Neutron Science and Supercomputing Come Together at Oak Ridge National Lab

By Agatha Bardoel

December 4, 2012

Novel capability will deliver the best of high-performance computing and cloud computing

Next-generation neutron scattering requires next-generation data analysis infrastructure. And that means not just more data, accelerated reduction, and translation and analysis, but linking the neutron scattering on a beam line live to a simulation platform where modeling and simulation can guide the experiment.

As the data sets generated by the increasingly powerful neutron scattering instruments at Oak Ridge National Laboratory’s (ORNL’s) Spallation Neutron Source (SNS) grow ever more massive, the facility’s users require significant advances in data reduction and analysis tools so they can cope. SNS is the world’s most intense pulsed, accelerator-based neutron source for scientific research and development.

Funded by the US Department of Energy Office of Basic Energy Sciences, this national user facility hosts hundreds of scientists from all over the world every year, most of whom are engaged in materials science research. Now the SNS data specialists have teamed with ORNL’s Computing and Computational Sciences Directorate to form a strategic alliance to meet the neutron science users’ next-generation requirements.

The result is ADARA – the Accelerating Data Acquisition, Reduction, and Analysis Collaboration project, which comprises individuals from across ORNL spanning five divisions: the Neutron Sciences Directorate’s (NScD’s) Neutron Data Analysis and Visualization Division (NDAV) and Research Accelerator Division, the Computing and Computational Sciences Directorate’s (CCSD’s) Computer Science and Mathematics Division, National Center for Computational Sciences (NCCS) and Information Technology Services Division.

The collaboration between neutron sciences and supercomputing, two of ORNL’s most high-powered research centers, has created a new data infrastructure that will enhance users’ ability to reduce and analyze data as they are taken; create data files instantly after acquisition, regardless of size; reduce a data set in seconds after acquisition; and provide the resources for any user to do post-acquisition reduction, analysis, visualization, and modeling – not just on site – but literally from anywhere.

At neutron experimental facilities today, research scientists collect data during experiments and do an initial analysis of their findings. The detailed data analysis that follows can take from minutes to months. For maximum effect, visiting users manipulate their data – reduce it, analyze it, and, increasingly, visualize and model it on supercomputers – to fully understand the content. This is an interactive process.

Galen Shipman is data system architect for the Computing and Computational Sciences Directorate and principal investigator of the ADARA project. We asked him to tell us what improvements SNS users can expect in the coming months.

What are the data access and analysis problems that confront SNS users today?

Galen Shipman: Much of the software infrastructure for data acquisition, reduction, and analysis at SNS was designed more than a decade ago. It is a good system and has served the needs of the users, but there is a need to shorten the time from experiment to the scientific result. That is really what the ADARA project is about. It’s about decreasing this time by providing a streaming data infrastructure and an integrated high-performance computing (HPC) capability that provides users with instant feedback from experiments at SNS.

We began in October 2011 with an analysis of the current infrastructure, working with experts at SNS. We quickly found that one of the major issues was how long it took to start getting feedback from an experiment on a beam line as it is running. What the scientist often wants to see from an experiment at SNS is an energy spectrum, but the data captured and provided to the user are simply the position and time of flight of neutrons as they travel through a material and hit a bank of detectors surrounding the material.

The current process of providing this feedback entails capturing all the neutron event data and saving it to a data file. After the entire experiment is complete, the data files are translated to a common data format known as NeXus. After this translation is complete, a data reduction process uses MANTID, a data-reduction platform, to transform the raw neutron event data to an energy spectrum or diffraction pattern. Finally, then, the user starts seeing the results of the experiment.

Often reduction is a short process. It can be minutes for small data sets on short experiments. In other cases, it can take a day or more – a full day from completion of the experiment and then another day to actually start getting feedback on what it meant and what the results are. This long lead time from the experiment to receiving feedback from the experiment can significantly impact the productivity of scientists at SNS.

How did the team propose to speed up data reduction and get to that energy spectrum faster?

Shipman: The concept, the leap forward, is to go from experiment to data reduction to obtaining an energy spectrum nearly instantaneously, while the experiment is still running.

Rather than the current approach of saving data in “buckets” and, once the bucket is full, handing the bucket off to the next process, we do a streaming approach. As data are being captured, we concurrently do translation. Every single event coming off a detector is translated live to a common data format. While doing translation, we are also doing data reduction, so as those events are coming off the detectors, we are also doing live data reduction into an energy spectrum.

How do you enable simultaneous translation and reduction of the neutron events coming off the detectors?

Shipman: For the architecture, we’ve leveraged some of the techniques that we were already using in HPC, as well as some of the techniques from more traditional, distributed computing. The fundamental architecture for our streaming data system is built upon a high-performance publish/subscribe system. We have a system we call the stream management service (SMS). It collects information from multiple feeds: from the neutron detectors, the experiment environment, such as temperature within the sample environment, and orientation of the sample. This information is what we call slow controls information. We also collect data from a variety of other sources such as Fermi choppers [devices that block the neutron beam for a fraction of time in milliseconds]. All of these data are “published” to the SMS, which then aggregates the data into a single, common network stream that can be sent to one or more downstream “subscribers.”

One of the downstream subscribers we have developed has been dubbed the Streaming Translation Service, which translates the unified neutron event stream on the fly and creates NeXus files live, as the experiment is conducted. The instant an experiment is over, the full NeXus file is created. It’s done. It doesn’t matter if it is a terabyte. It doesn’t matter if it is just a few megabytes.

Another downstream subscriber we have developed, known as the Streaming Reduction Service, which leverages the MANTID system, transforms the neutron event stream live from simple detector position and time of flight to an energy spectrum in real time. This provides scientists at SNS with real-time feedback from their experiment coupled with the Mantid reduction and analysis platform.

What happens to all the data after the experiment is completed?

Shipman: Although much of our work has focused on providing real-time feedback from an experiment, certain tasks in the data processing chain can be conducted only after the experiment is completed. To support this, the ADARA team has developed an automated workflow engine based on the Apache ActiveMQ system for post-stream processing. This workflow engine allows for coupling of an arbitrary number of tasks to the completion of an experiment, such as cataloging of the experiment data, additional data reduction and analysis, and archiving of the experiment data to our multi-petabyte archival storage system at the NCCS.

Once cataloged, these data are available for subsequent reanalysis and intercomparison with previous experiments. This post-processing step can be highly interactive in which users interact with their data through the Mantid software package or through other analysis tools and custom applications. Although much of the data captured can be analyzed using a workstation computer, many of the datasets require HPC systems to provide users with timely feedback. While HPC systems can provide timely feedback and support interactive analysis, in the past these systems have only been accessible by advanced users with a background in parallel computing. To support a much broader set of users, we have integrated support of HPC systems into Mantid, effectively hiding the complexities of parallel computing while providing its benefits to our users.

So you bring the advantages of HPC systems to all the SNS users?

Shipman: Exactly. We have built an integrated HPC capability for users at SNS. Through a web service-enabled architecture, scientists at SNS – or scientists sitting in a coffee shop across the country – can seamlessly conduct a variety of analysis or reduction tasks on HPC infrastructure at the NCCS. From the users’ perspective, they are interacting with an application on their desktop. But behind the scenes, we are farming out larger reduction and analysis tasks to HPC systems running the Moab Intelligence engine from Adaptive Computing through a Web Service RESTful API. These HPC systems have an order of magnitude more computational capability than their desktop. This has enabled dramatic acceleration in post-processing workloads, in which scientists reanalyze their data from a completed experiment or compare a number of completed experiments. Our ActiveMQ workflow manager, based on Apache Active MQ, can also leverage this framework, farming out computationally intensive tasks to HPC systems at the NCCS as part of the experiment pipeline. We are really excited about this capability; we have in essence developed an elastic compute capability using both software as-a-service and platform as-a-service models that deliver the best of HPC and cloud computing to users at SNS.

Is neutron science research effectively partnering with supercomputing?

Shipman: Yes. The ADARA team has already built out the software and hardware infrastructure to support the use of NCCS HPC systems by scientists at SNS. Our next steps will include coupling the live streaming capability with modeling and simulation, enabling real-time analysis of experiments, such as fitting of the experiment data to a model of the material in the experiment. This will enable an entirely new level of real-time feedback from experiments at SNS. In the future, this and techniques that leverage the coupling of experiment and simulation will enable systems at the Oak Ridge Leadership Computing Facility (OLCF) to steer the experiment, providing the scientist with real-time information from a simulation of the material that they can use to more efficiently conduct the experiment at SNS. In fact, we have begun the initial steps of this work through the Center for Accelerated Materials Modeling, led by Mark Hagen, NDAV group lead.

Through this and other upcoming work, we see a future in which the Titan multi-petaflop platform at the OLCF could be steering an experiment based on intercomparison of simulation of a material with neutron data captured at SNS. This coupling of neutrons and computation could provide new breakthroughs in materials science, biology, and engineering, while significantly improving the productivity of our users.

What and who got this started?

Shipman: Jeff Nichols, the associate Laboratory director for the CCSD, and Kelly Beierschmitt, the associate Laboratory director for NScD, recognized the importance of coupling computation and neutron science. They realized that by doing so we could make significant progress in increasing the productivity of scientists at SNS and ultimately develop new capabilities in multiple science domains that use neutrons and computing.

The ADARA project has required expertise in both computing and neutron science. The computing team doesn’t have the science background in neutrons but does have the software/engineering background required to help build the system. So in collaboration, leveraging previous work that the neutron sciences data team had done, the ADARA team was able to extend those concepts and write new software to deliver a streaming infrastructure and an integrated HPC capability at SNS. Although we have made significant progress through the ADARA project, this is just the beginning of a long-term strategic partnership between computing and neutron science here at ORNL, a partnership enabled by the Laboratory’s multi-program science and technology capabilities.

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