TACC Software Helps Researchers Visualize Science With Greater Fidelity

August 13, 2015

Aug. 13 — When scientists run experiments–whether physically smashing atoms at the Large Hadron Collider or virtually simulating future weather–the output is often a huge set of numbers incomprehensible to the ordinary human brain.

To tame the data and put it into a form that our minds can understand, researchers use scientific visualization.

In its simplest form, scientific visualization can be a graph or chart. But in cases where researchers need detailed information to draw insights–say to understand how a protein functions in cancer and to design a drug to combat it–scientific visualization can be quite complex.

The science of scientific visualization is quite complex, too. To give structure, order, color and form to multi-dimensional data requires powerful software. And as datasets grow, scientific visualization increasingly requires advanced computing resources as well.

This is something that Paul Navratil at the Texas Advanced Computing Center (TACC) knows well. As manager of the Scalable Visualization Technologies group, he’s worked for the past decade as part of a team that helps scientists visualize the data that comes off on some of the most powerful supercomputers in the world.

Modeling hurricanes in real-time as they barrel toward the Gulf Coast; assisting cardiac researchers to simulate blood flow through a congested heart; visualizing the formation of galaxies during the Dark Ages of the universe–Navratil has seen it all.

He’s also watched the landscape of scientific visualization change in response to evolving trends in hardware and software.

In fact, Navratil is actively involved in upending the supremacy of rasterization, a method that takes vector graphics and converts them into pixels, or dots, for display, printing or storage.

Vector graphics use points, lines, curves, and polygons–all based on mathematical expressions–to represent images in computer graphics. But modern printers and displays need that information converted to dots in order to use it. Rasterization has been the dominant conversion technique, but Navratil and others are advancing “ray tracing,” an alternative visualization method. Ray tracing has a history as long as rasterization’s, and recently become advantageous, thanks to new hardware and methods.

With support from the National Science Foundation (NSF), Navratil is leading an effort to design a new framework that would allow the tens of thousands of scientists and engineers who use the nation’s supercomputers to easily add ray tracing visualizations to their research, regardless of the type of computing system or hardware they are using.

Rasterization Vs. Ray Tracing

Whereas rasterization works by projecting a flat surface onto the 3-D model of an object, scene or person, ray tracing simulates the photons of light as they bounce from a light source off an object and into our eyes, based on the laws of optics.

This physically realistic rendering has a number of benefits. It creates much more realistic reflections and shading, which helps our minds understand the spatial relationships between the parts of the visualization. And since the objects being rendered are described computationally, according to their specific material properties and shape, they are also much more scientifically accurate.

Navratil uses the metaphor of a Wild West movie set to describe the difference.

“Rasterization looks realistic from the outside, but you can’t explore beyond the surface,” he explained. Ray tracing on the other hand is like a real street in a Western ghost town. “You can walk into the saloon and sit down at the bar.”

This may not be an important distinction for some applications, but for scientists trying to understand the deep mysteries of the universe, precise information is required.

New Hardware Architecture Enables New Capabilities 

Computer processing speeds were once the bottleneck preventing individuals from using ray tracing routinely in their research. But as microprocessors have become faster, memory access and communication are now the primary obstacles.

The new software Navratil and his collaborators developed, GraviT (pronounced “gravity”), automatically recognizes the type of problem a researcher is working on and the configuration of the system he or she is using, and then appropriately distributes data from the simulation to multiple computer processors–potentially thousands of them–for visualization.

The process requires little knowledge or understanding of visualization by the researchers, so they can focus their efforts on their specific science questions and not the science of software engineering.

The project is a collaboration among computer and computational scientists at TACC, the University of Oregon, the University of Utah, Intel Corporation and ParaView, a company that designs leading scientific visualization software. Hank Childs (Oregon) and Charles Hanson (Utah) serve as co-principal investigators for the project.

“Different ways of visualizing data have come and gone over the years based on the underlying hardware,” said Daniel S. Katz, a program director at NSF. “Software based ray-tracing is now viable again. To bring it into the future, so it works on current and future hardware, we need sustainable software. This work can be incorporated into different visualization packages and into the community of visualization tools.”

In designing the software, the research team looked ahead to a time in the near future when scientists working on supercomputers in the cloud will be creating simulations so big that they can’t easily be moved for rendering. (This is already the case with many of the researchers who use the nation’s supercomputers and many believe it will be the norm in the future.)

Such simulations will require visualizing the data locally, even as the simulation is running–a process known as “in-situ visualization.”

In this scenario, simulation data is never written to disk and stored. Simulations are simply visualized as the data is processed. This idea breaks the age-old paradigm of separating modeling from visualization, which was typically done afterward as a post-processing step.

In Spring 2015, the researchers released the first component of the system, called GluRay, as an open source tool on GitHub. GluRay lets researchers visualize their research on distributed computers, regardless of the type of hardware or architecture the computer uses.

The team plans to release the beta version of GraviT in the Fall. GraviT extends GluRay by scheduling work across multiple nodes of a supercomputer, particularly when the total data is larger than available memory. GraviT also provides an advanced interface for application developers who want to use more ray tracing capabilities and improve their performance.

Helping Scientists Across Disciplines

Working with test problems from teams of researchers in diverse fields, Navratil and company have already seen great gains using ray tracing on high-performance computers, facilitated by GluRay.

Geologists using the software to explore how water flows through limestone karsts in Florida experienced improved depth perception in their visualizations and consequently a better understanding of how the aquifer is recharged through thumb-sized holes in the limestone. Other researchers have used the software for astrophysics simulations and seismic analysis.

Beyond the improved visual fidelity that GraviT will provide, there’s another reason that Navratil and his team believe their research will prove useful to science. It turns out that many phenomena that scientists study look a lot like ray tracing.

“Whether it’s fluid flow or stellar magnetism, these problems involve tracing particles,” Navratil said. “For all of these problems, the solutions we’re developing will be a big help.”

Source: Aaron Dubrow, TACC

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

DoE Awards 24 ASCR Leadership Computing Challenge (ALCC) Projects

June 28, 2017

On Monday, the U.S. Department of Energy’s (DOE’s) ASCR Leadership Computing Challenge (ALCC) program awarded 24 projects a total of 2.1 billion core-hours at the Argonne Leadership Computing Facility (ALCF). The o Read more…

By HPCwire Staff

STEM-Trekker Badisa Mosesane Attends CERN Summer Student Program

June 27, 2017

Badisa Mosesane, an undergraduate scholar who studies computer science at the University of Botswana in Gaborone, recently joined other students from developing nations around the world in Geneva, Switzerland to particip Read more…

By Elizabeth Leake, STEM-Trek

The EU Human Brain Project Reboots but Supercomputing Still Needed

June 26, 2017

The often contentious, EU-funded Human Brain Project whose initial aim was fixed firmly on full-brain simulation is now in the midst of a reboot targeting a more modest goal – development of informatics tools and data/ Read more…

By John Russell

DOE Launches Chicago Quantum Exchange

June 26, 2017

While many of us were preoccupied with ISC 2017 last week, the launch of the Chicago Quantum Exchange went largely unnoticed. So what is such a thing? It is a Department of Energy sponsored collaboration between the Univ Read more…

By John Russell

HPE Extreme Performance Solutions

Creating a Roadmap for HPC Innovation at ISC 2017

In an era where technological advancements are driving innovation to every sector, and powering major economic and scientific breakthroughs, high performance computing (HPC) is crucial to tackle the challenges of today and tomorrow. Read more…

UMass Dartmouth Reports on HPC Day 2017 Activities

June 26, 2017

UMass Dartmouth's Center for Scientific Computing & Visualization Research (CSCVR) organized and hosted the third annual "HPC Day 2017" on May 25th. This annual event showcases on-going scientific research in Massach Read more…

By Gaurav Khanna

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “pre-exascale” award), parsed out additional information ab Read more…

By Tiffany Trader

Tsinghua Crowned Eight-Time Student Cluster Champions at ISC

June 22, 2017

Always a hard-fought competition, the Student Cluster Competition awards were announced Wednesday, June 21, at the ISC High Performance Conference 2017. Amid whoops and hollers from the crowd, Thomas Sterling presented t Read more…

By Kim McMahon

GPUs, Power9, Figure Prominently in IBM’s Bet on Weather Forecasting

June 22, 2017

IBM jumped into the weather forecasting business roughly a year and a half ago by purchasing The Weather Company. This week at ISC 2017, Big Blue rolled out plans to push deeper into climate science and develop more gran Read more…

By John Russell

DoE Awards 24 ASCR Leadership Computing Challenge (ALCC) Projects

June 28, 2017

On Monday, the U.S. Department of Energy’s (DOE’s) ASCR Leadership Computing Challenge (ALCC) program awarded 24 projects a total of 2.1 billion core-hour Read more…

By HPCwire Staff

DOE Launches Chicago Quantum Exchange

June 26, 2017

While many of us were preoccupied with ISC 2017 last week, the launch of the Chicago Quantum Exchange went largely unnoticed. So what is such a thing? It is a D Read more…

By John Russell

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Tsinghua Crowned Eight-Time Student Cluster Champions at ISC

June 22, 2017

Always a hard-fought competition, the Student Cluster Competition awards were announced Wednesday, June 21, at the ISC High Performance Conference 2017. Amid wh Read more…

By Kim McMahon

GPUs, Power9, Figure Prominently in IBM’s Bet on Weather Forecasting

June 22, 2017

IBM jumped into the weather forecasting business roughly a year and a half ago by purchasing The Weather Company. This week at ISC 2017, Big Blue rolled out pla Read more…

By John Russell

Intersect 360 at ISC: HPC Industry at $44B by 2021

June 22, 2017

The care, feeding and sustained growth of the HPC industry increasingly is in the hands of the commercial market sector – in particular, it’s the hyperscale Read more…

By Doug Black

At ISC – Goh on Go: Humans Can’t Scale, the Data-Centric Learning Machine Can

June 22, 2017

I've seen the future this week at ISC, it’s on display in prototype or Powerpoint form, and it’s going to dumbfound you. The future is an AI neural network Read more…

By Doug Black

Cray Brings AI and HPC Together on Flagship Supers

June 20, 2017

Cray took one more step toward the convergence of big data and high performance computing (HPC) today when it announced that it’s adding a full suite of big d Read more…

By Alex Woodie

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

Leading Solution Providers

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of “quantum supremacy,” researchers are stretching the limits of today’s most advanced supercomputers. Read more…

By Tiffany Trader

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Share This