NERSC Accepts Edison Supercomputer

January 30, 2014

Jan. 30 — The National Energy Research Scientific Computing (NERSC) Center recently accepted “Edison,” a new flagship supercomputer designed for scientific productivity.

Named in honor of American inventor Thomas Alva Edison, the Cray XC30 will be dedicated in a ceremony held at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) on Feb. 5, and scientists are already reporting results.

About 5,000 researchers working on 700 projects and running 600 different codes compute at NERSC, which is operated by Berkeley Lab. They produce an average of 1,700 peer-reviewed publications every year, making NERSC the most productive scientific computing center serving the Department of Energy’s Office of Science.

“We support a very broad range of science, from basic energy research to climate science, from biosciences to discovering new materials, exploring high energy physics and even uncovering the very origins of the universe,” said NERSC Director Sudip Dosanjh.

Edison can execute nearly 2.4 quadrillion floating-point operations per second (petaflop/s) at peak theoretical speeds. While theoretical speeds are impressive, “NERSC’s longstanding approach is to evaluate proposed systems by how well they meet the needs of our diverse community of researchers, so we focus on sustained performance on real applications,” said NERSC Division Deputy for Operations Jeff Broughton, who led the Edison procurement team.

“For us, what’s really important is the scientific productivity of our users,” Dosanjh said. That’s why Edison was configured to handle two kinds of computing equally well: data analysis and simulation and modeling.

Data Analysis Joins Simulation and Modeling

Traditionally, scientific supercomputers are configured to simulate and model complex phenomena, such as nanomaterials converting electricity into photons of light, climate changing over decades or centuries, or interstellar gases forming into stars and galaxies. Simulations require a lot of processors running in unison, but not necessarily a lot of memory for each processor.

Data analysis, such as genome sequencing or molecular screening programs that search for promising new materials or drugs,  often involves high throughput computing—running large numbers of loosely coupled simulations simultaneously. Such “ensemble computing” requires more memory per node and has typically been relegated to separate computer clusters. As instruments and experiments deliver more and more data however, scientists need more computing power to crunch it; so smaller clusters no longer suffice.

“Facilities throughout the Department of Energy are being inundated with data that researchers don’t have the ability to understand, process or analyze sufficiently,” said Dosanjh. Historically, NERSC was an exporter of data as scientists ran large-scale simulations and then moved that data to other sites. But with the growth of experimental data coming from other sites, NERSC is now a net importer, taking in a petabyte of data in fields such as biosciences, climate and high-energy physics each month.

Both types of computing rely heavily on moving data, said Dosanjh. “So Edison has been optimized for that: It has a really high-speed interconnect, it has lots of memory bandwidth, lots of memory per node, and it has very high input/output speeds to the file system and disk system.”

“If you have a computing resource like Edison, one with the flexibility to run different classes of problems, then you can apply the full capacity of your system to the problem at hand, whether that be high-throughput genome sequencing or highly parallel climate simulations,” said Broughton.

Less Time Tweaking Codes, More Time Doing Science

Because Edison does not employ accelerators, such as graphics processing units (GPUs), scientists have been able to move their codes from NERSC’s previous flagship system (a Cray XE6 named for computer scientist Grace Hopper) to Edison with little or no changes, another consideration meant to keep scientists doing science instead of rewriting code.

“We were able to open Edison to all our users shortly after installation for testing, and the system was immediately full,” said Broughton. By the time Edison was accepted and placed into production, scientists had logged millions of processor hours of research into areas as varied as carbon sequestration, nanomaterials, cosmology, and combustion.

And while researchers may not see or appreciate Edison’s advances in energy efficiency, it will impact their ability to do science. “In coming years, performance will be more limited by power than anything else, so energy efficiency is critical,” said Dosanjh.

Free Cooling

In preparation for its 2015 move into a custom-built data center (the Computational Research and Theory facility), Edison is the first supercomputer at NERSC to rely solely on outside air for cooling, a technique known as “free cooling.” Edison is cooled without mechanical chillers. Instead water is circulated through outdoor cooling towers and back into the system’s internal radiators, which cool air rather than heat it. Fans located between each pair of cabinets in a row pull air in one end; circulate it through a radiator, over the hot components and on to the next set of cabinets before it exits at the row’s end. This side-to-side airflow, or transverse cooling, is more energy efficient than the typical front-to-back flow of most systems.

Edison will be dedicated on Ferbruary 5 as part of the annual NERSC Users Group being held February 3-6 at Berkeley Lab. “As we celebrate NERSC’s 40th anniversary, it’s quite fitting we start the year by dedicating Edison, a system that embodies our guiding principle over the last four decades: computing in the service of science,” said NERSC director Dosanjh.

Deployment of Edison was made possible in part by funding from DOE’s Office of Science and the DARPA High Productivity Computing Systems program.

DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time.  For more information, please visit science.energy.gov.

About NERSC and Berkeley Lab

The National Energy Research Scientific Computing Center (NERSC) is the primary high-performance computing facility for scientific research sponsored by the U.S. Department of Energy’s Office of Science. Located at Lawrence Berkeley National Laboratory, the NERSC Center serves more than 4,000 scientists at national laboratories and universities researching a wide range of problems in combustion, climate modeling, fusion energy, materials science, physics, chemistry, computational biology, and other disciplines. Berkeley Lab is a U.S. Department of Energy national laboratory located in Berkeley, California. It conducts unclassified scientific research and is managed by the University of California for the U.S. DOE Office of Science.

—–

Source: NERSC

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!

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Hedge Funds (with Supercomputing help) Rank First Among Investors

May 22, 2017

In case you didn’t know, The Quants Run Wall Street Now, or so says a headline in today’s Wall Street Journal. Quant-run hedge funds now control the largest Read more…

By John Russell

IBM, D-Wave Report Quantum Computing Advances

May 18, 2017

IBM said this week it has built and tested a pair of quantum computing processors, including a prototype of a commercial version. That progress follows an an Read more…

By George Leopold

PRACEdays 2017 Wraps Up in Barcelona

May 18, 2017

Barcelona has been absolutely lovely; the weather, the food, the people. I am, sadly, finishing my last day at PRACEdays 2017 with two sessions: an in-depth loo Read more…

By Kim McMahon

HPE Extreme Performance Solutions

Exploring the Three Models of Remote Visualization

The explosion of data and advancement of digital technologies are dramatically changing the way many companies do business. With the help of high performance computing (HPC) solutions and data analytics platforms, manufacturers are developing products faster, healthcare providers are improving patient care, and energy companies are improving planning, exploration, and production. Read more…

US, Europe, Japan Deepen Research Computing Partnership

May 18, 2017

On May 17, 2017, a ceremony was held during the PRACEdays 2017 conference in Barcelona to announce the memorandum of understanding (MOU) between PRACE in Europe Read more…

By Tiffany Trader

NSF, IARPA, and SRC Push into “Semiconductor Synthetic Biology” Computing

May 18, 2017

Research into how biological systems might be fashioned into computational technology has a long history with various DNA-based computing approaches explored. N Read more…

By John Russell

DOE’s HPC4Mfg Leads to Paper Manufacturing Improvement

May 17, 2017

Papermaking ranks third behind only petroleum refining and chemical production in terms of energy consumption. Recently, simulations made possible by the U.S. D Read more…

By John Russell

PRACEdays 2017: The start of a beautiful week in Barcelona

May 17, 2017

Touching down in Barcelona on Saturday afternoon, it was warm, sunny, and oh so Spanish. I was greeted at my hotel with a glass of Cava to sip and treated to a Read more…

By Kim McMahon

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Cray Offers Supercomputing as a Service, Targets Biotechs First

May 16, 2017

Leading supercomputer vendor Cray and datacenter/cloud provider the Markley Group today announced plans to jointly deliver supercomputing as a service. The init Read more…

By John Russell

HPE’s Memory-centric The Machine Coming into View, Opens ARMs to 3rd-party Developers

May 16, 2017

Announced three years ago, HPE’s The Machine is said to be the largest R&D program in the venerable company’s history, one that could be progressing tow Read more…

By Doug Black

What’s Up with Hyperion as It Transitions From IDC?

May 15, 2017

If you’re wondering what’s happening with Hyperion Research – formerly the IDC HPC group – apparently you are not alone, says Steve Conway, now senior V Read more…

By John Russell

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

HPE Launches Servers, Services, and Collaboration at GTC

May 10, 2017

Hewlett Packard Enterprise (HPE) today launched a new liquid cooled GPU-driven Apollo platform based on SGI ICE architecture, a new collaboration with NVIDIA, a Read more…

By John Russell

IBM PowerAI Tools Aim to Ease Deep Learning Data Prep, Shorten Training 

May 10, 2017

A new set of GPU-powered AI software announced by IBM today brings automation to many of the tedious, time consuming and complex aspects of AI project on-rampin Read more…

By Doug Black

Bright Computing 8.0 Adds Azure, Expands Machine Learning Support

May 9, 2017

Bright Computing, long a prominent provider of cluster management tools for HPC, today released version 8.0 of Bright Cluster Manager and Bright OpenStack. The Read more…

By John Russell

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. Just how close real-wo 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 cam Read more…

By John Russell

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 Read more…

By Tiffany Trader

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 a Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

Since our first formal product releases of OSPRay and OpenSWR libraries in 2016, CPU-based Software Defined Visualization (SDVis) has achieved wide-spread adopt Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Last week, Google reported that its custom ASIC Tensor Processing Unit (TPU) was 15-30x faster for inferencing workloads than Nvidia's K80 GPU (see our coverage Read more…

By Tiffany Trader

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 ne Read more…

By Tiffany Trader

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

Leading Solution Providers

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 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 w 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 Read more…

By Steve Campbell

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 Eng 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 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 networ Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

As China continues to prove its supercomputing mettle via the Top500 list and the forward march of its ambitious plans to stand up an exascale machine by 2020, Read more…

By Tiffany Trader

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 advance Read more…

By Tiffany Trader

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