The importance of locality in the visualization of large datasets

John Brooke1, James Marsh1, Steve Pettipher1,

Lakshmi Sastry2

1 The University of Manchester2 CLRC Rutherford Appleton Laboratory

http://www.man.ac.uk

http://www.clrc.ac.uk

GODIVA - Grid for Ocean Diagnostics, Interactive Visualization and Analysis

Reading (Coordinator) Prof. K. Haines Assimilation, Modelling, Data services

Southampton Oceanography Centre

Prof. P. Killworth Oceanography, visualization

Imperial Prof. A. Goddard Adaptive meshing and finite elements

Manchester Dr J. Brooke,

Dr S. Pettifer

Local rendering and visualization

Rutherford Dr L. Sastry Visualization web services

Department PI ExpertiseDepartment PI Expertise

GODIVA: Background

At Reading and SOC we hold copies of various datasets (~2TB)– Mainly from models of oceans and atmosphere– Also some observational data (e.g. satellite data)– From Met Office, SOC, ECMWF, more

We serve these datasets to many end users– Scientists (1000s of hits per year)– Industry (e.g. British Maritime Technology)

Datasets are in a variety of formats– netCDF, GRIB, HDF, HDF5 …

Data do not conform to naming conventions– E.g. “temp” instead of “sea_water_potential_temperature”

GODIVA: Background (2)

There is a clear need to make access to these datasets easier– Users shouldn’t have to know details of how data are stored

Hence development of GADS (Grid Access Data Service) Developed as part of GODIVA project

– Grid for Ocean Diagnostics, Interactive Visualisation and Analysis

– NERC e-Science pilot project

Originally developed by Woolf et al (2003) Allows richer queries and more flexibility than DODS

standard– Although we plan to implement a DODS translation layer

GODIVA Web Portal

• Allows users to interactively select data for download using a GUI

• Users can create movies on the fly

• cf. Live Access Server

Advantages of GADS

User’s don’t need to know anything about storage details Can expose data with conventional names without

changing data files Users can choose their preferred data format, irrespective

of how data are stored Behaves as aggregation server

– Delivers single file, even if original data spanned several files Deployed as a Web Service

– Can be called from any platform/language– Can be called programmatically (easily incorporated into larger

systems), workflows– Java / Apache Axis / Tomcat

GODIVA: Science Drivers

The Thermohaline

Circulation

1. Convection and Downwelling Sites (SOC)

2. Thermodynamic Water Transformation Diagnostics (ESSC)

3. Unstructured Mesh Convection Modelling (IC)

Science: Convection and Downwelling

How are Convection and Downwelling related?

Visualize Isopycnal/Isothermal surfaces in time

Superpose Currents/Vertical velocities

Diagnostics eg. Q vectors Commodity Processors for

Remote access

Isopycnal 1027.28 kg/m3

Techology: Unstructured Mesh Modelling

How to resolve convection, mixing and downwelling?

Idealised Experiments Transformation and

visualization of 3D unstructured mesh data

Community Visualization development

Technology: Visualization and modelling

Software Condor Implementation XML Metadata Libraries Inferno - new approach to

Grid middleware Commodity Graphics Card

viewers Web services for visualization

tasks

GRID Large 3D arrays 1-10Gb/file Diverse computing resources

(T3E, SGI O3000 and Onyx, graphics workstations, Linux PCs)

Diverse data resources (eg. Winds, surface fluxes, assimilation data)

Intelligent use of locality

Locality-aware architecture

A. Data Storage

B. Data Service C. Visualization Service

D. Client viewer, rendering either locally or by using visualization service

Sends data to

Requests data from

Visualization: Local rendering

Investigating fast volumetric-rendering Generating geometric isosurfaces:

– Computationally expensive processing

– Changing the isovalue requires re-processing

Direct rendering using 3D textures:– Fast on modern PC graphics cards

– Can exploit programmable GPUs

GODIVA architecture allows data to be pre-fetched– Can hide network effects by fetching as user is viewing locally

– Local rendering can bypass visualization service

Direct Volume Rendering

Volume data stored in a 3D texture

– Multiple planes are drawn parallel to the screen sampling the texture

– Enough samples will reconstruct scalar data according to the nyqhist frequency

Surfaces look flat– No shading

Klaus Engel

Fragment Programs

What is a fragment program?– A short, simple program that is executed whenever a fragment is

drawn• A fragment is similar to a pixel, but may later be covered by another

fragment

– Determines actual pixel colour• Usually by performing vector operations

– Often uses one or more textures as input values

Shaded isosurfaces

Stack of partially transparent slices– 3D texture also contains isovalue gradient

– Each pixel is shaded according to the dot-product of the gradient and light vectors

– Requires small fragment program to describe how to perform the shading

Looks “realistic”– Small features more visible

– Can interactively change the isovalue

Shaded isosurface example

Isopycnal Surfaces

John Stark/SOC

Isopycnals

Density is generally more interesting than depth

Slow to render using existing packages

Can it be made interactive?– Second 3D texture containing

attribute data– Colour fragments based on this

data as well as the isosurface gradient

Simple?

No!– Need even more texture memory for extra data– Lighting calculation and texture look-ups are being performed for

every fragment/voxel, even if invisible– Performance is dependent on screen size (number of fragments)– When textures won’t fit in graphics card, performance suffers

terribly Current state

– Can render at approximately 10 frames per second– At present we need information as to what features are

scientifically important, not part of a general modular package

Multiple passes

First Pass: render just silhouette of density isosurface– Similar to earlier toy example– No lighting– Use colour of pixel to store the voxel co-ordinates that pixel

represents (r,g,b) vs. (x,y,z) Second Pass: colour and shade pixels

– Use output of first pass as a 2D texture– Use a fragment shader to combine first pass texture with 3D

gradient and attribute texture– Lighting calculation only performed for visible pixels

Result

Both density and attribute textures don’t need to be in memory at the same time– Opportunity to replace the textures in between passes

Fewer fragment program instructions– Speed much less dependent on screen size

Can re-use first pass in order to render second isopycnal for the same density

Use of texture mapping

We can use colour in the texture mapping to visualize a second quantity on the isopycnal surface

Resolution very high, demands high quality of dataset, e.g missing values can be seen

Next stage is interaction with the visualization service local renderer allows user to alert service to ranges of interest which can then be rendered by more conventional means.

Visualization and data services

Our local viewer can interact with either the data services (GADS) or the Visualization services from RAL.

Local viewing can allow rapid exploration of features, key scenes can be rendered to higher quality by the visualization service.

In an unstable Grid if we lose contact with the visualization service we can continue to work with cached data.

Next GODIVA challenges involve exploiting this locality intelligently.

Conclusions

Beyond client-server visualization, local renderer can be as powerful as centralized server.

GODIVA architecture fits to a web services world. With improved visualization techniques, quality control of

datasets becomes important. Next area of research is to explore possiblilities of caching

and prefetching data for both the central visualization service and the local renderer.

Demos on NERC/NIeES and Reading e-Science Centre stands.