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!

At Long Last, Supercomputing Helps to Map the Poles

August 22, 2019

“For years,” Paul Morin wrote, “those of us that made maps of the Poles apologized. We apologized for the blank spaces on maps, we apologized for mountains being in the wrong place and out-of-date information.” Read more…

By Oliver Peckham

Xilinx Says Its New FPGA is World’s Largest

August 21, 2019

In this age of exploding “technology disaggregation” – in which the Big Bang emanating from the Intel x86 CPU has produced significant advances in CPU chips and a raft of alternative, accelerated architectures... Read more…

By Doug Black

Supercomputers Generate Universes to Illuminate Galactic Formation

August 20, 2019

With advanced imaging and satellite technologies, it’s easier than ever to see a galaxy – but understanding how they form (a process that can take billions of years) is a different story. Now, a team of researchers f Read more…

By Oliver Peckham

AWS Solution Channel

Efficiency and Cost-Optimization for HPC Workloads – AWS Batch and Amazon EC2 Spot Instances

High Performance Computing on AWS leverages the power of cloud computing and the extreme scale it offers to achieve optimal HPC price/performance. With AWS you can right size your services to meet exactly the capacity requirements you need without having to overprovision or compromise capacity. Read more…

HPE Extreme Performance Solutions

Bring the combined power of HPC and AI to your business transformation

FPGA (Field Programmable Gate Array) acceleration cards are not new, as they’ve been commercially available since 1984. Typically, the emphasis around FPGAs has centered on the fact that they’re programmable accelerators, and that they can truly offer workload specific hardware acceleration solutions without requiring custom silicon. Read more…

IBM Accelerated Insights

Keys to Attracting the Newest HPC Talent – Post-Millennials

[Connect with HPC users and learn new skills in the IBM Spectrum LSF User Community.]

For engineers and scientists growing up in the 80s, the current state of HPC makes perfect sense. Read more…

Singularity Moves Up the Container Value Chain

August 20, 2019

The enterprise version of the Singularity HPC container platform released this week by Sylabs is designed to allow users to create, secure and share the high-end containers in self-hosted production deployments. The e Read more…

By George Leopold

At Long Last, Supercomputing Helps to Map the Poles

August 22, 2019

“For years,” Paul Morin wrote, “those of us that made maps of the Poles apologized. We apologized for the blank spaces on maps, we apologized for mountains being in the wrong place and out-of-date information.” Read more…

By Oliver Peckham

IBM Deepens Plunge into Open Source; OpenPOWER to Join Linux Foundation

August 20, 2019

IBM today announced it was contributing the instruction set (ISA) for its Power microprocessor and the designs for the Open Coherent Accelerator Processor Inter Read more…

By John Russell

Ayar Labs to Demo Photonics Chiplet in FPGA Package at Hot Chips

August 19, 2019

Silicon startup Ayar Labs continues to gain momentum with its DARPA-backed optical chiplet technology that puts advanced electronics and optics on the same chip Read more…

By Tiffany Trader

Scientists to Tap Exascale Computing to Unlock the Mystery of our Accelerating Universe

August 14, 2019

The universe and everything in it roared to life with the Big Bang approximately 13.8 billion years ago. It has continued expanding ever since. While we have a Read more…

By Rob Johnson

AI is the Next Exascale – Rick Stevens on What that Means and Why It’s Important

August 13, 2019

Twelve years ago the Department of Energy (DOE) was just beginning to explore what an exascale computing program might look like and what it might accomplish. Today, DOE is repeating that process for AI, once again starting with science community town halls to gather input and stimulate conversation. The town hall program... Read more…

By Tiffany Trader and John Russell

Cray Wins NNSA-Livermore ‘El Capitan’ Exascale Contract

August 13, 2019

Cray has won the bid to build the first exascale supercomputer for the National Nuclear Security Administration (NNSA) and Lawrence Livermore National Laborator Read more…

By Tiffany Trader

AMD Launches Epyc Rome, First 7nm CPU

August 8, 2019

From a gala event at the Palace of Fine Arts in San Francisco yesterday (Aug. 7), AMD launched its second-generation Epyc Rome x86 chips, based on its 7nm proce Read more…

By Tiffany Trader

Lenovo Drives Single-Socket Servers with AMD Epyc Rome CPUs

August 7, 2019

No summer doldrums here. As part of the AMD Epyc Rome launch event in San Francisco today, Lenovo announced two new single-socket servers, the ThinkSystem SR635 Read more…

By Doug Black

High Performance (Potato) Chips

May 5, 2006

In this article, we focus on how Procter & Gamble is using high performance computing to create some common, everyday supermarket products. Tom Lange, a 27-year veteran of the company, tells us how P&G models products, processes and production systems for the betterment of consumer package goods. Read more…

By Michael Feldman

Supercomputer-Powered AI Tackles a Key Fusion Energy Challenge

August 7, 2019

Fusion energy is the Holy Grail of the energy world: low-radioactivity, low-waste, zero-carbon, high-output nuclear power that can run on hydrogen or lithium. T Read more…

By Oliver Peckham

Cray, AMD to Extend DOE’s Exascale Frontier

May 7, 2019

Cray and AMD are coming back to Oak Ridge National Laboratory to partner on the world’s largest and most expensive supercomputer. The Department of Energy’s Read more…

By Tiffany Trader

Graphene Surprises Again, This Time for Quantum Computing

May 8, 2019

Graphene is fascinating stuff with promise for use in a seeming endless number of applications. This month researchers from the University of Vienna and Institu Read more…

By John Russell

AMD Verifies Its Largest 7nm Chip Design in Ten Hours

June 5, 2019

AMD announced last week that its engineers had successfully executed the first physical verification of its largest 7nm chip design – in just ten hours. The AMD Radeon Instinct Vega20 – which boasts 13.2 billion transistors – was tested using a TSMC-certified Calibre nmDRC software platform from Mentor. Read more…

By Oliver Peckham

TSMC and Samsung Moving to 5nm; Whither Moore’s Law?

June 12, 2019

With reports that Taiwan Semiconductor Manufacturing Co. (TMSC) and Samsung are moving quickly to 5nm manufacturing, it’s a good time to again ponder whither goes the venerable Moore’s law. Shrinking feature size has of course been the primary hallmark of achieving Moore’s law... Read more…

By John Russell

Cray Wins NNSA-Livermore ‘El Capitan’ Exascale Contract

August 13, 2019

Cray has won the bid to build the first exascale supercomputer for the National Nuclear Security Administration (NNSA) and Lawrence Livermore National Laborator Read more…

By Tiffany Trader

Deep Learning Competitors Stalk Nvidia

May 14, 2019

There is no shortage of processing architectures emerging to accelerate deep learning workloads, with two more options emerging this week to challenge GPU leader Nvidia. First, Intel researchers claimed a new deep learning record for image classification on the ResNet-50 convolutional neural network. Separately, Israeli AI chip startup Hailo.ai... Read more…

By George Leopold

Leading Solution Providers

ISC 2019 Virtual Booth Video Tour

CRAY
CRAY
DDN
DDN
DELL EMC
DELL EMC
GOOGLE
GOOGLE
ONE STOP SYSTEMS
ONE STOP SYSTEMS
PANASAS
PANASAS
VERNE GLOBAL
VERNE GLOBAL

Nvidia Embraces Arm, Declares Intent to Accelerate All CPU Architectures

June 17, 2019

As the Top500 list was being announced at ISC in Frankfurt today with an upgraded petascale Arm supercomputer in the top third of the list, Nvidia announced its Read more…

By Tiffany Trader

Top500 Purely Petaflops; US Maintains Performance Lead

June 17, 2019

With the kick-off of the International Supercomputing Conference (ISC) in Frankfurt this morning, the 53rd Top500 list made its debut, and this one's for petafl Read more…

By Tiffany Trader

AMD Launches Epyc Rome, First 7nm CPU

August 8, 2019

From a gala event at the Palace of Fine Arts in San Francisco yesterday (Aug. 7), AMD launched its second-generation Epyc Rome x86 chips, based on its 7nm proce Read more…

By Tiffany Trader

A Behind-the-Scenes Look at the Hardware That Powered the Black Hole Image

June 24, 2019

Two months ago, the first-ever image of a black hole took the internet by storm. A team of scientists took years to produce and verify the striking image – an Read more…

By Oliver Peckham

Cray – and the Cray Brand – to Be Positioned at Tip of HPE’s HPC Spear

May 22, 2019

More so than with most acquisitions of this kind, HPE’s purchase of Cray for $1.3 billion, announced last week, seems to have elements of that overused, often Read more…

By Doug Black and Tiffany Trader

Chinese Company Sugon Placed on US ‘Entity List’ After Strong Showing at International Supercomputing Conference

June 26, 2019

After more than a decade of advancing its supercomputing prowess, operating the world’s most powerful supercomputer from June 2013 to June 2018, China is keep Read more…

By Tiffany Trader

Qualcomm Invests in RISC-V Startup SiFive

June 7, 2019

Investors are zeroing in on the open standard RISC-V instruction set architecture and the processor intellectual property being developed by a batch of high-flying chip startups. Last fall, Esperanto Technologies announced a $58 million funding round. Read more…

By George Leopold

Intel 7nm GPU on Roadmap for 2021, OneAPI Coming This Year

May 8, 2019

At Intel's investor meeting today in Santa Clara, Calif., the company filled in details of its roadmap and product launch plans and sought to allay concerns about delays of its 10nm chips. In laying out its 10nm and 7nm timelines, Intel revealed that its first 7nm product would be... Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This