high quality graph partitioning - peter sanders, christian schulz

1 Peter Sanders, Christian Schulz:High Quality Graph Partitioning

Department of InformaticsInstitute for Theoretical Computer Science, Algorithmics II

High Quality Graph PartitioningPeter Sanders, Christian Schulz

KIT – Universitat des Landes Baden-Wurttemberg undnationales Großforschungszentrum in der Helmholtz-Gemeinschaft www.kit.edu

Overview

IntroductionMultilevel AlgorithmsAdvanced TechniquesEvolutionary TechniquesExperimentsSummary

ε-Balanced Graph Partitioning

Partition graph G = (V ,E , c : V → R>0,ω : E → R>0)into k disjoint blocks s.t.

total node weight of each block ≤ 1 + ε

ktotal node weight

total weight of cut edges as small as possible

Applications:linear equation systems, VLSI design, route planning, . . .

Multi-Level Graph Partitioning

Successful in existing systems:Metis, Scotch, Jostle,. . . , KaPPa, KaSPar, KaFFPa, KaFFPaE

Advanced TechniquesTalk Today

Edge RatingsHigh Quality MatchingsFlow Based RefinementsMore Localized Local SearchF-cycles for Graph Partitioning

Graph PartitioningMatching Selection

Goals:1. large edge weights sparsify2. large #edges few levels3. uniform node weights “represent” input4. small node degrees “represent” input unclear objective gap to approx. weighted matchingwhich only considers 1.,2.

Our Solution:Apply approx. weighted matching to general edge rating function

input graph

contract

Graph PartitioningEdge Ratings

ω({u, v})

expansion({u, v}) :=ω({u, v})

c(u) + c(v)

expansion∗({u, v}) :=ω({u, v})c(u)c(v)

expansion∗2({u, v}) :=ω({u, v})2

c(u)c(v)

innerOuter({u, v}) :=ω({u, v})

Out(v) + Out(u)− 2ω(u, v)

where c = node weight, ω =edge weight,Out(u) := ∑{u,v}∈E ω({u, v})

Flows as Local ImprovementTwo Blocks

∂1B ∂2B

area B, such that each (s, t)-min cut is ε-balanced cut in Ge.g. 2 times BFS (left, right)

stop the BFS, if size would exceed (1 + ε) c(V )2 − c(V2)

⇒ c(V2new) ≤ c(V2) + (1 + ε) c(V )2 − c(V2)

Flows as Local ImprovementTwo Blocks

obtain optimal cut in Bsince each cut in B yields a feasible partition→ improved two-partitionadvanced techniques possible and necessary

Example100x100 Grid

ExampleConstructed Flow Problem (using BFS)

ExampleApply Max-Flow Min-Cut

ExampleOutput Improved Partition

Local Improvement for k -partitionsUsing Flows?

on each pair of blocks

input graph

initial

outputpartition

local improvement

partitioning

contract uncontract

More Localized Local Search

Idea: KaPPa, KaSPar ⇒ more local searches are betterTypical: k -way local search initialized with complete boundaryLocalization:

1. complete boundary⇒ maintained todo list T2. initialize search with single node v ∈rnd T3. iterate until T = ∅

each node moved at most once

DistributedEvolutionary Graph Partitioning

Evolutionary Algorithms:highly inspired by biologypopulation of individualsselection, mutation, recombination, ...

Goal: Integrate KaFFPa in an Evolutionary StrategyEvolutionary Graph Partitioning:

individuals ↔ partitionsfitness ↔ edge cut

Parallelization→ quality records in a few minutes for small graphs

Combine

contract

two individuals P1, P2:don’t contract cut edges of P1 or P2

until no matchable edge is leftcoarsest graph↔ Q-graph of overlay→ exchanging good parts is easyinital solution: use better of both parents

ExampleTwo Individuals P1, P2

ExampleOverlay of P1, P2

ExampleMultilevel Combine of P1, P2

contract

Exchanging good parts is easyCoarsest Level

v4 >> <

>> large weight, < small weightstart with the better partition (red, P2)move v4 to the opposite blockintegrated into multilevel scheme (+local search on each level)

ExampleResult of P1, P2

Parallelization

each PE has its own island (a local population)locally: perform combine and mutation operationscommunicate analog to randomized rumor spreading

1. rumor↔ currently best local partition2. local best partition changed → send it to O(log P) random PEs3. asynchronous communication (MPI Isend)

Experimental ResultsComparison with Other Systems

Geometric mean, imbalance ε = 0.03:11 graphs (78K–18M nodes) ×k ∈ {2,4,8,16,64}

Algorithm large graphsBest Avg. t[s]

KaFFPa strong 12 053 12 182 121.22KaSPar strong 12 450 +3% 87.12KaFFPa eco 12 763 +6% 3.82Scotch 14 218 +20% 3.55KaFFa fast 15 124 +24% 0.98kMetis 15 167 +33% 0.83

Repeating Scotch as long as KaSPar strong run and choosing thebest result 12.1% larger cutsWalshaw instances, road networks, Florida Sparse MatrixCollection, random Delaunay triangulations, random geometricgraphs

QualityEvolutionary Graph Partitioning

blocks k KaFFPaEimprovement overreps. of KaFFPa

2 0.2%4 1.0%8 1.5%

16 2.7%32 3.4%64 3.3%

128 3.9%256 3.7%

overall 2.5%

2h time, 32 cores per graph and k , geom. mean

Quality

1 5 20 100 500

normalized time tn

ut Repetitions

KaFFPaE

Walshaw Benchmark

runtime is not an issue614 instances (ε ∈ {1%,3%,5%})focus on partition quality

Algorithm < ≤KaPPa 131 189KaSPar 155 238KaFFPa 317 435KaFFPaE 300 470

overall quality records ≤:ε ≤1% 78%3% 92%5% 94%

Summary

input graph

OutputPartition

contract

... ...

distr.evol. Alg.

partitioning

initial

local improvement

uncontract

Cycles a la multigridW−F−V−

[ALENEX12]

flows etc.

MultilevelGraph Partitioning

Outlook

Further Material in the Paper(s)F-cycles, High Quality Matchings, ....Different combine and mutation operatorsSpecialization to road networks (Buffoon)Many more details and experiments ...

Future Workother objective functions

currently via selection criterionconnectivity? f (P) := f (P) + χ{P not connected} · |E |

integrate other partitionersgraph clusteringopen source release

Thank you!

Contact: christian.schulz@kit.edusanders@kit.edu

high quality graph partitioning - peter sanders, christian schulz

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