Who are you and why you care about this

I am Hank Childs, Lawrence Berkeley and UC Davis My professional career to date has been developing and delivering

big data visualization solutions to users I am currently responsible for delivering HPC visualization and

analysis software to many different communities: VACET / SciDAC (CSWA) Longhorn / XSEDE (CSWA) 10-05 Climate community (CSWA) NEAMS (PI) NERSC Analytics team

What research activities are you doing in this area?

Non-research activities: Software development Frontline support

Research: What are the bottlenecks

at massive concurrency? How can we utilize hybrid

parallelism to achieve better performance and scalability?

How can we make non-embarrassingly parallel algorithms more scalable?

& more…

1 trillion cell mesh rendered on 16K cores of LBNL Franklin

4608^2 image using 216K cores of ORNL Jaguar

What have you developed, where can people download it, and why should

they use it? VisIt is an open

source, richly featured, turn-key application for large data.

http://www.llnl.gov/visit Popular

R&D 100 award in 2005

Used on many of the Top500

>>>100K downloads217 pin reactor cooling simulation

Run on ¼ of Argonne BG/P Image credit: Paul Fischer, ANL

1 billion grid points / time slice

What are the GAPS in this area?

I will let everyone else talk about in situ processing, massively concurrency, fault tolerance, etc.

Our community has invested massively in CPU-based software. This investment is in the hundreds of

person-years. (VisIt alone represents 75 man-years,

1.5M lines of code) How are we going to translate this

investment to the accelerator environment?

Tell us what you think the future is

We are envisioning machines where we won’t be able to: get the data off the machine for later

storage regularly post-process data

This is a step backwards for the user. We must innovate approaches that allow

users to continue to do critical activities, such as interactive exploration