Improved Mesh Partitioning For Parallel Substructure Finite Element Computations Shang-Hsien Hsieh, Yuan-Sen Yang and Po-Liang Tsai Department of Civil

Improved Mesh PartitioningImproved Mesh Partitioning F Foror Parallel S Parallel Substructure Finite Element Computationsubstructure Finite Element Computations

Shang-Hsien Hsieh, Yuan-Sen Yang and Po-Liang TsaiShang-Hsien Hsieh, Yuan-Sen Yang and Po-Liang Tsai

Department of Civil EngineeringDepartment of Civil Engineering

National Taiwan UniversityNational Taiwan University

Taipei, Taiwan, R.O.C.Taipei, Taiwan, R.O.C.

Sponsored by the National Science Council of R.O.C.Sponsored by the National Science Council of R.O.C.

ObjectiveObjective

To improve the efficiency of the parallel substructure To improve the efficiency of the parallel substructure

finite element method through investigation on mesh finite element method through investigation on mesh

partitioning.partitioning.

Parallel Substructure MethodParallel Substructure Method

((a) Mesh partitioning (preprocessed by a single processor)a) Mesh partitioning (preprocessed by a single processor) (b) Concurrent substructure condensation(b) Concurrent substructure condensation (c) Solution of condensed system equations associated with the (c) Solution of condensed system equations associated with the

interface d.o.f.’s using a single processor interface d.o.f.’s using a single processor (d) Concurrent solution of the substructure internal d.o.f.’s (d) Concurrent solution of the substructure internal d.o.f.’s

((a)a) ((b)b) ((c)c) ((d)d)

Parallel Substructure Method Parallel Substructure Method (Cont’d)(Cont’d)

• Major difficultyMajor difficulty– Workloads are not well balanced.Workloads are not well balanced.

• ReasonReason– Insufficient mesh partitioning criteriaInsufficient mesh partitioning criteria

0 50 100 150 200 250 300

4

3

2

1Pr

oc ID

Time (sec.)

Read

Stiff

Condn

Data

Wait

Com+asm

Solv

UpdatBLD30-145,480 BC elms 152,400 D.O.F.‘s

Common criteria used by most of mesh Common criteria used by most of mesh partitioning algorithms:partitioning algorithms:

– Balance of number of elements among Balance of number of elements among substructuressubstructures

– Minimization of total number of interface nodesMinimization of total number of interface nodes

Mesh PartitioningMesh Partitioning

Mesh Partitioning Mesh Partitioning (Cont’d)(Cont’d)

New criteriaNew criteria

– Balance of the total element weights among Balance of the total element weights among substructuressubstructures

– Minimization of number of interface nodesMinimization of number of interface nodes

An iterative approachAn iterative approach– Mesh partitioning kernel – Mesh partitioning kernel –

METIS METIS (Karypis and Kumar, 1995)(Karypis and Kumar, 1995)

– Evaluation of performance Evaluation of performance indicatorsindicators

– Adjustment of element Adjustment of element weights based on the weights based on the number of substructure number of substructure interface nodesinterface nodes

Improved Mesh PartitioningImproved Mesh Partitioning

M E T I S

P o te n t ia l fo rim p r o v e m e n t

A d ju s t in gE le m e n tw e ig h ts

N o

Y e s

E v a lu a t in gp e r fo r m a n c e

in d ic a to r s

F in ite e le m e n tm e s h

P a r t it io n e d m e s h

Improved Mesh Partitioning Improved Mesh Partitioning (Cont’d)(Cont’d)

Tuning factor F of iteration Tuning factor F of iteration i i ::

NNiiIN, IN, jj

Min(NNiiIN, IN, jj , for each substructure j)

Fii jj =

88/13/13

Fi i 11 = 6/6=1.0

Fii 3 3 = 7/6=1.17

Fii 2 2 = 6/6=1.0


Indicator E:Indicator E:

– Indicator of efficiency of iteration Indicator of efficiency of iteration ii

– EEii = max(E = max(Eii1,1,

jj for each substructure for each substructure jj) + E) + Eii

22

– EEii1, 1, jj : condensation time indicator of substructure : condensation time indicator of substructure jj

– EEii1, 1, jj = [(I = [(Iii

1, 1, jj )) 2.52.5+(I+(Iii

2, 2, j j )) 2.52.5 ] / [(I ] / [(I00

1, 1, jj )) 2.52.5+(I+(I00

2, 2, j j )) 2.52.5 ] ]

– IIii1, 1, jj : N: Nii

ELM, ELM, jj / N/ NELMELM

– IIii2, 2, jj : N: Nii

IN, IN, jj / N / N00IN, IN, jj

– Interface solution time factor - EInterface solution time factor - E2,2,ii ::

– EEii22 = (N = (Nii

IN IN / N/ N00IN IN ))

33


Indicator E vs. Total elapsed time TIndicator E vs. Total elapsed time T

Model: 4E123072solid(B20) elements 48,975 D.O.F.‘s

(Tsai, 1999)

0

0.2

0.4

0.6

0.8

1

1.2

0 1 2 3 4 5 6

E

TN

orm

aliz

ed E

or

T

Iteration i

CPU: Intel Pentium II-CPU: Intel Pentium II-350350Memory: NEC Memory: NEC 128MB128MB PC100 SDRAM PC100 SDRAMNetwork: ACCTON 10/Network: ACCTON 10/100 Mbps100 Mbps

D-Link D-Link 100100 Mbps Hub Mbps HubOS: OS: LinuxLinux Redhat 5.2 Redhat 5.2

CPU: Intel Pentium II-CPU: Intel Pentium II-350350Memory: NEC Memory: NEC 128MB128MB PC100 SDRAM PC100 SDRAMNetwork: ACCTON 10/Network: ACCTON 10/100 Mbps100 Mbps

D-Link D-Link 100100 Mbps Hub Mbps HubOS: OS: LinuxLinux Redhat 5.2 Redhat 5.2

PC Cluster

Computing EnvironmentComputing Environment

Numerical ExperimentsNumerical Experiments

BLADE944 solid(B20) elements 18,180 D.O.F.‘s

i Max/Min ( Ni

IN, j ) Ni

IN Ei

0 1.82 580 2.0 1 1.51 506 1.54 2 1.95 498 1.49 3 1.59 556 1.84

Improved mesh partitioning iterationsImproved mesh partitioning iterations

(Wawrzynek, 1991)

Nsub ( number of substructures) = 4

CPU time: 1.6 sec.

•METIS without iteration

•Improved mesh partitioning (with 2 iterations)

BLADE944 solid(B20) elements 18,180 D.O.F.‘s

0 20 40 60 80

1

2

3

4 Read

Stiff

Condn

Data

Wait

Com+asm

Solv

Updat

Np = 4Hardware: PC cluster ( P II 350)OS : Linux Redhat 5.2

0 20 40 60 80

1

2

3

4ReadStiffCondnDataWaitCom+asmSolvUpdat

Numerical Experiments Numerical Experiments (Cont’d)(Cont’d)

67.4 sec.

45.4 sec.

Additional 1.6 sec. for iterative mesh partitioning


i Max/Min ( Ni

IN, j ) Ni

IN Ei

0 1.33 321 2.0 1 1.35 279 1.52 2 1.33 307 1.83

Improved mesh partitioning iterationsImproved mesh partitioning iterations

ESTORY3012,750 BC elements 28,080 D.O.F.‘s

CPU time: 3.6 sec.

Nsub ( number of substructures) = 4

•METIS without iteration

•Improved mesh partitioning (with 1 iteration)ESTORY3012,750 BC elements 28,080 D.O.F.‘s

Np = 4Hardware: PC cluster ( P II 350)OS : Linux Redhat 5.2

0 20 40 60 80

1

2

3

4

Proc

Sec

Read

Stiff

Condn

Data

Wait

Com+asm

Solv

Updat


89.2 sec.

56.5 sec.

Additional 3.6 sec. for iterative mesh partitioning

0 20 40 60 80

1

2

3

4

Proc

Sec

Read

Stiff

Condn

Data

Wait

Com+asm

Solv

Updat64.5 sec.

ConclusionsConclusions

The iterative mesh partitioning approach can The iterative mesh partitioning approach can effectively improve the efficiency of parallel effectively improve the efficiency of parallel substructure finite element computations.substructure finite element computations.

Better mesh partitioning is still needed.Better mesh partitioning is still needed. A parallel equation solver becomes more A parallel equation solver becomes more

important.important.

Documents

Improved Mesh Partitioning For Parallel Substructure Finite Element Computations Shang-Hsien Hsieh, Yuan-Sen Yang and Po-Liang Tsai Department of Civil