal computation (Sync Operation)

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Constant:Total # Samples

Sync: SampleStatistics

Sync: LoglikelihoodConstant: Temperature

Carnegie Mellon

Safetyand

Consistency

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Write-Write Race

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Write-Write Race If adjacent update functions write simultaneously

Left update writes: Right update writes:Final Value

Race Conditions + Deadlocks

Just one of the many possible racesRace-free code is extremely difficult to write

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GraphLab design ensures race-free operation

Scope Rules

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Guaranteed safety for all update functions

Full Consistency

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Only allow update functions two vertices apart to be run in parallelReduced opportunities for parallelism

Obtaining More Parallelism

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Not all update functions will modify the entire scope!

Belief Propagation: Only uses edge dataGibbs Sampling: Only needs to read adjacent vertices

Edge Consistency

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Obtaining More Parallelism

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“Map” operations. Feature extraction on vertex data

Vertex Consistency

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Sequential ConsistencyGraphLab guarantees sequential

consistency

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For every parallel execution, there exists a sequential execution of update functions which will produce the same result.

CPU 1

CPU 2

CPU 1

Parallel

Sequential

time

GraphLab

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GraphLabModel

Data Graph Shared Data Table

Scheduling

Update Functions and

Scopes

Carnegie Mellon

Experiments

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ExperimentsShared Memory Implemention in C++ using PthreadsTested on a 16 processor machine

4x Quad Core AMD Opteron 838464 GB RAM

Belief Propagation +Parameter Learning

Gibbs SamplingCoEMLasso

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Compressed SensingSVMPageRankTensor Factorization

Graphical Model Learning

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3D retinal image denoising

Data Graph: 256x64x64 vertices

Update Function

Belief PropagationSync

Acc: Compute inference statisticsApply:Take a gradient step

Sync: Edge-potential

Graphical Model Learning

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Approx. Priority Schedule

Splash Schedule

15.5x speedup on 16 cpus

Graphical Model Learning

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Inference

Gradient Step

Standard parameter learning takes gradient only after inference is compute

Parallel Inference + Gradient Step

With GraphLab:Take gradient step while inference is running

Ru

nti

me

3x faster!

2100 sec

700 sec

Iterated Simultaneous

Gibbs SamplingTwo methods for sequentially consistency:

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ScopesEdge Scope

graphlab(gibbs, edge, sweep);

SchedulingGraph Coloring

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graphlab(gibbs, vertex, colored);

Gibbs SamplingProtein-protein interaction networks [Elidan et al. 2006]

Pair-wise MRF14K Vertices100K Edges

10x SpeedupScheduling reduceslocking overhead

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Round robin schedule

Colored Schedule

CoEM (Rosie Jones, 2005)Named Entity Recognition Task

Vertices

Edges

Small 0.2M 20M

Large 2M 200M

the dog

Australia

Catalina Island

<X> ran quickly

travelled to <X>

<X> is pleasant

Hadoop 95 Cores 7.5 hrs

Is “Dog” an animal?Is “Catalina” a place?

CoEM (Rosie Jones, 2005)

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GraphLab 16 Cores 30 min

15x Faster!6x fewer CPUs!

Hadoop 95 Cores 7.5 hrs

Lasso

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L1 regularized Linear Regression

Shooting Algorithm (Coordinate Descent)Due to the properties of the update, full consistency is needed

Lasso

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L1 regularized Linear Regression

Shooting Algorithm (Coordinate Descent)Due to the properties of the update, full consistency is needed

Lasso

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L1 regularized Linear Regression

Shooting Algorithm (Coordinate Descent)Due to the properties of the update, full consistency is needed

Finance Dataset from Kogan et al [2009].

Full Consistency

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Optimal

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Dense

Sparse

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Relaxing Consistency

51Why does this work? (Open Question)

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GraphLabAn abstraction tailored to Machine Learning Provides a parallel framework which compactly expresses

Data/computational dependenciesIterative computation

Achieves state-of-the-art parallel performance on a variety of problemsEasy to use

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Future WorkDistributed GraphLab

Load BalancingMinimize CommunicationLatency HidingDistributed Data ConsistencyDistributed Scalability

GPU GraphLabMemory bus bottle neckWarp alignment

State-of-the-art performance for <Your Algorithm Here> .

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Carnegie Mellon

Parallel GraphLab 1.0

Available Today

http://graphlab.ml.cmu.edu

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Documentation… Code… Tutorials…