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Performance Measures x.x, x.x, and x.x. Visualization and Analysis Activities May 19, 2009. Hank Childs VisIt Architect. Outline. VisIt project overview Visualization and analysis highlights with the Nek code Why petascale computing will change the rules Summary & future work. Outline.

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outline
Outline
  • VisIt project overview
  • Visualization and analysis highlights with the Nek code
  • Why petascale computing will change the rules
  • Summary & future work
outline1
Outline
  • VisIt project overview
  • Visualization and analysis highlights with the Nek code
  • Why petascale computing will change the rules
  • Summary & future work
visit is a richly featured turnkey application
VisIt is a richly featured, turnkey application

27B element

Rayleigh-Taylor Instability

(MIRANDA, BG/L)

  • VisIt is an open source, end user visualization and analysis tool for simulated and experimental data
    • Used by: physicists, engineers, code developers, vis experts
    • >100K downloads on web
  • R&D 100 award in 2005
  • Used “heavily to exclusively” on 6 of world’s top 8 supercomputers

4

terribly named intended for more than just visualization

Quantitative Analysis

?

=

  • Data Exploration
  • Comparative Analysis
  • Visual
  • Debugging

Presentations

Terribly Named!! Intended for more than just visualization!
visit has a rich feature set that can impact many science areas
VisIt has a rich feature set that can impact many science areas.

Meshes: rectilinear, curvilinear, unstructured, point, AMR

Data: scalar, vector, tensor, material, species

Dimension: 1D, 2D, 3D, time varying

Rendering (~15): pseudocolor, volume rendering, hedgehogs, glyphs, mesh lines, etc…

Data manipulation (~40): slicing, contouring, clipping, thresholding, restrict to box, reflect, project, revolve, …

File formats (~85)

Derived quantities: >100 interoperable building blocks

+,-,*,/, gradient, mesh quality, if-then-else, and, or, not

Many general features: position lights, make movie, etc

Queries (~50): ways to pull out quantitative information, debugging, comparative analysis

visit employs a parallelized client server architecture
Client-server observations:

Good for remote visualization

Leverages available resources

Scales well

No need to move data

Additional design considerations:

Plugins

Multiple UIs: GUI (Qt), CLI (Python), more…

localhost – Linux, Windows, Mac

Graphics Hardware

VisIt employs a parallelized client-server architecture.

remote machine

User

data

Parallel vis resources

the visit team focuses on making a robust usable product for end users
The VisIt team focuses on making a robust, usable product for end users.
  • Manuals
    • 300 page user manual
    • 200 page command line interface manual
    • “Getting your data into VisIt” manual
  • Wiki for users (and developers)
  • Revision control, nightly regression testing, etc
  • Executables for all major platforms
  • Day long class, complete with exercises

Slides from the VisIt class

visit is a vibrant project with many participants
VisIt is a vibrant project with many participants.

Over 50 person-years of effort

Over one million lines of code

Partnership between: Department of Energy’s Office of Nuclear Energy, Office of Science, and National Nuclear Security Agency, and among others

GNEP funds LLNL

to support GNEP

codes at Argonne

Project started

Developers from

LLNL, LBL, & ORNL

Start dev in repo

AWE enters repo

2005 R&D100

Partnership with

CEA is developed

UC Davis & UUtah

research done

in VisIt repo

LLNL user

community

transitioned

to VisIt

SciDAC Outreach

Center enables

Public SW repo

Saudi Aramco

funds LLNL to

support VisIt

User community

grows, including

AWE & ASC

Alliance schools

2000

2003

2007

Spring ‘07

2007

Summer‘07

‘07-’08

‘07-’08

2004-6

2005

Spring ‘08

Institutional support

leverages effort from

many labs

VACET is funded

More developers

Entering repo all

the time

Fall ‘06

2008

Spring ‘09

outline2
Outline
  • VisIt project overview
  • Visualization and analysis highlights with the Nek code
  • Why petascale computing will change the rules
  • Summary & future work
flow analysis for 217 pin simulation 1 billion grid points
Flow analysis for 217 pin simulation / 1 billion grid points

217 pin reactor

cooling simulation.

Run on ¼ of Argonne BG/P.

tracing particles through the channels work in progress1

Observe which channels the

particles pass through

Observe where particles come out

Tracing particles through the channels (work in progress)

Place 1000 particles in one channel

tracing particles through the channels work in progress2

White triangle shows current channel

Tracing particles through the channels (work in progress)
  • Two different “matrices” to describe flow from channel I to channel J
    • Exit location versus travel time in channel
    • Issues: pathlines vs streamlines, 12X vs A12
the algorithm for advecting particles is complex
The algorithm for advecting particles is complex.
  • Two extremes:
    • Partition data over processors and pass particles amongst processors
      • Parallel inefficiency!
    • Partition seed points over processors and process necessary data for advection
      • Redundant I/O!
  • Hybrid solution:
    • Master-slave approach that adapts between parallel inefficiencies and redundant I/O
      • SC09 submission
the visit project provides outstanding leverage to this campaign
The VisIt project provides outstanding leverage to this campaign.
  • The particle advection code represents 2+ man-years of effort from developers at UC Davis, Oak Ridge, Lawrence Berkeley, and Lawrence Livermore
    • Efforts of VACET, a SciDAC center for visualization and analysis
  • The development time to adapt this algorithm for our custom analysis is on the order of weeks.
  • Further, VisIt represents 50+ man-years of effort, much of which is highly relevant to this campaign, including:
    • Parallel infrastructure
    • Visualization algorithms (contouring, etc)
    • Parallel rendering
    • Etc.
summary of activities
Summary of activities
  • Activities are a mix of development and support
    • Development
      • File format readers
      • New analysis capability
      • Bug fixes / usability
      • Tuning, tuning, tuning
    • Support
      • Movies
      • “How do I …?”
      • Scripts
outline3
Outline
  • VisIt project overview
  • Visualization and analysis highlights with the Nek code
  • Why petascale computing will change the rules
  • Summary & future work
petascale visualization and analysis will change the rules
Petascale visualization and analysis will change the rules.

Michael Strayer (U.S. DoE Office of Science): “petascale is not business as usual”

Especially true for visualization and analysis!

Large scale data creates two incredible challenges: scale and complexity

Scale is not “business as usual”

Current trajectory for terascale postprocessing will be cost prohibitive at the petascale

We will need “smart” techniques in production environments

More resolution leads to more and more complexity

Will the “business as usual” techniques still suffice?

Shortened out complexity portion of this talk: data anlaysis is key.

current modes of terascale visualization and analysis 1
SC and dedicated cluster share disk

Dedicated cluster has good I/O access

SC runs lightweight OS; dedicated cluster runs Linux

Graphics cards on dedicated cluster

Current modes of terascale visualization and analysis (1)

ASC BG/L

Gauss

I/O

slide25
Simulation, processing both done on purple

Simulation writes to disk, vis. job reads from disk

SC runs full OS (AIX)

No graphics cards

Current modes of terascale visualization and analysis (2)

ASC Purple

ASC Purple

I/O

Portion of purple for

Vis & analysis

slide26
Rayleigh Taylor instability by MIRANDA code

27 billion elements

Run on ASC BG/L

Visualized on gauss using VisIt

These modes of processing have worked well at the terascale.

further observations about the terascale gameplan
Further observations about the “terascale” gameplan.

No need to move data.

(Important!)

Not scaling up to huge numbers of cores.

Current algorithm used by major vis tools (VisIt, EnSight, ParaView):

Read in all data from disk and keep in primary memory.

buying a dedicated vis machine will be cost prohibitive at the petascale
Buying a dedicated vis. machine will be cost-prohibitive at the petascale.

Why is it so expensive?

Visualization and analysis is:

I/O-intensive

Memory-intensive

Compute has become cheap; memory and I/O are still expensive.

ASC BG/L

Gauss

512 procs

$1-$2M

~600 TF

I/O

4 years: 5 PF

5000? procs

$15M (!!!)

visualization performance is based on total memory and i o
Visualization performance is based on total memory and I/O.

Trend for next generation of supercomputers is weaker and weaker relative I/O

To maintain performance, we need more I/O

So we will have to use more nodes to get more I/O

Recent series of hero runs for 1 trillion cell data set:

16K cores

I/O = ~5 minutes, processing = ~10 seconds

“Petascale machine”

Terascale machine

  • Vis is almost always >50% I/O and sometimes 98% I/O
  • Amount of data to visualize is typically O(total mem)
  • --> Relative I/O (ratio of total memory and I/O) is key

Memory

I/O

FLOPs

using a portion of the sc is also problematic at the petascale
Using a portion of the SC is also problematic at the petascale.

Fundamentally, we are I/O-bound, not compute bound

multi-core has limited value-added

To increase I/O, we will need to use more of the machine

ASC Purple

ASC Purple

I/O

slide31
Additionally:

Use cases are “bursty” – do we want the supercomputer sitting idle while someone studies results?

Can we afford to devote a large portion of the SC to visualization and analysis?

Lightweight OS’s present challenges

Using a portion of the SC is also problematic at the petascale.

ASC Purple

ASC Purple

I/O

anedoctal evidence relative i o is getting slower at llnl
Anedoctal evidence: relative I/O is getting slower at LLNL.

Time to write memory to disk

  • “I/O doesn’t pay the bills.”
part of the problem is hw i o memory but the rest is software
Part of the problem is HW (I/O & memory), but the rest is software.

Production visualization and analysis tools use “pure parallelism”.

Research has established “smart techniques”: viable, effective alternatives to pure parallelism

Out of core processing

In situ processing

Multi-resolution techniques

Not going to dig in on these, but none is a panacea in isolation.

There are gaps here (production-readiness & more)

These techniques are difficult to implement.

Petascale computing makes them cost effective.

summary of techniques
Summary of techniques
  • Pure parallelism can be used for anything, but it takes a lot of resources
  • Smart techniques can only be used situationally.
  • Strategy 1:
    • Stick with pure parallelism and live with high machine costs.
  • Other strategies?
    • Here are some assumptions:
      • We’re not going to buy massive dedicated clusters
      • We can fall back on the super computer, but only rarely
alternate strategy smart techniques
Alternate strategy: smart techniques

Multi-res

In situ

Use P.P.

for remaining

~5% on SC

Out-of-core

All visualization and analysis work

outline4
Outline
  • VisIt project overview
  • Visualization and analysis highlights with the Nek code
  • Why petascale computing will change the rules
  • Summary & future work
summary future work
Summary & Future Work
  • This effort enables Nek (& others) to successfully meet their visualization and analysis needs.
  • Lots of future work:
    • In situ visualization and analysis
      • Support for the petascale
    • Subsetting and enhancements for code interoperability
    • Energy groups
    • Continued support for analysis, scripts, movies, bugs, etc. (LOTS of time spent here)
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