Learning from Clouds Past: A Look Back at Magellan

By Tiffany Trader

February 1, 2012

In 2009, the US Department of Energy (DOE) launched a bold experiment, a $32 million program to assess the benefit of cloud computing to the scientific community. A distributed testbed infrastructure, named Magellan, was established at the Argonne Leadership Computing Facility (ALCF) and the National Energy Research Scientific Computing Center (NERSC) to provide a tool for computational science in a cloud environment. Magellan, with funding from the American Recovery and Reinvestment Act, was to help major research organizations answer the classic cloud question: is it better to rent or buy?

“What we’re exploring is the question of whether the DOE or other government agencies should be buying their own clusters … or whether those kinds of purchases should be done in a more consolidated way,” said NERSC Director Kathy Yelick in a previous article.

Despite high-hopes and community support, in late 2011, we learned that the Magellan project was being discontinued, leaving many wondering what happened. Now we have some answers in the form of a 169-page report, sponsored by the Department of Energy’s Office of Advanced Scientific Computing Research (ASCR), which funded the study to assess what Magellan tells us about the the role of cloud computing for scientific applications.

Since industry was already benefiting from the cloud model, from the economies of scale generated by a shared pool of network-accessible resources, the Magellan team members initially set out to determine if cloud would hold the same potential for science. As stated in the executive summary:

The goal of Magellan, a project funded through the U.S. Department of Energy (DOE) Office of Advanced Scientific Computing Research (ASCR), was to investigate the potential role of cloud computing in addressing the computing needs for the DOE Office of Science (SC), particularly related to serving the needs of mid-range computing and future data-intensive computing workloads. A set of research questions was formed to probe various aspects of cloud computing from performance, usability, and cost.

Specifically, Magellan was tasked with addressing the following questions:

  • Are the open source cloud software stacks ready for DOE HPC science?
  • Can DOE cyber security requirements be met within a cloud?
  • Are the new cloud programming models useful for scientific computing?
  • Can DOE HPC applications run efficiently in the cloud? What applications are suitable for clouds?
  • How usable are cloud environments for scientific applications?
  • When is it cost effective to run DOE HPC science in a cloud?

It should be noted that Magellan was not a typical commercial cloud, rather this “science cloud” was purpose-built for the special requirements of scientific computing. Magellan was based on the IBM iDataplex chassis using Intel processor cores for a theoretical peak performance of over 100 teraflop/s. Other components include:

  • High bandwidth, low-latency node interconnects (InfiniBand).
  • High-bin processors tuned for performance.
  • Preinstalled scientific applications, compilers, debuggers, math libraries and other tools.
  • High-bandwidth parallel file system.
  • High-capacity data archive.

During Magellan’s two-year run, the staff at NERSC and Argonne National Laboratory examined how different aspects of cloud computing infrastructure and technologies could be harnessed by various scientific applications. They evaluated cloud models such as Infrastructure as a Service (IaaS) and Platform as a Service (Paas), virtual software stacks, MapReduce and open-source implementation (Hadoop), as well as resource provider and user perspectives.

Using a wide-range of applications as benchmarks, the researchers compared the Magellan cloud with various other architectures, including a Cray XT4 supercomputer, a Dell cluster system, and Amazon’s EC2 commercial cloud offering. Despite the testbed moniker, a lot of important production science took place, contributing to advances in particle physics, climate research, quantum chemistry, plasma physics and astrophysics.

Science workloads, by their nature, tend to be cloud-challenged, although to varying degrees. The report outlines the three major classifications of computational models, beginning with large-scale tightly-coupled science codes, which require the power of traditional supercomputers and take a big penalty working in a virtualized cloud environment. Then, there are the mid-range tightly-coupled applications, which run at a smaller scale and tend to be good candidates for cloud, although there is some performance loss. The final category, high-throughput workloads, usually involve asynchronous, independent computations, and in the past relied on desktop and small clusters for processing. But due to an explosion in sensor data, cloud is a good fit, especially when you factor in the fact that these high-throughput and data-intensive workloads do not fit into current scheduling and allocation policies.

The two-year Magellan project led to these key findings:

  • Scientific applications have special requirements that require cloud solutions that are tailored to these needs.
  • Scientific applications with minimal communication and I/O are best suited for clouds.
  • Clouds require significant programming and system administration support.
  • Significant gaps and challenges exist in current open-source virtualized cloud software stacks for production science use.
  • Clouds expose a different risk model requiring different security practices and policies.
  • MapReduce shows promise in addressing scientific needs, but current implementations have gaps and challenges.
  • Public clouds can be more expensive than in-house large systems. Many of the cost benefits from clouds result from the increased consolidation and higher average utilization.
  • DOE supercomputing centers already achieve energy efficiency levels comparable to commercial cloud centers.
  • Cloud is a business model and can be applied at DOE supercomputing centers.

From this list, it is apparent that cloud was unable to measure up to a centralized supercomputer system in many ways, but the delivery model does have its place. According to the report, “users with applications that have more dynamic or interactive needs could benefit from on-demand, self-service environments and rapid elasticity through the use of virtualization technology, and the MapReduce programming model to manage loosely coupled application runs.”

In other words, cloud excels when it comes to flexibility and responsiveness. In fact, the report found that “for users who need the added flexibility offered by the cloud computing model, additional costs may be more than offset by the increased flexibility. Furthermore, in some cases the potential for more immediate access to compute resources could directly translate into cost savings.”

However, when it comes to the potential cost savings of using a public cloud versus the costs of hardware acquisition, the report makes the point that DOE procurement costs are often significantly discounted, which offsets some of the potential savings:

Existing DOE centers already achieve many of the benefits of cloud computing since these centers consolidate computing across multiple program offices, deploy at large scales, and continuously refine and improve operational efficiency. Cost analysis shows that DOE centers are cost competitive, typically 3-7x less expensive, when compared to commercial cloud providers. Because the commercial sector constantly innovates, DOE labs and centers should continue to benchmark their computing cost against public clouds to ensure they are providing a competitive service.

“Cloud computing is ultimately a business model,” state the authors. “But cloud models often provide additional capabilities and flexibility that are helpful to certain workloads. DOE labs and centers should consider adopting and integrating these features of cloud computing into their operations in order to support more diverse workloads and further enable scientific discovery, without sacrificing the productivity and effectiveness of computing platforms that have been optimized for science over decades of development and refinement.”

The authors further suggest that when an integrated approach is not sufficient, a private cloud solution should be considered based on its ability to provide many of the benefits of commercial clouds while avoiding some of the pitfalls, such as security, data management, and performance penalties.

To recap: cloud services are a good complement to centralized computing resources, but not a replacement. This should not come as a surprise to our community. This is HPC, high-performance computing, and whenever you add additional layers, i.e., virtualization, the application takes a performance hit. However, as the report makes clear, there are good use cases for cloud services, such as “scientific groups needing support for on-demand access to resources, sudden surges in resource needs, customized environments, periodic predictable resource needs (e.g., monthly processing of genome data, nightly processing of telescope data), or unpredictable events such as computing for disaster recovery.” The report goes on to note that “cloud services essentially provide a differentiated service model that can cater to these diverse needs, allowing users to get a virtual private cluster with a certain guaranteed level of service.”

Magellan was billed as an exploratory project, set to go for two years. In fact, the project was named Magellan in honor of the Portuguese explorer Fernão de Magalhães, the first person to lead an expedition across the Pacific. The original “clouds of Magellan” refers to two small galaxies in the southern sky. The current-day Magellan, as the first major scientific cloud testbed, also navigated uncharted waters and documented the journey for the benefit of future generations.

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!

HPE Launches Apollo 6500 Gen10 System as Part of AI Solution Push

March 21, 2018

HPE today announced the latest rev of its HPE Apollo 6500 platform, Gen10, along with a spate of new AI-oriented offerings designed to help customers optimize and scale up their AI and deep learning usage. Like is Gen Read more…

By Tiffany Trader

IBM Touts OpenPOWER Ecosystem, Announces New Customers, Products for AI and Hyperscale

March 20, 2018

At SC17 in Denver four months ago, Ken King, GM, OpenPOWER, IBM Systems Group, told a somewhat jaundiced trio of journalists that 2018 would, finally, after several years of expectations, be the year OpenPOWER and IBM’ Read more…

By Doug Black

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate scientists the ability to use machine learning to identify e Read more…

By Rob Farber

HPE Extreme Performance Solutions

Harness the Full Power of HPC Servers with an Effective Cooling Approach

High performance computing (HPC) innovation is rapidly transforming the way we operate – with an onslaught of cutting-edge technologies designed to optimize applications and workloads, increase productivity, and enable better business outcomes. Read more…

IBM Unveils New Cloud for Data Science and Engineering

March 19, 2018

Days ahead of its inaugural IBM Think mega-event, the multinational tech mainstay on Friday (March 16) unveiled a new cloud offering called Cloud Private Data that’s designed to help organizations utilize data science Read more…

By Alex Woodie

HPE Launches Apollo 6500 Gen10 System as Part of AI Solution Push

March 21, 2018

HPE today announced the latest rev of its HPE Apollo 6500 platform, Gen10, along with a spate of new AI-oriented offerings designed to help customers optimize a Read more…

By Tiffany Trader

IBM Touts OpenPOWER Ecosystem, Announces New Customers, Products for AI and Hyperscale

March 20, 2018

At SC17 in Denver four months ago, Ken King, GM, OpenPOWER, IBM Systems Group, told a somewhat jaundiced trio of journalists that 2018 would, finally, after sev Read more…

By Doug Black

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate Read more…

By Rob Farber

How the Cloud Is Falling Short for HPC

March 15, 2018

The last couple of years have seen cloud computing gradually build some legitimacy within the HPC world, but still the HPC industry lies far behind enterprise I Read more…

By Chris Downing

Stephen Hawking, Legendary Scientist, Dies at 76

March 14, 2018

Stephen Hawking passed away at his home in Cambridge, England, in the early morning of March 14; he was 76. Born on January 8, 1942, Hawking was an English theo Read more…

By Tiffany Trader

Hyperion Tackles Elusive Quantum Computing Landscape

March 13, 2018

Quantum computing - exciting and off-putting all at once - is a kaleidoscope of technology and market questions whose shapes and positions are far from settled. Read more…

By John Russell

Part Two: Navigating Life Sciences Choppy HPC Waters in 2018

March 8, 2018

2017 was not necessarily the best year to build a large HPC system for life sciences say Ari Berman, VP and GM of consulting services, and Aaron Gardner, direct Read more…

By John Russell

Google Chases Quantum Supremacy with 72-Qubit Processor

March 7, 2018

Google pulled ahead of the pack this week in the race toward "quantum supremacy," with the introduction of a new 72-qubit quantum processor called Bristlecone. Read more…

By Tiffany Trader

Inventor Claims to Have Solved Floating Point Error Problem

January 17, 2018

"The decades-old floating point error problem has been solved," proclaims a press release from inventor Alan Jorgensen. The computer scientist has filed for and Read more…

By Tiffany Trader

Japan Unveils Quantum Neural Network

November 22, 2017

The U.S. and China are leading the race toward productive quantum computing, but it's early enough that ultimate leadership is still something of an open questi Read more…

By Tiffany Trader

Researchers Measure Impact of ‘Meltdown’ and ‘Spectre’ Patches on HPC Workloads

January 17, 2018

Computer scientists from the Center for Computational Research, State University of New York (SUNY), University at Buffalo have examined the effect of Meltdown Read more…

By Tiffany Trader

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit a Read more…

By Tiffany Trader

Fast Forward: Five HPC Predictions for 2018

December 21, 2017

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Read more…

By John Russell

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. 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

Chip Flaws ‘Meltdown’ and ‘Spectre’ Loom Large

January 4, 2018

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. T Read more…

By Tiffany Trader

Leading Solution Providers

GlobalFoundries, Ayar Labs Team Up to Commercialize Optical I/O

December 4, 2017

GlobalFoundries (GF) and Ayar Labs, a startup focused on using light, instead of electricity, to transfer data between chips, today announced they've entered in Read more…

By Tiffany Trader

How Meltdown and Spectre Patches Will Affect HPC Workloads

January 10, 2018

There have been claims that the fixes for the Meltdown and Spectre security vulnerabilities, named the KPTI (aka KAISER) patches, are going to affect applicatio Read more…

By Rosemary Francis

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC thinking of booth visitors. Attendees of tutorials, workshops and other technical sessions will be inundated with requests for feedback. Read more…

By Andrew Jones

V100 Good but not Great on Select Deep Learning Aps, Says Xcelerit

November 27, 2017

Wringing optimum performance from hardware to accelerate deep learning applications is a challenge that often depends on the specific application in use. A benc Read more…

By John Russell

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with par Read more…

By Tiffany Trader

AMD Wins Another: Baidu to Deploy EPYC on Single Socket Servers

December 13, 2017

When AMD introduced its EPYC chip line in June, the company said a portion of the line was specifically designed to re-invigorate a single socket segment in wha Read more…

By John Russell

World Record: Quantum Computer with 46 Qubits Simulated

December 18, 2017

Scientists from the Jülich Supercomputing Centre have set a new world record. Together with researchers from Wuhan University and the University of Groningen, Read more…

New Blueprint for Converging HPC, Big Data

January 18, 2018

After five annual workshops on Big Data and Extreme-Scale Computing (BDEC), a group of international HPC heavyweights including Jack Dongarra (University of Te Read more…

By John Russell

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