Astrophysics, Biology, Climate, Combustion, Fusion, HEP, Nanoscience Sim Scientist DOE NL

Astrophysics, Biology, Climate, Combustion, Fusion, HEP, Nanoscience

Sim Scientist

DOE NL

5/24/2004 Chicago Meeting DOE Data Management 2

Workflows

• Critical need: Enable (and Automate) Scientific Work Flows– Data Generation. – Data Storage– Data Transfer– Data Analysis– Visualization

• An order of magnitude more effort can be spent on manually managing these work flows than on performing the simulation itself.

• Workflows are not static.

Simulations• Simulations run in batch mode.• Remaining workflow interactive or “on demand.”• Simulation and analyses performed by distributed teams

of research scientists.– Need to access remote and distributed data,

resources.– Need for distributed collaborative environments.

• We will not present solutions in this talk!– Some solutions will be problem dependent.

• Example: Remote Viz. vs. Local Viz., Parallel HDF5 vs. Parallel netcdf, …

How do we do simulation science (I)

• Let’s suppose that we have a verified HPC code.– I will use the Gyrokinetic Toroidal Code (GTC) to

serve as an example.• We also suppose that we have a suite of analysis and

visualization programs.• We want to eventually compare the output of this to

theoretical and/or experimental and/or other simulation results.

A fast peek at the workflow

Thought

Computevolume average

Compute tracer particle energy,

positionmomentum

Compute 1d and 2d radial and

velocity profiles

Computecorrelationfunctions

Feature tracking of the

heat potential

Thought

Global Analysis

TB’s

viz features metadata

movies

Let’s go through the scientific process

requirements:1TB/sim now: 10TB/year100TB/sim 5yr: .5PB/year58Mbs now, 1.6Gbs 5 yr

Data TransferData GenerationData AnalysisData VisualizationData Storage

Stage 1: Initial Question + Thought

• Scientist thinks of a problem to answer a physical question.

• Example:– What saturates transport driven

by Ion Temperature Gradient?

• Requirements:– Possible changes in the code.– New visualization routines to

examine particles.– New modifications in analysis

tools.

Question

Thought

thought

Question

Collaborate withO(5) people: faceto face, phone.

Stage 2: Change code add analysis• If

– Code is mature, go to stage 4.

• Else– Scientists modify HPC code to

put in new routines for new physics, new capabilities.

– Scientists change the code to answer the question.

– If necessary, analysis/viz routines are added/modified

– where do the inputs come from?• experiments, other sims,

theory.

Thought

thought

QuestionCodemodifications

Total output = 1TB/full run40 hours= 58Mbs: now

5 years: 0.1PB/hero run150 hours= 1.6Gbs

O(5) people modify code

Code input Code input

computation

I/O I/O

Runtime

Stage 3: Debugging Stage• Scientists modify HPC code to

put in new routines for new physics

• Scientist generally run a parameter survey to answer the question(s).

• Scientist change the code to answer the question.

• 1 to 2 people debug the code.• Verify code again, regression

Thought

weeks…

TimeQuestion

Codemodifications

Total output = 0.1Mbs

Thought

Compute volume average

Continue Run sequence

thought

results are thrown away

Stage 4: Run production code.• Now the scientist has confidence in

the modifications.• Scientist generally run a parameter

survey and/or sensitivity analysis to answer the question(s).

• Scientist need good analysis and visualization routines.

• O(3) look at raw data and run analysis programs.

– Filter data

– Look for features for the larger group.

• O(10) look at end viz. and interpret the results.

TimeQuestion

0.01Mbs

Particles50Mbs

Production run

Interpret resultsthought

Thought

Compute volume averageCompute tracer particle energy,

position, momentum

Compute 1d and 2d radial and

velocity profiles

VIZ VIZ

VIZscalar 60 Mbs

.5% TS

1000TS

data can flow from RAM to RAM/disk/WAN/LAN.

Stage 4a: Data Management Observations.

• We must understand1. Data Generation from simulation and

analysis routines.2. Size of Data being generated.

– Latency issues for access patterns.

– Can we develop good compression techniques?

– Bandwidth/disk speed issues.– Do we need non-volatile

storage? RAM-RAM, RAM – Disk-tape

– “Plug and play” analysis routines, need a common data model

– non-trivial to transfer from N processors to M processors!

– Bottleneck analysis is too slow.

Codemodifications

thought

Thought

HPC VIZ

VIZVIZ

particles 50Mbs

0.01Mbs

scalar 60 Mbs

.5% TS

1000TS

•Save scalar data for more post-processing.•Save Viz data•Toss particle Data

Particles50Mbs

Stage 5: Feedback Stage• After the production run we

interpret the results• We then ask a series of

questions:– Do I have adequate analysis

routines?

– Was the original hypothesis correct?

– Should the model equations change?

– Do we need to modify it?

• If everything is ok, should we continue the parameter survey?

Production run

Interpret results…

Thought

Computecorrelation

function

Thought

The workflow is changing!

comparison to other data, theory, sim., experiments

Stage 5: Observations• To expedite this process

– Need standard data model(s).– Can we build analysis routines which can be used for multiple codes

and or multiple disciplines??

• Data Model must allow flexibility.– Commonly we add/remove variables used in the simulations/analysis

routines.– Need for metadata, annotation, and provenance:

• Nature of Metadata– Code versions, compiler information, machine configuration.– Simulation parameters, model parameters.– Information on simulation inputs.

– Need for tools to record provenance in databases.• Additional provenance (above that provided by the above metadata)

needed to describe:– Reliability of data; how the data arrived in the form in which it was

accessed; data ownership.

Production run

Stage 5: Observations• Data Analysis routines can include

– Data Transformation• Format transformation• Reduction• Coordinate transformation• Unit transformation• Creation of derived data• …

– Feature detection, extraction, tracking• Define metadata• Find regions of interest• Perform level set analyses in spacetime• Perform born analyses.

– Inverse feature tracking

– Statistical Analysis: PCA, Comparative Component Analyses, data fitting, correlations

Production run

Thought

Stage 5: Observations• Visualization Needs

– Local, Remote, Interactive, Collaborative, Quantitative, Comparative

– Platforms

– Fusion of different data types• Experimental, Theoretical, Computational,…• New representations

Production run

Thought

Stage 6: Complete parameter survey

• Complete all of the runs for the parameter survey to answer the question.

• 1 – 3 are looking at the results during the parameter survey.

Production run

Interpret results Production run

Interpret results …Thought

Featuretracking

Thought

Stage 7: Run a “large” Hero run• Now we can run a high

resolution case, which will run for a very long time.

• O(10) are looking at the results.

LARGE Hero run, Interpret results…Thought

Thought

Stage 8: Assimilate the results.• Did I answer the question?

– Yes• Now publish a paper.• O(10+) look at results.• Compare to experiment

– Details here.

• What do we need stored?– Short term storage

– Long term storage

– NO• Go back to Stage 1:

Question

Interpret results

TB’s viz features metadata movies

Data repository

Global Analysis

Data Miningtools

assimilate results

Stage 9: Other scientist use information

• Now other scientist can look at this information and use it for their analysis, or input for their simulation.

• What is the data access patterns– Global Interactive VIZ: GB’s of

data/time slice, TB’s in the future.

– Bulk data is accessed numerous times.

– Look at derived quantities. MB’s to GB’s of data.

• How long do we keep the data?– Generally less than 5 years.

Data repository

Global Analysis

TB’s viz features metadata movies

Let Thought be the bottleneck • Simulation Scientists generally have scripts to semi-

automate parts of the workflow.• To expedite this process they need to

– Automate the workflow as much as possible.– Remove the bottlenecks

• Better visualization, better data analysis routines, will allow users to decrease the interpretation time.

• Better routines to “find the needle in the haystack” will allow the thought process to be decreased: Feature detection/tracking

• Faster turn around time for simulations will decrease the code runtimes.

– Better numerical algorithms, more scalable algorithms.– Faster processors, faster networking, faster I/O.– More HPC systems, more end stations.

Summary:

• Biggest bottleneck: Interpretation of Results.– This is the biggest bottleneck because

• Babysitting– Scientists spend their “real-time” babysitting

computational experiments. [trying to interpret results, move data, and orchestrate the computational pipeline].

– Deciding if the analysis routines are working properly with this “new” data.

• Non-scalable data analysis routines– Looking for the “needle in the haystack”.– Better analysis routines could mean less time in the

thought process and in the interpretation of the results.

• The entire scientific process can not be fully automated.

Workflows• No changes in these workflows.

Section 3: Astrophysical Simulation Workflow Cycle

Parallel HDF5

Run Simulationbatch job on capability

system

Archivecheckpoint

filesto HPSS

Simulationgenerates

checkpointfiles

MSS, Disks, & OS

Migrate subset of checkpointfiles to local

cluster

ApplicationLayer

GPFS PVFSor

LUSTRE

Vis & Analysison local

Beowulf cluster

ContinueSimulation?

Start NewSimulation?

StorageLayer

ParallelI/O Layer

Biomolecular Simulation

Molecular System

Construction

StatisticalAnalysis

StructureDatabase(e.g. PDB)

Parameterization

Hardware, OS, Math Libraries, MSS (HPSS)

MolecularTrajectories

StorageManagement,

Data MovementAnd Access

Workflow

DesignMolecular

System

Analysis&

Visualization

ComputerSimulation

ArchiveTrajectories

Review/Curation

Trajectory Database Server(e.g.BioSimGrid)

Large Scale Temporary Storage

Raw Data

Visualization

Combustion Workflow

GTC Workflow

Deposit the charge of very particle on the grid

Solve Poisson equation to get the potential on the grid

Calculate the electric field

Gather the forces from the grid to the particles and push them

Do process migration with the particles that have moved out of their current domain

GTC Compute volumeaveragedquantities

Compute tracerParticle

Energy, positionmomentum

Compute 1dand 2d radial and velocity profiles

analysis

ComputeCorrelation functions

NIMROD Workflow

nimrod.inNIMROD

Run-timeConfig

nimhdf, nimfl, nimplot, …

Run-timeConfig

Phi.h5nimfl.bin

XdrawAVS/Express

SCIRunOpenDX

AnimationsAnimationsAnimations

ImagesImagesImagesImages

Screen

nimset dump.00000

Inputfiles

fluxgrid.in

nimhdf.innimfl,.in

discharge energy nimhist

data for every time step

dump.*

Restart file

Initial Run

VMEC, JSOLVER, EFIT, etc

M3D Simulation Studies 2009 (rough estimate)

Restart 1 Restart 2 Restart N

HPSS (NERSC)

PPPL Local Project Disks

Run M3D at NERSC on 10,000 processors for 20 hours per segment

Post-process locally on PPPL upgraded cluster. Requires 10 min per time slice to analyze. Typically analyze 20 time slices.

1 TB files, transfer time 10 min, if parallel?

A Simplified VORPAL Workflow

Initial Parameters

InputData

VORPAL

Run-time Configurations

D1 DnD2 D3

Data Filtering/Extraction

D1 DnD2 D3

Image Generator (Xdraw)

png1 pngnpng2 png3

Time slices

Sim1Animation

Sim2Animation

SimXAnimation

Currently, the workflow is handled by a set of scripts. Data movement is handled either by scripts or manually.

TRANSP Workflow

Preliminary dataAnalysis andPreparation

(largely automated)

DiagnosticHardware

Experiments (CMod, DIII-D, JET, MAST, NSTX)

20-50 signals {f(t), f(x,t)}Plasma position, Shape,Temperatures, DensitiesField, Current, RF andBeam Injected Powers.

TRANSP Analysis*:Current diffusion, MHD equilibrium, fast ions,

thermal plasma heating; power, particle and

momentum balance.

TRANSP Analysis*:Current diffusion, MHD equilibrium, fast ions,

thermal plasma heating; power, particle and

momentum balance.

Experiment simulationOutput Database

~1000-2000 signals{f(t), f(x,t)}

Visualization

Load RelationalDatabases

Detailed (3d) time-slice physics simulations (GS2, ORBIT, M3D…)

Pre- and Post-processing at the experimental site…

D. McCune 23 Apr 2004

Workflow for Pellet Injection Simulations

Preliminary Analysis (Deciding run parameters)

Run 1D Pellet code Table of energy sinkterm as a function offlux-surface and time

Run AMR Production Code

HDF5 data filesRun post-processing codeto compute visualizationvariables and other diagnosticquantities (e.g. total energy) for plotting

Visualize field quantitiesin computational spaceusing ChomboVis

Create diagnostic plots

Interpolate solution on finest mesh. Create data files for plotting field quantities in a torus

Input Files

HDF5 data files of plotting variables

ASCII files of diagnostic variables

Interpolated data files (binary)Visualize field quantitiesin a torus using AVS or ensight

Majority of Time

Degas2 Workflow

High-Energy Physics WorkflowTypical of a major collaboration

SIMULATION

Users: Simulation Team

At: 10s of sites

DATA ACQUISITION

Users: DAQ team

At: 1 site

DATABASES:

< 1 terabyteConditions,Metadata

AndWorkflow

RECONSTRUCTION (Feature

Extractions)

Users: Reconstruction Team

At: few sites

SKIMMING/FILTERING

Users: Skim Team

At: few sites

ANALYSIS

Users: All Physicists

At: 100+ Sites

100s of terabytes today

10s of petabytes in 2010

Nuclear Physics WorkflowTypical of a major collaboration

SIMULATION

Users: Simulation Team

At: 10s of sites

DATA ACQUISITION

Users: DAQ team

At: 1 site

DATABASES:

< 1 terabyteConditions,Metadata

AndWorkflow

RECONSTRUCTION (Feature

Extractions)

Users: Reconstruction Team

At: few sites

SKIMMING/FILTERING

Users: Skim Team

At: few sites

ANALYSIS

Users: All Physicists

At: 100+ Sites

100s of terabytes today

10s of petabytes in 2010

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Astrophysics, Biology, Climate, Combustion, Fusion, HEP, Nanoscience Sim Scientist DOE NL

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