Bridging the Gap Between High Quality and High Performance for HPC Visualization

Transcription

1 Bridging the Gap Between High Quality and High Performance for HPC Visualization Rob Sisneros National Center for Supercomputing Applications University of Illinois at Urbana Champaign

2 Outline Why am I here? NVIDIA/NCSA collaboration Current efforts address need in HPC visualization This talk Some context Some opinions on HPC Visualization A gap in the state of the art Why it is critical to address this gap Introduction to IndeX + ParaView

3 CONTEXT

4 Visualization: Data Analysis (e.g. statistics) Graphics (e.g. plots) Operation. Operation. Operation Understanding Pretty Pictures

5 NCSA Data Analysis and Visualization Team Recently formalized 3 Members and 2 students (and we re hiring!) Various visualization expertise Large scale visualization, algorithms, and I/O Data analysis Visual analytics Infovis Production visualization Our mission Support large scale science by making visualization available to teams utilizing NSF computing resources (primarily Blue Waters) Further the science of visualization through cutting edge research

6 Blue Waters (AKA The World s Best Cray) Total Cabinets: 288 Compute Nodes (XE + XK): 26,864 Aggregate Memory: 1.5 PB SCUBA Subsystem - Storage Configuration for User Best Access 10/40/100 Gb Ethernet Switch External Servers IB Switch >1 TB/sec 120+ Gb/sec 100 GB/sec Gbps WAN Spectra Logic: 300 usable PB Sonexion: 26 usable PB

7 Talking Points Blue Waters Compelling use cases of GPUs for visualization Data staging on XK nodes In situ analysis/visualization Full nodes or even just GPUs Drivers and methods for remote visualization And also We re hiring ^ to at least talk to

8 Visualization Software for HPC Centers Scalable General Widely used Accessibility Utility Ongoing development Active community Open source

9 VisIt Scalable Scaled > 100K cores Offer interactive client/server mode Can operate in batch mode In situ support Rich set of data operators Native support for many file formats Paraview

10 HPC VISUALIZATION RESEARCH

11 Data Structures I/O Disk In Situ Frameworks Simulation Data Generation

12 Data Structures I/O Disk In Situ Frameworks This area Where the data we visualize and analyze lives The data that drives science and therefore support How does support affect our research?

13 Data Structures I/O Disk In Situ Frameworks How Visualization Fits in HPC Research HPC research w/ Visualization application Visualization research w/ HPC application

14 How Visualization Research Fits in HPC Research HPC research w/ Visualization application Theoretical and applied Value often judged via application to specific science domain Novelty is often at odds with adoption Not investment priority at HPC centers Visualization research w/ HPC application Applied Value often judged via Performance Performance Performance Makes bosses happy

15 WHAT S MISSING?

16 Mind the Gap ^ Widening Should we (at least sometimes) offer capability in lieu of performance? Slow codes on a supercomputer simply aren t publishable The cutting edge of visualization research is providing data models and algorithmic developments to address ~95% of typical data analysis/visualization tasks for expected future hardware (Yay!) Those I know using GPUs for visualization are in the 5% Real Q+A session: Q: How can I use GPUs for vis? A: Why would you want to do that? Are you sure you want to?

17 The Problem Sometimes I have huge data, and I want an insanely high quality rendering Or I have huge data, and I want interactivity

18 The Good News Future HPC Visualization will finally begin to focus on interactivity Reason 1: The 5% will bring it with them Reason 2: The data is too big not to use interactivity

19 HPC Data CONVERGING TO INTERACTIVITY

20 Data Reductions for Visualization Typical large-scale visualization Begins with data Which goes through an analysis pipeline Ends with (interactive) graphics The pipeline Calculations, derived variables Direct data reductions: thresholds, isosurfaces Finding the region of interest Graphics Interactions: rotations, panning, zooming Indirect data reductions Setting up the camera

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23 The Problem with Current Reduction Split Direct data reductions are all we care about Finding a region of interest is the hard problem, the big data problem The assumption: the region of interest is trivially viewable Not true anymore even for small-scale features

24 The New Research Question How do we view at resolution? Direct Solutions Throw on big, expensive display Resolve yourself to look at several resolution chunks

25 Resolution Chunks VR Headset! Rendering in 360 degrees Looking around determines which chunk to look at (and all indirect reduction is removed from rendering)

26 The Point + Some Bad News Interactivity may make a comeback, but Was never distanced from visualization, just not expected For HPC visualization 1 frame per second is hopeful (10+ FPS is happy dance time) Top quality rendering for HPC data doesn t have as natural an alignment with future HPC visualization Is nonetheless critical

27 DAV Team s SC Showcase of Tornado Data ON VALUE OF HIGH QUALITY

28 Non- Photorealistic vs. Photorealistic Renderings

29 Unsurprisingly For data with naturally physical representations, high quality and/or photorealistic rendering is preferred This represents a large portion of generated simulation data for which we could be doing better, and Is completely underrepresented in the literature

30 Moving in the Right Direction NVIDIA INDEX + PARAVIEW

31 NVIDIA IndeX: In Situ & At Scale Rendering Leverages GPU-clusters for scalable large-scale data visualization Is a GPU-cluster aware solution for interactive visual computing Is a commercial software solution available and already deployed by customers for In-Situ visualization for large-scale data

32 Features Supports 8-bit, 16-bit, float & RGBA data types Supports unstructured tetrahedral cells Depth correct transparent geometry such as heightfields and triangle meshes and volumetric data In Situ operation Asynchronous streaming of time varying data Over 1 TB of data rendered at 20+ fps

33 Integration with ParaView Cluster Domain decomposition is done by ParaView Affinity information is supplied to NVIDIA IndeX NVIDIA IndeX adapts to ParaView s domain decomposition [..] [..]

34 Implementation Interface with vtkvolumemapper & vtkimagevolumerepresentation MPI infrastructure is implemented in the plugin ParaView Variety of readers, mature computa5onal pipeline ParaView Plugin NVIDIA IndeX Scalable, high quality rendering

35 AND NOW FOR SOMETHING COMPLETELY RELATED