SDSC’s New Storage Cloud: ‘Flickr for Scientific Data’

By Michael Feldman

October 6, 2011

Last month, the San Diego Supercomputer Center launched what it believes is “the largest academic-based cloud storage system in the U.S.” The infrastructure is designed to serve the country’s research community and will be available to scientists and engineers from essentially any government agency that needs to archive and share super-sized data sets.

Certainly the need for such a service exists. The modern practice of science is a community activity and the way researchers collaborate is by sharing their data. Before the emergence of cloud, the main way to accomplish that was via emails and sending manuscripts back and forth over the internet. But with the coalescence of some old and new technologies, there are now economically viable ways for sharing really large amounts of data with colleagues.

In the press release describing the storage cloud, SDSC director Michael Norman described it thusly: “We believe that the SDSC Cloud may well revolutionize how data is preserved and shared among researchers, especially massive datasets that are becoming more prevalent in this new era of data-intensive research and computing.” Or as he told us more succinctly, “I think of it as Flickr for scientific data.”

It’s not just for university academics. Science projects under the DOE, NIH, NASA, and others US agencies are all welcome. Even though the center is underwritten by the NSF, it gets large amounts funding and researchers from all of those organizations. Like most NSF-supported HPC centers today, SDSC is a multi-agency hub.

Norman says that the immediate goal of this project is to support the current tape archive customers at SDSC with something that allows for data sharing. For collaboration, he says, tape archive is probably the worst possible solution. Not only is the I/O bandwidth too low, but with a tape platform, there is always a computer standing between you and your data.

With a disk-based cloud solution, you automatically get higher bandwidth, but more importantly, a web interface for accessing data. Every data file is provided a unique URL, making the information globally accessible from any web client. “It can talk to your iPhone as easily as it can talk to your mainframe,” says Norman.

The initial cloud infrastructure consists of 5.5 petabytes of disk capacity linked to servers via a couple of Arista Networks 7508 switches, which provide 10 terabits/second of connectivity. Dell R610 nodes are used for the storage servers, as well as for load balancing and proxy servers. The storage hardware is made up of Supermicro SC847E26 JBODs, with each JBOD housing 45 3TB Seagate disks. All of this infrastructure is housed and maintained at SDSC.

The cloud storage will replace the current tape archive at the center, in this case a StorageTek system that currently holds about a petabyte of user data spread across 30 or 40 projects. Over the next 12 to 18 months, SDSC will migrate the data, along with their customers, over to the cloud and mothball the StorageTek hardware.

According to Norman some of these tape users would like to move other data sets into these archives and the cloud should make that process a lot smoother. “We are setting this up as a sustainable business and hope to have customers who use our cloud simply as preservation environment,” he says. For example, they’re already talking with a NASA center that is looking to park their mission data somewhere accessible, but in an archive type environment.

The move to a storage cloud was not all locally motivated however. Government agencies like the NSF and NIH began mandating data sharing plans for all research projects. Principal investigators (PIs) can allocate up to 5 percent of their grant funding for data storage, but as it turns out, on a typical five- or six-figure research grant, that’s not very much money.

In order for such data sharing to be economically viable to researchers, it basically has to be a cost-plus model. Norman thinks they have achieved that with their pricing model, although admits that “if you asked researchers what would be the right price, it would be zero.”

For 100 GB of storage, rates are $3.25/month for University of California (UC) users, 5.66/month for UC affiliates and $7.80/month for customers outside the UC sphere. Users who are looking for a big chunk of storage in excess of 200TB will need to pay for the extra infrastructure, in what the program refers to as their “micro-condo” offering.

The condo pricing scheme is more complex, but is offered to users with really large datasets and for research grants that include storage considerations for proposals and budgeting. And even though this model doesn’t provide for a transparently elastic cloud, the condo model at least makes the infrastructure expandable. According to Norman, their cloud is designed to scale up into the hundreds of petabytes realm.

Although data owners pay for capacity, thanks to government-supported science networks , data consumers don’t pay for I/O bandwidth. Wide are networks under projects such as CENIC (Corporation for Education Network Initiatives in California), ESNet (Energy Sciences Network), and XSEDE (Extreme Science and Engineering Discovery Environment) are public investments that can be leveraged by SDSC’s cloud. That can be a huge advantage over commercial storage clouds like Amazon’s Simple Storage Service (S3), where users have to account for data transfer costs.

While some researchers may end up using commercial offerings like Amazon S3, Norman thinks those types of setups generally don’t cater to academic types and are certainly not part of most researchers’ mindsets. They are also missing the some of the high-performance networking enabled by big 10GbE pipes and low-latency switching at SDSC.

Whether the center’s roll-your-own cloud will be able to compete against commercial clouds on a long-term basis remains to be seen. One of the reasons a relatively small organization like SDSC can even build such a beast today is thanks in large part to the availability of cheap commodity hardware and the native expertise at the center to build high-end storage systems from parts.

There is also OpenStack — an open-source cloud OS that the SDSC is using as the basis of their offering. Besides being essentially free for the taking, the non-proprietary nature of OpenStack also means the center will not be locked into any particular software or hardware vendors down the road.

“With OpenStack going open source, it’s now possible for anybody to set up a little cloud business,” explains Norman “We’re just doing it in an academic environment.”

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion XL — were added to the benchmark suite as MLPerf continues Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire