ESnet Launches Architecture to Help Researchers Deliver on Data-Intensive Science

By Nicole Hemsoth

April 26, 2012

The U.S. Department of Energy’s Energy Sciences Network, or ESnet, provides reliable high-bandwidth network services to thousands of researchers tackling some of the most pressing scientific and engineering problems, such as finding new sources of clean energy, increasing energy efficiency, understanding climate change, developing new materials for industry and discovering the nature of our universe. To support these research endeavors, ESnet connects scientists at more than 40 DOE sites with experimental and computing facilities in the U.S. and abroad, as well as with their collaborators around the world. ESnet is managed for DOE’s Office of Science by Lawrence Berkeley National Laboratory.

As science becomes increasingly data-intensive, the ESnet staff regularly meets with scientists to better understand their future networking needs, then develops and deploys the infrastructure and services to address those requirements before they become a reality. One example of this is the Advanced Networking Initiative, a prototype 100 gigabits-per-second networking connecting the DOE Office of Science’s top supercomputing centers in California, Illinois and Tennessee, and an international peering point in New York. This 100 Gbps prototype is now being transitioned to production and will be rolled out to all other connected DOE sites in the coming year.

In order to help these research institutions fully capitalize on this growing availability of bandwidth to manage their growing data sets, ESnet is now working with the scientific community to encourage the use of a network design model called the “Science DMZ.” The Science DMZ is a specially designed local networking infrastructure aimed at speeding the delivery of scientific data. In March 2012, the National Science Foundation supported the concept by issuing a solicitation for proposals from universities to develop Science DMZs as they upgrade their local network infrastructures.

Leading the development of the Science DMZ effort at ESnet is Eli Dart, a network engineer with previous experience at Sandia National Laboratories and the National Energy Research Scientific Computing Center. In this interview conducted by Jon Bashor of Berkeley Lab, Dart answers some basic questions about the nature of the project and its principle goals.

Jon Bashor: What is the Science DMZ and where did the Science DMZ idea come from?

Eli Dart: In its purest form, it’s an element of the overall network architecture, typically a dedicated portion of a site or campus network, located as close to the network perimeter as possible, that serves only high-performance science applications. The intent of the Science DMZ is to simplify the deployment and support of high-performance and data-intensive science applications that rely on high-speed networking for success. These applications have unique network requirements that typically cannot be met by networks that are optimized for normal business operations like web browsing, procurement and financial systems, and the like. The idea itself came from two places.

The concept of a DMZ network originated in the network security space where so-called network “demilitarized zones” or DMZs are used to provide a dedicated portion of the network near the site perimeter specifically configured to support services that interact with the outside world. These services often include authoritative DNS, incoming email, outward facing websites, etc. These services usually fall under a security policy that’s different than the one for the rest of the enterprise architecture.

You can extend that notion to build a dedicated piece of the network specifically for high performance scientific applications, again located at or near the perimeter, and with hardware you know can handle these applications. The Science DMZ is not configured to handle the standard enterprise or business functions, such as email and web servers, desktop applications, and so forth. These typically need a massive security infrastructure to protect them, and the security measures required to protect business servers and desktop applications typically cause problems for high-performance applications. The Science DMZ model explicitly separates the science traffic from general-purpose network traffic, and allows appropriate security policies and enforcement mechanisms to be applied to each.

The second source for the Science DMZ concept came from working with TCP, or the Transmission Control Protocol. While most science applications that need reliable data delivery use TCP-based tools for data movement, TCP’s interpretation of packet loss can cause performance issues. TCP interprets packet loss as network congestion, and so when loss is encountered TCP dramatically reduces its sending rate – slowing the data transfer. In practice even a tiny amount of loss (much less than 1%) can be enough to reduce TCP performance by over a factor of 100.

For years people have been trying to fix TCP (with some success), but packet loss combined with high latency is a serious performance killer. It’s easier to build an infrastructure to provide loss-free IP service and to accommodate TCP rather than change it – this is what the Science DMZ model aims to accomplish.

Bashor: What makes up the Science DMZ model?

Dart: The Science DMZ itself is a portion of the network, at or near the site perimeter, which is specifically configured to support high-performance science applications. There are several key aspects to the Science DMZ.

First, it must be built with capable equipment that can handle high-rate flows without dropping packets. Typically, that means good equipment (not cheap wiring closet switches) with enough output buffer space to handle bursty high-rate long-distance TCP flows. The switches and routers need to be able to accurately account for packets (especially the ones they drop) so that packet loss can be accounted for and its cause fixed.

Second, data transfer should be done on dedicated servers – Data Transfer Nodes, or DTNs – that are designed and configured for the purpose. Their TCP stacks need to be tuned and they need access to high-speed storage. We have seen successful DTN implementations using high-speed local RAID as well as GPFS or Lustre filesystems, the parallel filesystem model is typically found at supercomputer centers.

Third, a Science DMZ needs test and measurement infrastructure, typically perfSONAR that allows you to identify any issue that may be causing performance issues. Many problems that are real performance killers are what we call “soft failures.” A soft failure causes performance degradation so that the network is not useful for data-intensive science but does not cause an outage that identifies the failing component. The only way to find these is to independently test the infrastructure to locate the problem – if perfSONAR is already deployed, this is much easier than if the first step of the process is to find and deploy a test machine and the second step is to get the site at the other end to find a spare box and deploy it.

Finally, the Science DMZ incorporates a security policy that is tailored to the science applications rather than to general-purpose business computing. You don’t need to scan 50TB of simulation output for email viruses, and you don’t run an email client on your Data Transfer Node. So, why conflate the security policies and enforcement mechanisms for the two, especially when doing so will effectively compromise the science mission? Firewalls and other security enforcement boxes are typically unable to handle the throughput needed for data-intensive science – and they essentially never support advanced science services such as virtual circuits or software-defined networking.

Bashor: Why does it matter?

Dart: The real reason all this matters is that the current and future generations of scientific instruments are producing data at a level we’ve never seen before. Based on our projections, ESnet is expected to carry over 100 petabytes of data per month by 2015. And there is the potential for stupendous scientific advancements in that data deluge. The challenge is to figure out how to get the science done without spending the bulk of your time doing data management. Scientists are physicists, chemists, biologists, geneticists and so on, but they are seldom network experts. They are scientists.

The data volumes are becoming large enough that the systems and networks are not capable of handling them if the equipment is configured to default settings or to accommodate business applications. There’s a need for an infrastructure that supports data-intensive science. That infrastructure needs to be designed for data mobility, which means you can get the data where you need it, when you need it. In some cases, the analysis code is on a system close to the data, while other times the scientist wants to analyze the data on local resources – we need to support it all. Data-intensive science is what we’re all going to be doing for the next decade or more.

Bashor: Can you describe a typical user who would benefit from having a Science DMZ?

Dart: The main benefit of the Science DMZ is that the scientist who needs to move data doesn’t have to first troubleshoot the infrastructure in order to use it. Scientists should not have to fix the network, the data transfer servers, and so forth before they can get to work.

There really isn’t a typical user, but there are some basic commonalities. One example could be data taken from a beamline at DOE’s Advanced Light Source. A data transfer node has been set up and Globus Online installed for users who need to fetch the data. Then you have the well-known Large Hadron Collider, which has several primary Tier 1 centers feeding data to the Tier 2 centers. This requires significantly more infrastructure. In both cases, you need to make sure the network is designed correctly so that data transfer tools work correctly. These fundamental principles benefit all users.

Bashor: How does ESnet play into this equation?

Dart: ESnet is the high-performance network for DOE’s Office of Science. It’s the backbone network infrastructure for the national laboratory system, supporting science at those labs. Through our 25 years of experience serving the scientific community, we have become a central repository for the expertise to support high-performance networking. So, part of our job is to be available to support scientists at the labs and their collaborators, such as researchers at universities.

The assumption is that the high-performance network infrastructure exists to support all parts of these modern scientific collaborations. The services must be consistent from end to end – from scientist to scientist – now matter where they may located and regardless of who owns the pieces of the infrastructure. For example, if scientists at the SLAC Linear Accelerator Center are sharing data with colleagues at a Max Planck Institute in Germany, the data moves from SLAC’s local network over ESnet to GEANT, the pan-European research network, then over Germany’s DFN network and onto the local network at the institute – crossing five different domains, owned and operated by five different organizations. ESnet has built partnerships with the global ecosystem of research and education networks so that if a network problem occurs, we can work collaboratively to quickly resolve it – wherever it is.

Bashor: The NSF recently cited Science DMZ as an upgrade that universities should consider as they work to enhance their overall IT infrastructure. Your thoughts on this?

Dart: We think it’s wonderful. The infrastructure that will be built with those funds will enable discoveries that otherwise would not be possible. It’s a critical investment in the scientific infrastructure of this country.

As I said, we’re all going spend the next decade or more supporting data-intensive science, so we need to get the infrastructure right. It needs to be adaptable, flexible and expandable. We can see what’s coming in the next one to three years. In some fields, the cost of generating data has fallen to almost zero. In genome sequencing, the cost per genome has fallen off a cliff. The cost of a raw megabyte of DNA sequence is now less than 10 cents. In July 2001, it was about $4,500. What this means is that we are entering a world where scientific productivity is gated on data analysis, not data generation.

In physics, new detectors will capture data in the terabyte-per-second range, with data analysis and reduction built into the detectors, so that only the data the researchers are really interested in will be kept. This is already happening at the LHC. The ATLAS detector generates about a petabyte of data a second. It’s sent through a multi-stage trigger farm where it’s reduced to about 2.5 gigabits per second coming out. Now many other science domains are getting into this same situation.

Looking 10 years out is beyond the current planning and budget outlooks – and well outside the scope of a single procurement or a single technology. This puts the work into the architecture space, not the technology or device space. We do know that everything about the data is growing exponentially, but not the funding. So we need to design a system that works well in general and is adaptable.

If you want to do capability-class science, you need to have capability-class infrastructure. You have to have the resources appropriate to get the most return on your scientific investment.

Bashor: ESnet has a number of projects to improve end-to-end network performance through testing and measurement. Can you talk about those briefly?

Dart: Performance testing and measurement is absolutely critical. If we go back to the need to accommodate TCP because packet loss is the number one enemy of data-intensive science, we have to be able to find and fix any problems quickly. Because issues can arise anywhere on the network path which can include multiple administrative domains, you need to have the means to individually test the paths, and take out or reconfigure the problem areas.

For this reason, ESnet – with Internet2 and several other collaborators – helped develop perfSONAR, an infrastructure for network performance monitoring, making it easier to solve end-to-end performance problems on paths crossing several networks. ESnet has test and measurement capabilities at every hub site and router on our network. You have to have this infrastructure in place before a problem occurs – this allows you to find and fix the problem in hours or days, not months.

Another service for improving end-to-end performance is OSCARS, ESnet’s On-Demand Secure Circuits and Advance Reservation System. OSCARS provides multi-domain, high-bandwidth virtual circuits that guarantee end-to-end network data transfer performance. With a Science DMZ, OSCARS can touch down at an institution, along with other science-specific services. This allows for capability-class services to be used without interfering with the enterprise system. The bottom line is that science opportunities have a better chance of not being missed.

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!

HPE Server Shows Low Latency on STAC-N1 Test

February 22, 2017

The performance of trade and match servers can be a critical differentiator for financial trading houses. Read more…

By John Russell

HPC Financial Update (Feb. 2017)

February 22, 2017

In this recurring feature, we’ll provide you with financial highlights from companies in the HPC industry. Check back in regularly for an updated list with the most pertinent fiscal information. Read more…

By Thomas Ayres

Rethinking HPC Platforms for ‘Second Gen’ Applications

February 22, 2017

Just what constitutes HPC and how best to support it is a keen topic currently. Read more…

By John Russell

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

HPE Extreme Performance Solutions

O&G Companies Create Value with High Performance Remote Visualization

Today’s oil and gas (O&G) companies are striving to process datasets that have become not only tremendously large, but extremely complex. And the larger that data becomes, the harder it is to move and analyze it – particularly with a workforce that could be distributed between drilling sites, offshore rigs, and remote offices. Read more…

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

ExxonMobil, NCSA, Cray Scale Reservoir Simulation to 700,000+ Processors

February 17, 2017

In a scaling breakthrough for oil and gas discovery, ExxonMobil geoscientists report they have harnessed the power of 717,000 processors – the equivalent of 22,000 32-processor computers – to run complex oil and gas reservoir simulation models. Read more…

By Doug Black

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

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

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

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

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

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

Drug Developers Use Google Cloud HPC in the Fight Against ALS

February 16, 2017

Within the haystack of a lethal disease such as ALS (amyotrophic lateral sclerosis / Lou Gehrig’s Disease) there exists, somewhere, the needle that will pierce this therapy-resistant affliction. Read more…

By Doug Black

Azure Edges AWS in Linpack Benchmark Study

February 15, 2017

The “when will clouds be ready for HPC” question has ebbed and flowed for years. Read more…

By John Russell

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

Cray Posts Best-Ever Quarter, Visibility Still Limited

February 10, 2017

On its Wednesday earnings call, Cray announced the largest revenue quarter in the company’s history and the second-highest revenue year. Read more…

By Tiffany Trader

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

US, China Vie for Supercomputing Supremacy

November 14, 2016

The 48th edition of the TOP500 list is fresh off the presses and while there is no new number one system, as previously teased by China, there are a number of notable entrants from the US and around the world and significant trends to report on. Read more…

By Tiffany Trader

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

In April 2015, U.S. Department of Energy Undersecretary Franklin Orr announced that Intel would be the prime contractor for Aurora: Read more…

By Jan Rowell

D-Wave SC16 Update: What’s Bo Ewald Saying These Days

November 18, 2016

Tucked in a back section of the SC16 exhibit hall, quantum computing pioneer D-Wave has been talking up its new 2000-qubit processor announced in September. Forget for a moment the criticism sometimes aimed at D-Wave. This small Canadian company has sold several machines including, for example, ones to Lockheed and NASA, and has worked with Google on mapping machine learning problems to quantum computing. In July Los Alamos National Laboratory took possession of a 1000-quibit D-Wave 2X system that LANL ordered a year ago around the time of SC15. Read more…

By John Russell

Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. Read more…

By John Russell

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

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

CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

By Tiffany Trader

Leading Solution Providers

Nvidia Sees Bright Future for AI Supercomputing

November 23, 2016

Graphics chipmaker Nvidia made a strong showing at SC16 in Salt Lake City last week. Read more…

By Tiffany Trader

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

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

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

Dell Knights Landing Machine Sets New STAC Records

November 2, 2016

The Securities Technology Analysis Center, commonly known as STAC, has released a new report characterizing the performance of the Knight Landing-based Dell PowerEdge C6320p server on the STAC-A2 benchmarking suite, widely used by the financial services industry to test and evaluate computing platforms. The Dell machine has set new records for both the baseline Greeks benchmark and the large Greeks benchmark. Read more…

By Tiffany Trader

What Knights Landing Is Not

June 18, 2016

As we get ready to launch the newest member of the Intel Xeon Phi family, code named Knights Landing, it is natural that there be some questions and potentially some confusion. Read more…

By James Reinders, Intel

KNUPATH Hermosa-based Commercial Boards Expected in Q1 2017

December 15, 2016

Last June tech start-up KnuEdge emerged from stealth mode to begin spreading the word about its new processor and fabric technology that’s been roughly a decade in the making. Read more…

By John Russell

Intel and Trump Announce $7B for Fab 42 Targeting 7nm

February 8, 2017

In what may be an attempt by President Trump to reset his turbulent relationship with the high tech industry, he and Intel CEO Brian Krzanich today announced plans to invest more than $7 billion to complete Fab 42. Read more…

By John Russell

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