DOE Labs to Build Science Clouds

By Michael Feldman

October 13, 2009

Like many organizations that rely on industrial-strength datacenters, the US Department of Energy (DOE) would like to know if cloud computing can make its life easier. To answer that question, the DOE is launching a $32 million program to study how scientific codes can make use of cloud technology. Called Magellan, the program will be funded by the American Recovery and Reinvestment Act (ARRA), with the money to be split equally between the the two DOE centers that will be conducting the work: the Argonne Leadership Computing Facility (ALCF) at Argonne National Laboratory and the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory.

One of the major questions the study hopes to answer is how well the DOE’s mid-range scientific workloads match up with various cloud architectures and how those architectures could be optimized for HPC applications. Today most public clouds lack the network performance, as well as CPU and memory capacities to handle many HPC codes. The software environment in public clouds also can be at odds with HPC, since little effort has been made to optimize computational performance at the application level. Purpose-built HPC clouds may be the answer, and much of the Magellan effort will be focused on developing these private “science clouds.”

The bigger question, though, is to find out if the cloud model in general is applicable to high performance computing applications used at DOE labs and can offer a cost-effective and flexible approach for researchers. According to ALCF director Pete Beckman, that means getting the best science for the dollar. In a cloud architecture, the virtualization of resources usually translates into better utilization of hardware. In the HPC realm though, virtualization can be a performance killer and utilization is often not the big problem it is in commercial datacenters where hardware is typically undersubscribed. Perhaps of greater interest for HPC users is the ability to fast-track application deployment by taking advantage of the cloud’s ability to encapsulate complete software environments.

“There are a lot users who spend time developing there own software inside their own software stack,” says Beckman. “Getting those running on traditional supercomputers can be quite challenging. In the cloud model, sometimes these people find it easier to bring their software stack with them. That can broaden the community.”

The entire range of DOE scientific codes will be looked at, including energy research, climate modeling, bioinformatics, physics codes, applied math, and computer science research. But the focus will be on those codes that are typically run on HPC capacity clusters, which represent much of the computing infrastructure at DOE labs today. In general, codes that require capability supercomputers such as the Cray XT and the IBM Blue Gene are not considered candidates for cloud environments. This is mainly because large-scale supercomputing apps tend to be tightly coupled, relying on high speed inter-node communication and a non-virtualized software stack for maximum performance.

Most of the program’s $32 million will, in fact, be spent on new cluster systems, which will form the testbed for Magellan. According to NERSC director Kathy Yelick, the cluster hardware will be fairly generic HPC systems, based on Intel Nehalem CPUs and InfiniBand technology. Total compute performance across both sites will be on the order of 100 teraflops. Yelick says there will also be a storage cloud, with a little over a petabyte of capacity. In addition, flash memory technology will be used to optimize performance for data-intensive applications. The NERSC and ALCF clusters will be linked via ESnet, the DOE’s cutting-edge 100 Gbps network. ESnet was also a recipient of ARRA funding, and will be used to facilitate super-speed data transfers between the two sites.

One of the challenges in building a private cloud today is the lack of software standards. However, the Magellan work will employ some of the more popular frameworks that have emerged from the cloud community. Argonne, for instance, will experiment with the Eucalyptus toolkit, an open-source package that is compatible with Amazon Web Services API. The idea is to be able to build a private cloud with the same interface as Amazon EC2.

Apache’s Hadoop and Google’s MapReduce, two related software frameworks that deal with large distributed datasets, will also be evaluated. Like Eucalyptus, Hadoop and MapReduce grew up outside of the HPC world, so currently there’s not much support for them at traditional supercomputing centers. But the notion of large distributed data sets is a feature of many data-intensive scientific codes and is a natural fit for cloud-style computing.

The other aspect of the Magellan effort has to do with experimentation of commercial cloud offerings, such as those from Amazon, Google, and Microsoft. Public clouds, in particular, are attracting a lot of interest due to their ability to offer virtually infinite capacity and elasticity. (Private clouds, because of their smaller size, tend to be seen as fixed resources.) Just as important to the DOE, a public cloud has the allure of offloading the development and maintainence of local infrastructure to someone else.

“Will it be more cost effective for a commercial entity to run a cloud, and presumably make a profit on it, than for the DOE to run their own cloud?” asks Yelick. “That is going to be one of the questions most challenging to answer.”

Some DOE researchers are already giving public clouds a whirl. Argonne’s Jared Wilkening recently tested the feasibility of employing Amazon EC2 to run a metagenomics application (PDF). The BLAST-based code is a nice fit for cloud computing because there is little internal synchronization, therefore it doesn’t rely on high performance interconnects. Nevertheless, the study’s conclusion was that Amazon is significantly more expensive than locally-owned clusters, due mainly to EC2’s inferior CPU hardware and the premium cost associated with on-demand access. Of course, given increased demand for compute-intensive workloads, that could change. Wilkening’s paper was published in Cluster 2009, and slides (PDF) are available on the conference Web site.

The Magellan program is slated to run for two years, with the initial clusters expected to be installed sometime in the next few months. At NERSC, Yelick says the hardware could arrive as early as November, and become operational in December or January. Meanwhile at Argonne, Beckman is already running into researchers who can’t wait to host their codes on the Magellan cloud. “They’re lined up,” he says. “They keep coming down to my office asking when it will be here and how soon they can log in.”

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!

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

March 18, 2024

Nvidia's latest and fastest GPU, code-named Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from its predecessors, including the red-hot H100 and A100 GPUs. 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. While Nvidia may not spring to mind when thinking of the quant Read more…

2024 Winter Classic: Meet the HPE Mentors

March 18, 2024

The latest installment of the 2024 Winter Classic Studio Update Show features our interview with the HPE mentor team who introduced our student teams to the joys (and potential sorrows) of the HPL (LINPACK) and accompany Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the field was normalized for boys in 1969 when the Apollo 11 missi Read more…

Apple Buys DarwinAI Deepening its AI Push According to Report

March 14, 2024

Apple has purchased Canadian AI startup DarwinAI according to a Bloomberg report today. Apparently the deal was done early this year but still hasn’t been publicly announced according to the report. Apple is preparing Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimization algorithms to iteratively refine their parameters until Read more…

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

March 18, 2024

Nvidia's latest and fastest GPU, code-named 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…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimizat Read more…

PASQAL Issues Roadmap to 10,000 Qubits in 2026 and Fault Tolerance in 2028

March 13, 2024

Paris-based PASQAL, a developer of neutral atom-based quantum computers, yesterday issued a roadmap for delivering systems with 10,000 physical qubits in 2026 a Read more…

India Is an AI Powerhouse Waiting to Happen, but Challenges Await

March 12, 2024

The Indian government is pushing full speed ahead to make the country an attractive technology base, especially in the hot fields of AI and semiconductors, but Read more…

Charles Tahan Exits National Quantum Coordination Office

March 12, 2024

(March 1, 2024) My first official day at the White House Office of Science and Technology Policy (OSTP) was June 15, 2020, during the depths of the COVID-19 loc Read more…

AI Bias In the Spotlight On International Women’s Day

March 11, 2024

What impact does AI bias have on women and girls? What can people do to increase female participation in the AI field? These are some of the questions the tech 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…

Analyst Panel Says Take the Quantum Computing Plunge Now…

November 27, 2023

Should you start exploring quantum computing? Yes, said a panel of analysts convened at Tabor Communications HPC and AI on Wall Street conference earlier this y 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…

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…

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…

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

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…

Training of 1-Trillion Parameter Scientific AI Begins

November 13, 2023

A US national lab has started training a massive AI brain that could ultimately become the must-have computing resource for scientific researchers. Argonne N 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…

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…

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…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire