TeraGrid 2010 Keynote: The Physics of Black Holes with Cactus

By Michael Schneider

August 11, 2010

Opening a new window on the universe — that’s the promise of gravitational wave astronomy, and its fulfillment presents a scientific computing challenge that might almost be akin to pulling light out of a black hole, if that were possible. Or maybe the more appropriate analogy is water in a desert, where sometimes the solution is cactus.
Gabrielle AllenMake that a capital “C” — as in Cactus, an open, collaborative software framework for numerical relativity that since 1997 has enabled research that underlies more than 200 scientific papers and 30 student theses. That and more than that, in a fast-moving, information-packed presentation, was the topic of Gabrielle Allen’s keynote talk, Tuesday, August 3 at TeraGrid ’10, the fourth annual conference of the TeraGrid, in Pittsburgh, Pa.

Allen is associate professor in Computer Science at Louisiana State University, and a faculty member at LSU’s Center for Computation & Technology. Before moving there in 2003, she led the computer science area of the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) in Potsdam, Germany. At AEI, she was a PI for the European GridLab project, and led the initial development of Cactus.

She began her talk, “Cyberinfrastructure for Numerical Relativity,” by noting that she’s been a TeraGrid user since 2001. “Accurately modeling astrophysical systems that are governed by Einstein’s Equations of General Relativity, such as black holes, stellar core collapse or gamma ray bursts,” she added, “requires the use of cutting-edge computational resources and software.”

Solving the problems of this field of science, Gravitational Wave Physics, depends on interactions between modern theory, observation and computation, and all three aspects, says Allen, are leading to new discoveries. Gravitational waves are one of the startling aspects of Einstein’s predictions from general relativity. Measurements of the decaying orbits of binary pulsars agree with Einstein’s prediction of gravity waves, yet even now these waves haven’t been directly observed.

Two large projects have mounted gravity wave detectors — the U.S. LIGO (Laser Interferometer Gravitational Wave Observatory) project and GEO 600 in Germany — to test Einstein’s theory, but these extremely sensitive instruments need to be precisely tuned and use complex data analysis to recognize the delicate signatures of gravity waves from events in deep space. For this the physicists need numerical simulations.

The numerical problem is finding ways to solve the Einstein equations that govern gravity-wave phenomena. “There are thousands of terms on the right-hand side,” says Allen, “and these equations are very difficult to work with.” The initial challenge has been modeling binary black holes — two black holes in orbit around each other — a relatively “simple” system with relatively few parameters, as a test case for LIGO.

Recent work using TeraGrid resources at multiple sites, a project of an international team that included Allen’s LSU colleague Erik Schnetter, a research professor in the Department of Physics and Astronomy, modeled the binary black hole problem with unprecedented detail. Allen describes this work, featured on the cover of the 2009 TeraGrid Science Highlights publication, as an outcome of what has so far been a 40-year plus effort to model gravity waves from binary black holes, which has only now arrived at numerically generated waveforms. “We still can’t do extreme mass ratios or very fast spins,” she says, “but this has opened the door to modeling more complex scenarios, such as general relativistic hydrodynamics.”

Allen went on to describe the essential elements of cyberinfrastructure needed to move this work forward, and elaborated on the Cactus framework — so-called from its design of a central core (“flesh”) which connects to application modules (“thorns”) through an extensible interface. It’s a modular system, with thorns that are defined by parameters, variables and methods, and the flesh binds it together.

Cactus derived originally, Allen explained, from a mid-90s Black Hole Grand Challenge project, with multiple groups collaborating. “This came out of the vision of Ed Seidel,” she said. Seidel, who recently ended a term as director of NSF’s Office of Cyberinfrastructure, worked during this period at AEI in Germany, and recognized needs — that have been implemented through Cactus — for modularity, for easy code reuse, community sharing and development.

A recent set of Cactus thorns, Allen pointed out, has implemented adaptive mesh-refinement (AMR). Developed by Schnetter, this has allowed many groups to have access to AMR with little code change. “We can scale the AMR up to around 16,000 processors,” noted Allen. Cactus also implements automatic code generation through “Kranc” — a Mathematica tool to generate Cactus thorns from PDEs. “Your turn the Kranc and it spits out complete thorns of Cactus.”

Cactus interfaces with the Einstein Toolkit, a consortium that develops and supports open software for relativistic astrophysics. “Our aim,” said Allen, “is to provide the core computational tools than can enable new science, broaden our community, facilitate interdisciplinary research and take advantage of emerging petascale computers and advanced cyberinfrastructure.” The consortium includes 55 members at 17 sites in nine countries.

Among many challenges to be faced, Allen observed that changes in academic culture are needed to support the model of open collaboration versus competition among research teams. “We need incentives for faculty to encourage postdocs and students to use and contribute to community software.”

“Everything is a challenge,” she added, “in this kind of work. Nothing works as well as you’d like. The TeraGrid has been a big friend of numerical relativity, and has helped us to develop the kind of community we need — especially for students, it has been amazingly helpful. It provides access for students to the hardware we use, and the software and best practices. All these things are crucial.”

The biggest challenge ahead, she added, is how to handle tremendous amounts of data. “Everything is going to be about data very soon. We need to be ready for that. It is changing the world of science. There is a whole sociology of how data is going to be used in academia. We have a big chance to do this properly.”

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!

TACC Helps ROSIE Bioscience Gateway Expand its Impact

April 26, 2017

Biomolecule structure prediction has long been challenging not least because the relevant software and workflows often require high-end HPC systems that many bioscience researchers lack easy access to. 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

IBM, Nvidia, Stone Ridge Claim Gas & Oil Simulation Record

April 25, 2017

IBM, Nvidia, and Stone Ridge Technology today reported setting the performance record for a “billion cell” oil and gas reservoir simulation. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Remote Visualization Optimizing Life Sciences Operations and Care Delivery

As patients continually demand a better quality of care and increasingly complex workloads challenge healthcare organizations to innovate, investing in the right technologies is key to ensuring growth and success. Read more…

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 a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

Musk’s Latest Startup Eyes Brain-Computer Links

April 21, 2017

Elon Musk, the auto and space entrepreneur and severe critic of artificial intelligence, is forming a new venture that reportedly will seek to develop an interface between the human brain and computers. Read more…

By George Leopold

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

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

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

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). 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 a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Hyperion (IDC) Paints a Bullish Picture of HPC Future

April 20, 2017

Hyperion Research – formerly IDC’s HPC group – yesterday painted a fascinating and complicated portrait of the HPC community’s health and prospects at the HPC User Forum held in Albuquerque, NM. HPC sales are up and growing ($22 billion, all HPC segments, 2016). Read more…

By John Russell

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" processors. The infrastructure is based on the 68-core Intel Knights Landing processor with integrated Omni-Path fabric (the 7250F Xeon Phi). Read more…

By Tiffany Trader

CERN openlab Explores New CPU/FPGA Processing Solutions

April 14, 2017

Through a CERN openlab project known as the ‘High-Throughput Computing Collaboration,’ researchers are investigating the use of various Intel technologies in data filtering and data acquisition systems. Read more…

By Linda Barney

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

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 phase of neural networks (NN). Read more…

By Tiffany Trader

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. 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 campaign. 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 assets. Read more…

By Tiffany Trader

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

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

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Leading Solution Providers

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

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

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

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