SDSC Puts Data at Center Stage

By Michael Feldman

September 7, 2010

The naming of Michael Norman as director of the San Diego Supercomputer Center (SDSC) last week was long overdue. SDSC has been without an official director for more than 14 months, with Norman filling the spot as the interim head since last July. The appointment could mark something of a comeback for the center, which has not only gone director-less during this time, but has been operating without a high-end supercomputer as well.

DataStar, an aged 15-teraflop IBM P690 system was retired in October of 2008 and the center’s relatively small Blue Gene/L machine was mothballed in June 2009. But Trestles, a new 100-teraflop supercomputer funded by the NSF, is scheduled to be up and running before the end of the year. And with the addition of Gordon, a 245-teraflop supercomputer slated for deployment in mid-2011, SDSC will again be a relatively FLOPS-happy place.

You might say that our FLOPS profile looked like the Dow Jones,” laughed Norman. “It really swooned in 2008 and 2009. With the Trestles system coming at the end of 2010, we’ll be back on the board.”

A Little History

Norman, a computational astrophysicist, got his start at Lawrence Livermore in the 1970s, where he was able to combine his love of astronomy (his major at Caltech) with his interest in all things computational. “And then basically, I was a gypsy, going after supercomputer cycles where ever I could find them,” said Norman.

After his stint at Livermore, Norman did a four-year tour (1980 – 1984) at the Max Planck institute for Astrophysics, owners of the first Cray 1 system in Western Europe. It was during his time at Max Planck when he collaborated heavily with Larry Smarr, currently UC San Diego’s director of Calit2.

“I have worked closely with Mike for over 30 years, since he was a grad student at Livermore,” said Smarr. “He brings a wealth of experience from working at multiple national and international supercomputer centers, as well as being a hands-on pioneer in computational astrophysics and cosmology.”

It was also during this period at Max Planck that he and Smarr conceived of the idea of the National Center for Supercomputing Applications (NCSA). Two years after Norman had left the the institute, NCSA was born. But in the interim Norman went to Los Alamos to work as a staff scientist, before rejoining Smarr at NCSA in 1986. He stayed there for 14 years, before coming to San Diego.

There, he got involved with SDSC, first as a researcher from the UC San Diego physics department, then as a member of the center’s Executive Committee. Toward the end of 2007, Norman jumped into the NSF funding fray to help SDSC win one of agency’s Track 2 supercomputer procurements. The first attempt was not successful, but the second one was, resulting in the award for the Gordon system, with Norman as the principle investigator. In the summer of 2009 Norman became the interim director of the center after Fran Berman relinquished her directorship, moving to Rensselaer Polytechnic Institute as the VP of Research.

Data-Centric Supercomputing

Norman’s ascendance at SDSC ratifies the center’s new focus on data-intensive supercomputing. He, more than anyone, wanted to make San Diego a place for HPC and HPD (high-performance data), a term that he coined to draw attention to the data-centric model. The idea is to support the whole scientific enterprise, and that requires a more highly integrated storage infrastructure supporting the supercomputers.

“There are these two cultures: the HPC culture and the culture of data-intensive science, or fourth paradigm, whatever you want to call it,” explained Norman. “They seem to be living in different worlds. I’m hoping to bring them together at SDSC.”

Part of that goal will be served by the upcoming Gordon supercomputer, which will feature a quarter of a petabyte of flash memory and virtual shared memory software. At 245 teraflops, the machine delivers only moderate performance by today’s elite supercomputing standards. But a lot of today’s applications are I/O bound, rather than compute bound, and would really prefer to have their big datasets sitting in main memory. Since RAM is rather expensive, flash is turning out to be the next best thing. Because of its unique memory architecture, Gordon is expected to do exceeding well at dealing with terascale-sized databases.

The driver for all this is the so-called “data deluge,” which is flowing across multiple disciplines — in traditional technical computing areas such as physics, astronomy engineering, bioinformatics, and medicine, as well as in less traditional realms, such as social sciences, arts, and economics.

Terabyte streams coming from ocean observing sensors, astronomical CCD cameras, and genome sequencers are just a few examples of how data is outrunning the computing infrastructure. Some of these, like the astronomical data streams, can require their own dedicated supercomputer.

The problem is even more acute for genome sequencers. Genome biologists are accustomed to doing their work on a workstations, because that used to be perfectly adequate. But the throughput on these machines has increased so rapidly that sequencers have gone from generating gigabytes to terabytes in just a couple of years. Even bigger improvements on the horizon. “They’re really at sea right now,” said Norman, “and they’re realizing they can’t do this work in their labs anymore.”

The centerpiece of the data-intensive remodel at the SDSC will be something called Data Oasis, a very large scalable file store designed to serve multiple HPC clusters as well as data-intensive machines. Basically it’s an extensible disk farm that will have high connectivity through a very large 10 gigabit switch. From Norman’s perspective, this basically turns the datacenter inside out, with the compute machinery and data generators at the periphery and the data storage in the center.

Industry Partner Program Reboot

Once the new infrastructure gets in place, Norman hopes to revive the center’s industry partnership program. Since retiring their capacity supercomputers over the last couple of years, SDSC hasn’t been able to attract a lot of commercial collaborators. After DataStar was switched off, the center used internal funds to buy Triton, a 20-teraflop Appro cluster. According to Norman, they garnered a few industrial partners with that system, but it really doesn’t have the capacity to support a large program.

The Trestles supercomputer, true to its name, will act as bridge system, until the larger Gordon machine is installed next year. (Trestles, by the way, is also a the name of a famous surf break in San Diego.) Shell Oil is very interested in the Gordon architecture, said Norman. Currently, the oil company is employing Dash, a smaller flash memory-accelerated prototype of Gordon, for an undisclosed project, and they are hoping to grow that work once its more capable successor comes online.

According to Norman, they’re also active cultivating the biomedical informatics business, starting with work at the local USCD medical center. They intend to tap faculty working at the university’s School of Medicine to help build up some of this expertise, with the hope that this work could spill into commercial relationships.

Boutique Funding

With it’s data-centric focus, SDSC has chosen not to be part of the latest petaflop-to-exaflop race. While that gives the center an interesting niche, it’s generally at odds with the funding model the NSF uses to fund supercomputing nowadays. Tighter budgets have convinced the agency to spread the money in a more piecemeal way. So you might get money for a supercomputer, but not for the data store or the network infrastructure that’s needed by the whole facility. “In the old days when we had true supercomputer center funding, there was enough money to create an integrated environment,” explained Norman.

In that sense, the centers are no longer funded as such. They’re funded as places where the agency can plop down a resource. From Norman’s perspective, that’s not a workable long-term strategy for supercomputing centers. “It’s possible we’ll get back to a more sustainable model,” he said. “I certainly hope so.”

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!

China’s Expanding Effort to Win in Microchips

July 27, 2017

The global battle for preeminence, or at least national independence, in semiconductor technology and manufacturing continues to heat up with Europe, China, Japan, and the U.S. all vying for sway. A fascinating article ( Read more…

By John Russell

Hyperion: Storage to Lead HPC Growth in 2016-2021

July 27, 2017

Global HPC external storage revenues will grow 7.8% over the 2016-2021 timeframe according to an updated forecast released by Hyperion Research this week. HPC server sales, by comparison, will grow a modest 5.8% to $14.8 Read more…

By John Russell

Exascale FY18 Budget – The Senate Provides Their Input

July 27, 2017

In the federal budgeting world, “regular order” is a meaningful term that is fondly remembered by members of both the Congress and the Executive Branch. Regular order is the established process whereby an Administrat Read more…

By Alex R. Larzelere

HPE Extreme Performance Solutions

HPE Servers Deliver High Performance Remote Visualization

Whether generating seismic simulations, locating new productive oil reservoirs, or constructing complex models of the earth’s subsurface, energy, oil, and gas (EO&G) is a highly data-driven industry. Read more…

India Plots Three-Phase Indigenous Supercomputing Strategy

July 26, 2017

Additional details on India's plans to stand up an indigenous supercomputer came to light earlier this week. As reported in the Indian press, the Rs 4,500-crore (~$675 million) supercomputing project, approved by the Ind Read more…

By Tiffany Trader

Exascale FY18 Budget – The Senate Provides Their Input

July 27, 2017

In the federal budgeting world, “regular order” is a meaningful term that is fondly remembered by members of both the Congress and the Executive Branch. Reg Read more…

By Alex R. Larzelere

India Plots Three-Phase Indigenous Supercomputing Strategy

July 26, 2017

Additional details on India's plans to stand up an indigenous supercomputer came to light earlier this week. As reported in the Indian press, the Rs 4,500-crore Read more…

By Tiffany Trader

Tuning InfiniBand Interconnects Using Congestion Control

July 26, 2017

InfiniBand is among the most common and well-known cluster interconnect technologies. However, the complexities of an InfiniBand (IB) network can frustrate the Read more…

By Adam Dorsey

NSF Project Sets Up First Machine Learning Cyberinfrastructure – CHASE-CI

July 25, 2017

Earlier this month, the National Science Foundation issued a $1 million grant to Larry Smarr, director of Calit2, and a group of his colleagues to create a comm Read more…

By John Russell

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

Fujitsu Continues HPC, AI Push

July 19, 2017

Summer is well under way, but the so-called summertime slowdown, linked with hot temperatures and longer vacations, does not seem to have impacted Fujitsu's out Read more…

By Tiffany Trader

Researchers Use DNA to Store and Retrieve Digital Movie

July 18, 2017

From abacus to pencil and paper to semiconductor chips, the technology of computing has always been an ever-changing target. The human brain is probably the com Read more…

By John Russell

The Exascale FY18 Budget – The Next Step

July 17, 2017

On July 12, 2017, the U.S. federal budget for its Exascale Computing Initiative (ECI) took its next step forward. On that day, the full Appropriations Committee Read more…

By Alex R. Larzelere

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

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

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

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

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

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

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

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ 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 the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. Read more…

By Tiffany Trader

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

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

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

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 Read more…

By Alex Woodie

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

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