Register
S
peaker: Alex Razoumov (SFU)
Abstract: In this more advanced session
\, you will learn how to perform remote and large-scale visualization on t
he Alliance'\;s HPC clusters\, for cases where you do not want to -- or
simply cannot -- download the dataset to your local computer.
Smal
l-scale remote visualization can be accomplished using JupyterHub or Open
OnDemand\, running either standalone ParaView or a ParaView client within
a remote desktop environment.
For larger-scale interactive visualiz
ation\, we will run a parallel ParaView server for data processing and ren
dering on a cluster and connect to it from a ParaView client on your local
computer. This setup enables interactive visualization of very large data
sets (multi-GB and potentially TB-scale) without transferring data locally
\, and without being limited by your own system'\;s memory or CPU/GPU r
esources.
We will explore workflows involving both time and camera
animation. We will then save the interactive visualization pipeline as a s
cript and run it remotely as a batch job on the cluster\, performing all r
endering off-screen (without opening any windows) to generate animation fi
les.
Finally\, we will discuss strategies for optimizing data for p
arallel I/O and the compute resources required to process very large datas
ets.
Prerequisites: To participate in the hands-on exercises\, plea
se install ParaView on your computer. You can download it from the offici
al website. Familiarity with ParaView basics -- creating a visualizati
on pipeline\, applying filters\, and scripting -- is assumed and covered i
n earlier sessions.