Building the Universe Pixel by Pixel

By Kelen Tuttle

August 20, 2014

Recently, the Harvard-Smithsonian Center for Astrophysics unveiled an unprecedented simulation of the universe’s development. Called the Illustris project, the simulation depicts more than 13 billion years of cosmic evolution across a cube of the universe that’s 350-million-light-years on each side. The goal was to view the formation of galaxies and other large-scale structure we see around us today, to test our understanding of what makes up the universe – including dark matter and dark energy – as well as how those components interact. It was a massive undertaking, one that took more than 5 years to complete. But why was it important to conduct such a simulation?

To better understand the science and art of astrophysics visualizations, three experts came together in late July to discuss the processes the ways in which their work benefits both science and the public’s perception of science. The participants:

RALF KAEHLER – is a physicist and computer scientist by training who now runs the visualization facilities at the Kavli Institute for Particle Astrophysics and Cosmology, located at SLAC National Accelerator Laboratory and Stanford University.

STUART LEVY – is a research programmer and member of the National Center for Supercomputing Applications’ Advanced Visualization Lab team, which creates high-resolution data-driven scientific visualizations for public outreach.

DYLAN NELSON – is a graduate student at the Harvard-Smithsonian Center for Astrophysics and a member of the Illustris collaboration, which recently completed a large cosmological simulation of galaxy formation.

The following is an edited transcript of a roundtable discussion. The participants have been provided the opportunity to amend or edit their remarks.

THE KAVLI FOUNDATION: Dylan, youre a member of the Illustris project team, so let’s start with you. Illustris was a massive undertaking, one that took more than 5 years to complete. Why was it important to conduct this simulation?

DYLAN NELSON: This simulation tested our big-picture understanding of the universe’s evolution. We can’t just compare the observation of one galaxy; we need to compare whole populations of thousands or tens of thousands of galaxies, and the simulation lets us do this by creating a big volume of the universe. In visualizing this simulation, we found some unexpected features that in retrospect really shouldn’t have been unexpected at all. For example, when we made a movie showing the temperature of gas in the universe evolving over time, we saw that galaxies had a tendency to flicker rapidly. We traced this back to one of the three ways in which we let supermassive black holes input energy into the galaxies within which they reside. Although we expected that the energy would affect the temperature of the gas, we didn’t know how intermittent it would be, how it would create these flickers and bursts. That’s really something that we were surprised by, and something that made us rework our models a bit. 

TKF: Ralf, what types of insights are gained through the visualizations you create with scientists at the Kavli Institute for Particle Astrophysics and Cosmology? Do you also tend to find unexpected features?

RALF KAEHLER: What I often hear from scientists is that they gain intuition from watching the animations we create, intuition that’s hard to get from just looking at the raw numbers. They see how gas moves, how dark matter clumps on smaller scales and then merges and forms larger and larger clumps of dark matter. And it seems like this intuition is very important for a thorough understanding of the processes.

Another very important advantage visualizations offer is the ability to catch errors in the simulations. By just looking at the numbers, it can be easy to miss these errors. But when watching an animation they can become totally obvious. We can easily see if there’s some discontinuity in the data that shouldn’t be there and then you can investigate further if it’s a feature or an artifact or a bug. The software used to produce these simulations usually consists of hundred thousands or millions of lines of code. Codes of that size often contain bugs and visualizations can help to determine if there’s an error hidden within the code.

TKF: It sounds like visualizations are especially good for identifying issues with your assumptions or the underlying models. Stuart, would you agree with that?

STUART LEVY: I think that’s a really good point, and it’s something that people talk about when they’re thinking of doing a visualization. If you reduce things to a graph with some statistics on it, in choosing what the statistics should measure, you’re saying what the interesting things are. And the hope is that if you can present something visually, you might end up bringing in things that you didn’t expect to bring in.

To me, it also seems like visualizations are becoming more and more useful for looking at very large-scale phenomena. As in observational astrophysics, instead of spending a lot of time looking at modest numbers of individual objects, people are looking at huge numbers of objects.

DYLAN NELSON: I agree with that. Large simulations like Illustris are similar to big observational surveys. When you’re not looking at individual objects, you need sophisticated visualization techniques to pull out the interesting information. For instance, back when the kind of cosmological simulations we do today first started, people plotted a point for each dark matter particle. They learned lots of science from doing that. But these days, when the biggest dark matter simulations include a trillion particles, that’s not going to get you as far. You’re going to need more sophisticated visualization approaches – as well as machine learning techniques or other automated ways of finding interesting trends in the simulation. We’re working on that.

TKF: Even though all three of you create visualizations, your roles and your connections to the scientific questions driving the research are different. How does the process work for each of you? Who comes up with the scientific questions you seek to answer?

RALF KAEHLER:

For us, it’s often an interactive process. We sit together in front of the screen and analyze the data in real-time. I try to design a lot of the algorithms in a way that they produce visualizations pretty quickly, so that we can change parameters like the camera position or the color maps in real time and get an updated image in a fraction of a second. That way, we can explore the data together, focusing on regions of interest, zooming in and out, things like that. Other times, it’s more offline, where I’ll render something overnight and send the result to the scientists and let them have a look at that.

I would also say that while half of my work is for scientists, the other half is for outreach. Sometimes we create visualizations purely for outreach purposes, and sometimes we can use the same visualization for both science and outreach. In the latter case, the scientists first analyze the dataset and then we tweak it a little bit, spending more time with the camera path and the color scheme to make it look a little bit prettier before we use it for planetarium shows.

DYLAN NELSON: The process for me is a little different because my primary responsibility is science. It’s only a secondary responsibility that lets me create visualizations. I always say that when I create visualizations, it’s both for scientific exploration and for dissemination to the public. But I think in reality, in my research group we do those two things in completely different ways.

We need visualizations to understand what is going on in a simulation, to better understand our models and the physical processes we’re simulating. But those visualizations are not pretty; we do them as quickly as possible, and as soon as we have a useful science result, the effort on the visualization stops. On the other hand, when we’re doing a visualization for outreach, that’s really intended to make people say “Oh, wow, that’s really cool!” So there’s a lot more time spent past the point of scientific realization, polishing and making the visualization look visually impressive. 

STUART LEVY: My group really focuses on outreach. So we usually have an idea for a show first, then we’ll go and look for scientists who work in that area and can provide the simulation. They’ll also tell us what we should believe from their simulations and what we shouldn’t believe. Often they’ll be making simulations they know are representing some aspects of reality well and others less well. And so they’ll say something like, don’t pay attention to the temperature here, since we’re not including everything that could be heating things up. We’ll go back and forth both with the scientists and the people producing the show to create something that’s both interesting and scientifically correct.

That said, we do occasionally work with scientists on unanswered questions – though it’s not always in the realm of astrophysics. A few years ago, we were working with a simulation of a tornado. One of the things that the scientists were interested in learning was the origin of tornados. Most severe storms don’t create tornados, so what’s special about the subset of storms that do? They had an idea that we should be looking in the simulation for a feature that’s called a rear flank downdraft – a storm that’s flowing in a certain way. We were looking for this signature in the visualizations and just not finding it. But then one of the graduate students picked out this sort of rolling feature – a horizontal bunch of rolling air – and succeeded in convincing his senior professors that, in this simulation at least, it was that feature and not a rear flank downdraft that triggered the tornado. That was a surprising result, one made possible by the visualization.

TKF: What have been the big breakthroughs in visualization in the past five years? Are there new technologies or revelations that make possible all youve just described? 

STUART LEVY: Bigger disks! It seems a little mundane, but the ability to store huge amounts of data is really important. A couple of years ago, we got about four terabytes of data from a scientist. A few years earlier, that would have been an overwhelming amount, but today we could easily take on several of those. That makes a really big difference. The billion-dollar gaming industry has also been an incredible boon to us. It’s on the back of that industry that high performance graphics cards have been built. Fifteen years ago, the fastest graphics hardware cost the price of a house. In just a few years, that was superseded by hardware that you could get for a couple of thousand dollars. Now it’s come down to a few hundred dollars, and we’re able to use it routinely. If not for the gaming industry, we wouldn’t have all of the graphics processor power that we need.

RALF KAEHLER: I completely agree with Stuart here. The ever-evolving capabilities of graphics hardware are very important for this work. You can now realize interactive visualizations of datasets that were far out of the reach of standard desktop workstations five or ten years ago.

TKF: With all of that computing power, how much of the process is science, and how much of it is art? If the three of you were to visualize the same event, would you end up with similar results?

RALF KAEHLER: I would say that there’s a lot of creativity involved in the process. It might be comparable to taking a photograph of some object. You have all of this freedom of how to choose your camera position, lighting conditions, color filters and so on. Similarly, with the same numerical simulation, you can end up with millions of different images by changing around these variables. So it really depends on the audience you’re targeting, what features in the original dataset you want to highlight, and what story you want to get across. If different people work on visualizations for the same dataset, the results can be totally different.

One of our most recent visualizations was a collaboration with the Hayden Planetarium at the American Museum of Natural History in New York. It shows the role of dark matter in forming the larger structures in the universe. For this audience, we took the term dark matter a bit more literally than usual. If we had done this rendering for scientists, we would have represented higher dark matter densities in brighter colors. But studies have shown that often confuses the general public. So in this visualization, we actually turned it around and rendered the dark matter in darker colors and added some background light. That helped guide the audience and clarified what was dark matter and what was not.

STUART LEVY: I agree. I think we should look at visualization like mapmakers look at map making. A good mapmaker will be deliberate in what gets included in the map, but also in what gets left out. Visualizers think about their audience, as Ralf says, and the specific story they want to tell. And so even with the same audience in mind, you might set up the visualization very differently to tell different stories. For example, for one story you might want to show only what it’s possible for the human eye to see, and in others you might want to show the presence of something that wouldn’t be visible in any sort of radiation at all. That can help to get a point across.

TKF: It sounds like theres quite a bit of room for artistic choice. For outreach purposes, then, why is it important for the visualizations to be based on scientifically accurate data? Why are you creating them, rather than a movie house?

RALF KAEHLER: Using sophisticated numerical simulations ensures that the science is depicted correctly. Besides this, it’s hard to model a lot of the phenomena in astrophysics using the artistic tools that Hollywood movies employ. The phenomena are just too complex to draw by hand. I think more and more of these artistic tools are now starting to incorporate some sort of simplified simulation codes in order to model things like explosions, to make it look more realistic.

TKF: When you’ve created visualizations for a public outlet, have you ever sat in the audience and watched the public’s reaction? What’s that like?

DYLAN NELSON: It’s kind of amazing, to be honest, the amount of press and public interest that’s come out of the Illustris project. Actually, just yesterday I got a call from my father, who had been browsing the news on his phone and saw an image from Illustris on the front page of The New York Times website. This was a still image that we made just for the purposes of putting it on the website, and it’s probably appeared in a dozen newspapers so far. It’s great that there’s so much interest, and that the images are becoming almost iconic.

STUART LEVY: For me, it’s great to watch visualizations in planetarium domes. It’s the most wonderful thing to lie down in the middle of a planetarium – or even in an IMAX theater – and just look up. Having the audience completely surrounded by what they’re seeing can be really breathtaking.

RALF KAEHLER: I love it when visualizations are shown in planetariums, too. It just looks so impressive – much more impressive than looking at the visualizations on a flat monitor in my office. I’ve worked on visualizations that were shown in places like the American Museum of Natural History and the Morrison Planetarium at the California Academy of Sciences. These are great places to reach a lot of people in a nice, inspiring environment. Even though when I’m sitting in the audience it’s too dark to gauge other people’s reactions, sometimes we get emails from people who saw the planetarium shows and write how much they liked it. It’s really motivating, and shows that our time is being well invested.

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!

SC Bids Farewell to Denver, Heads to Dallas for 30th

November 17, 2017

After a jam-packed four-day expo and intensive six-day technical program, SC17 has wrapped up another successful event that brought together nearly 13,000 visitors to the Colorado Convention Center in Denver for the larg Read more…

By Tiffany Trader

SC17 Keynote – HPC Powers SKA Efforts to Peer Deep into the Cosmos

November 17, 2017

This week’s SC17 keynote – Life, the Universe and Computing: The Story of the SKA Telescope – was a powerful pitch for the potential of Big Science projects that also showcased the foundational role of high performance computing in modern science. It was also visually stunning. Read more…

By John Russell

How Cities Use HPC at the Edge to Get Smarter

November 17, 2017

Cities are sensoring up, collecting vast troves of data that they’re running through predictive models and using the insights to solve problems that, in some cases, city managers didn’t even know existed. Speaking Read more…

By Doug Black

HPE Extreme Performance Solutions

Harness Scalable Petabyte Storage with HPE Apollo 4510 and HPE StoreEver

As a growing number of connected devices challenges IT departments to rapidly collect, manage, and store troves of data, organizations must adopt a new generation of IT to help them operate quickly and intelligently. Read more…

SC17 Student Cluster Competition Configurations: Fewer Nodes, Way More Accelerators

November 16, 2017

The final configurations for each of the SC17 “Donnybrook in Denver” Student Cluster Competition have been released. Fortunately, each team received their equipment shipments on time and undamaged, so the teams are r Read more…

By Dan Olds

SC Bids Farewell to Denver, Heads to Dallas for 30th

November 17, 2017

After a jam-packed four-day expo and intensive six-day technical program, SC17 has wrapped up another successful event that brought together nearly 13,000 visit Read more…

By Tiffany Trader

SC17 Keynote – HPC Powers SKA Efforts to Peer Deep into the Cosmos

November 17, 2017

This week’s SC17 keynote – Life, the Universe and Computing: The Story of the SKA Telescope – was a powerful pitch for the potential of Big Science projects that also showcased the foundational role of high performance computing in modern science. It was also visually stunning. Read more…

By John Russell

How Cities Use HPC at the Edge to Get Smarter

November 17, 2017

Cities are sensoring up, collecting vast troves of data that they’re running through predictive models and using the insights to solve problems that, in some Read more…

By Doug Black

Student Cluster LINPACK Record Shattered! More LINs Packed Than Ever before!

November 16, 2017

Nanyang Technological University, the pride of Singapore, utterly destroyed the Student Cluster Competition LINPACK record by posting a score of 51.77 TFlop/s a Read more…

By Dan Olds

Hyperion Market Update: ‘Decent’ Growth Led by HPE; AI Transparency a Risk Issue

November 15, 2017

The HPC market update from Hyperion Research (formerly IDC) at the annual SC conference is a business and social “must,” and this year’s presentation at S Read more…

By Doug Black

Nvidia Focuses Its Cloud Containers on HPC Applications

November 14, 2017

Having migrated its top-of-the-line datacenter GPU to the largest cloud vendors, Nvidia is touting its Volta architecture for a range of scientific computing ta Read more…

By George Leopold

HPE Launches ARM-based Apollo System for HPC, AI

November 14, 2017

HPE doubled down on its memory-driven computing vision while expanding its processor portfolio with the announcement yesterday of the company’s first ARM-base Read more…

By Doug Black

OpenACC Shines in Global Climate/Weather Codes

November 14, 2017

OpenACC, the directive-based parallel programming model used mostly for porting codes to GPUs for use on heterogeneous systems, came to SC17 touting impressive Read more…

By John Russell

US Coalesces Plans for First Exascale Supercomputer: Aurora in 2021

September 27, 2017

At the Advanced Scientific Computing Advisory Committee (ASCAC) meeting, in Arlington, Va., yesterday (Sept. 26), it was revealed that the "Aurora" supercompute Read more…

By Tiffany Trader

NERSC Scales Scientific Deep Learning to 15 Petaflops

August 28, 2017

A collaborative effort between Intel, NERSC and Stanford has delivered the first 15-petaflops deep learning software running on HPC platforms and is, according Read more…

By Rob Farber

Oracle Layoffs Reportedly Hit SPARC and Solaris Hard

September 7, 2017

Oracle’s latest layoffs have many wondering if this is the end of the line for the SPARC processor and Solaris OS development. As reported by multiple sources Read more…

By John Russell

AMD Showcases Growing Portfolio of EPYC and Radeon-based Systems at SC17

November 13, 2017

AMD’s charge back into HPC and the datacenter is on full display at SC17. Having launched the EPYC processor line in June along with its MI25 GPU the focus he Read more…

By John Russell

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

Google Releases Deeplearn.js to Further Democratize Machine Learning

August 17, 2017

Spreading the use of machine learning tools is one of the goals of Google’s PAIR (People + AI Research) initiative, which was introduced in early July. Last w Read more…

By John Russell

GlobalFoundries Puts Wind in AMD’s Sails with 12nm FinFET

September 24, 2017

From its annual tech conference last week (Sept. 20), where GlobalFoundries welcomed more than 600 semiconductor professionals (reaching the Santa Clara venue Read more…

By Tiffany Trader

Amazon Debuts New AMD-based GPU Instances for Graphics Acceleration

September 12, 2017

Last week Amazon Web Services (AWS) streaming service, AppStream 2.0, introduced a new GPU instance called Graphics Design intended to accelerate graphics. The Read more…

By John Russell

Leading Solution Providers

EU Funds 20 Million Euro ARM+FPGA Exascale Project

September 7, 2017

At the Barcelona Supercomputer Centre on Wednesday (Sept. 6), 16 partners gathered to launch the EuroEXA project, which invests €20 million over three-and-a-half years into exascale-focused research and development. Led by the Horizon 2020 program, EuroEXA picks up the banner of a triad of partner projects — ExaNeSt, EcoScale and ExaNoDe — building on their work... Read more…

By Tiffany Trader

Delays, Smoke, Records & Markets – A Candid Conversation with Cray CEO Peter Ungaro

October 5, 2017

Earlier this month, Tom Tabor, publisher of HPCwire and I had a very personal conversation with Cray CEO Peter Ungaro. Cray has been on something of a Cinderell Read more…

By Tiffany Trader & Tom Tabor

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

Cray Moves to Acquire the Seagate ClusterStor Line

July 28, 2017

This week Cray announced that it is picking up Seagate's ClusterStor HPC storage array business for an undisclosed sum. "In short we're effectively transitioning the bulk of the ClusterStor product line to Cray," said CEO Peter Ungaro. Read more…

By Tiffany Trader

Intel Launches Software Tools to Ease FPGA Programming

September 5, 2017

Field Programmable Gate Arrays (FPGAs) have a reputation for being difficult to program, requiring expertise in specialty languages, like Verilog or VHDL. Easin Read more…

By Tiffany Trader

HPC Chips – A Veritable Smorgasbord?

October 10, 2017

For the first time since AMD's ill-fated launch of Bulldozer the answer to the question, 'Which CPU will be in my next HPC system?' doesn't have to be 'Whichever variety of Intel Xeon E5 they are selling when we procure'. Read more…

By Dairsie Latimer

IBM Advances Web-based Quantum Programming

September 5, 2017

IBM Research is pairing its Jupyter-based Data Science Experience notebook environment with its cloud-based quantum computer, IBM Q, in hopes of encouraging a new class of entrepreneurial user to solve intractable problems that even exceed the capabilities of the best AI systems. Read more…

By Alex Woodie

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

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