presentation of pam-crash v2004 part 3: efficiency · (nint=0) shell element hourglass prevention:...

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1 Presentation Presentation of PAM of PAM - - CRASH v2004 CRASH v2004 Part 3: Part 3: Efficiency Efficiency . .

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PresentationPresentation of PAMof PAM--CRASH v2004 CRASH v2004

Part 3: Part 3: EfficiencyEfficiency..

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OverviewOverview

Needed ComponentsHardwarePAM-CRASH SolverDatasets

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Needed ComponentsNeeded Components

The following components are needed to get:High throughput of simulation runsShort turn around time for a single simulation

1. Powerful Hardware2. Fast and Scalable PAM-CRASH3. Well defined input dataset

Conclusion:This requires a good collaboration between Hardware vendors, Software vendors and Software users

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OverviewOverview

Needed ComponentsHardwarePAM-CRASH SolverDatasets

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HardwareHardwareMainly the following type of High Performance computers used to run PAM-CRASH

Fat SMP systemExamples: HP Superdome, IBM Regatta, SGI ALTIX …Pro: very scalable, 64bit PAM-CRASH, general purpose computerContra: very expensive

Cluster with high performance interconnectExamples: IA32, IA64, Compaq, Opteron with Myrinet, Infiniband …Pro: very scalableContra: 64bit PAM-CRASH may not be available, maybe special MPI needed

Cluster with Gbit Ethernet interconnectExamples: IA32, Opteron with Gbit Ethernet …Pro: cheap, good scalability for Crash, robust MPIContra: small complex datasets may not scale, 64bit PAM-CRASH not available

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OverviewOverview

Needed ComponentsHardwarePAM-CRASH SolverDatasets

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PAMPAM--CRASH scalabilityCRASH scalability

PAM-CRASH shows excellent scalability for many case

Example A:ESI Neon Model (1.1e6 elements, 120msec)DMP scalability taken into account during model setup

NODE Network 32-way 64-way 128-way 256-way

Xeon(3.06/533) gigaE

myrinet 14,010s 7,647s 3,978s

Opteron(2.0/2w) myrinet 13,540s 6,961s

federation

15,030s 9,264s 5,604s

p655(1.7/8w) 17,180s 4,769s 2,999s

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PAMPAM--CRASH scalabilityCRASH scalability

Example B:Customer model (0.9e6 elements, 70msec)DMP scalability NOT taken into account during model setup (i.e. many CNTAC definitions)

NODE Network 8-way 16-way 32-way 64-way

gigaE 26,530s

5,826smyrinet 26,500s 14,880s 9,779s 4,335s

128-way

Xeon(3.06/533) 31,300s 21,620s 27,610s

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PAMPAM--CRASH scalabilityCRASH scalability

Conclusion:Users dataset is very important to achieve good scalabilityPowerful hardware can sometimes hide dataset deficiencies.ESI Recommendation:

Think DMP!Build up your model to use full power of the

Hardware and PAM-CRASH

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OverviewOverview

Needed ComponentsHardwarePAM-CRASH SolverDatasets

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DatasetsDatasets

Dataset has to fit the following requirements:

Avoid expensive Options: to be able to use the software and hardware potentialStable simulation: to avoid repeated simulation runs due to error terminationError free definitions: to avoid repeated runs due to wrong results

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Scalable DatasetScalable DatasetDomain Decomposition:

Use DCOMP VCB. It will generate less communication and fits well to contact algorithm.

CNTAC definitions:Each CNTAC definition may cause several additional communications and load unbalance!Very often this is the main reason for poor scalability

minimize communications+

minimize load unbalance =

minimize number of CNTAC definitions

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Scalable DatasetScalable DatasetCNTAC definitions:

Try to use as less CNTAC definitions as possible. For CRASH applications use ONE type 36 only.Use default NACC. Global search is expensiveDon't use different NACC for different CNTACs. This will introduce severe Load unbalance.Use CNTAC for road and rigid test device.Don’t use single large segments. Do meshing even for Road and device. Solver must be able to split the mesh.Don’t use kinematic iterations for type 33, 34, 36. This is an expensive operation which is done on 1 processor only.Try to avoid large contact thickness. Algorithm is tuned for T_cont = 0.1*ELE_length.If large contact thickness is needed impose contact search extension zone THKEXT = 0.1*ELE_length

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Scalable DatasetScalable Dataset

Spot-weld modelingUse PLINK instead of RBODYPlink will be distributed and scale very wellRBODY may need additional communication

Rigid bodiesTry to reduce number of RBODY definitionRBODY may need additional communication

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Scalable DatasetScalable Dataset

Rigid WallDon’t use RWALLData could not be distributed wellAlready in seq. CNTAC is much faster

Kinematic JointsMinimize number of KJOINTsVery often KJOINTs could be replaced by BEAM with end release

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Avoid expensive OptionsAvoid expensive Options

Options mainly for debugging (should be avoided for optimization runs):

PIPEDEBUGENERGY_MONITORING

Expensive options (avoid if possible):SRI solidsCNTAC type: 21, 37, 44, 46, 54

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Stable DatasetsStable Datasets

Very often a lot of CPU time is wasted due to not well defined models.Spend time to get a stable and numerical robust dataset.The Following guidelines are a brief overview only.

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Stable DatasetsStable DatasetsOptional keywords

Perform all data check options, which is the default: SHELLCHECK, SOLIDCHECK, DATACHECK

Time Step Control Parameters (TCTRL)Always verify the time step value, which has to be about 1.0 microsecond for a standard crash applicationSHELL_TIMESTEP: SMALL and BEND are recommendedUse INIT_MASS_SCALE to control the initial time step Use STIFFNESS_SCALE: set to 90% of the initial mass scalingUse DYNA_MASS_SCALE: set to 90% of the initial mass scalingUse the nodal time step when the non linear penalty stiffness of the contact is activated

Element Control Parameters (ECTRL)SREAIN RATE: YES RATEFILTER: NCFYL=50

Contact Control Parameters (CCTRL)Keep all default parameters

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Stable DatasetsStable DatasetsMaterial Section

Mass density for null material must have a physical meaning Shell element formulation: use the default (NINT=0)Solid element integration rule: 1 integration point (NINT=0)Shell element hourglass prevention: stiffness method using plastic modulus (ISHG=1, except for cases with large global rotation for which it is recommended to use a method with improved orthogonally to rigid body motion (ISHG=4)Solid element hourglass prevention: stiffness method with reduced hourglass coefficient (Q3= 0.01). Do not use SRIInternal damping ratio: 0.1 for all element types: shells, beams and solids (from version 2004)Material types for joints (from 220 to 230): Take care about friction and damping definition, which may give noisy solutions. It is recommended to make simple tests to validate the response before using it in a model

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Stable DatasetsStable Datasets

Material SectionAvoid material type 2Use material type 17 in place of type 11Use material type 24 in place of type 20Use material type 45 in place of type 21Avoid material type 26Use material type 103 in place of type 102Use material type 106 in place of type 105Avoid material type 118Use material type 204 in place of type 203

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Stable DatasetsStable Datasets

Elements SectionShell elements:

Check element quality:Warping, internal angles, aspect ratio

Split bad quads into 2 trianglesSplit quads on structural corners into 2 triangles

Solid elements:hexa and 10-node tetra elements are preferable.SRI hexa might be sensitive for negative volume error4-node tetra are good for foam, honeycomb or elastic.Avoid 4-node tetra for plasticity and rubber (element will lock due to incompressibility)Avoid hexa degenerated to tetra

Spring element (Material type 220): recommended that the connectivity nodes are at the same position.

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Stable DatasetsStable Datasets

Constraints SectionUse Nodal constraints only to model hinges with common nodesContact:

Do not cover solid elements by null shells. It is not needed. Code extracts surfaces automaticallyIt will blow up your modelIt may cause numerical problems if the solid elements are eliminated

Use default penalty 0.1Use nonlinear stiffness to avoid perforations (FSVNL =10-100)

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Stable DatasetsStable Datasets

Solver V2004 will help. Many internal improvements on numerical precision and numerical robustness:

PLINKsCNTAC type 37SOLIDs (internal damping)…

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Correct DatasetsCorrect Datasets

Often runs has to be repeated because of input deck errors detected late.Try to avoid input errors from the beginningValidate your model before you start optimization or parametric runs Be careful during the set up of the model

Think about Unit systemsBe careful with copy/paste operationsCheck data you receive from others

Try new or unknown options in small models to understand the behavior before using them in complex models.

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Correct DatasetsCorrect Datasets

Solver V2004 will help. Many new data checks are implemented to detect dataset errors:

Airbag checkMetric checkDuplicated GROUP name checkSLIPR node coincidence check…