Supernova Factory Employs EC2, Puts Cloud to the Test

By Nicole Hemsoth

July 9, 2010

There is something thrilling about the very term “supernova factory” in that it invokes startling mental images culled from science fiction and our own imaginations. However, the real factory in question here is at the heart of an international research collaboration, although not one in the business of mass-producing supernovas in some kind of cosmic warehouse. It is instead examining the nature of dark energy to understand a “simple” concept — the expanding universe.

The universe is growing rapidly due to what physicists have dubbed as dark energy — a finding that was made possible by comparing the relative brightness of “close” supernovae to the brightness of those much farther in the distance (which culminates in the difference of several billion years). The comparison is not possible without understanding the underlying physics that produced the supernovae that is the nearest, which is where the Nearby Supernova Factory (SNfactory) enters the picture. The project relies on a complicated “pipeline of serial processes that execute various image processing algorithms on approximately 10Tbs of data” to step closer to understanding dark energy and its role in the universe’s constant expansion.

While all of this is interesting enough on its own, the project has a particularly unique HPC and cloud slant due to the efforts of Berkeley researcher Lavanya Ramakrishnan and her team. They have been able to shed light on how a public cloud like EC2 can (and cannot) be used for some scientific computing applications by bringing SNfactory’s pipeline to the cloud. During a recent chat with Ramakrishnan, it became clear that while there are attractive features of clouds, there are some hurdles that relate to just the issues that most concern scientific users, including performance, reliability, as well as ease of use and configuration.

In her research that spans beyond this particular project’s scope, Lavanya Ramakrishnan focuses directly on topics related to finding ways to handle scientific workloads that are reliant on high performance and distributed systems. Accordingly, she has looked extensively at the possibilities of deploying clouds to handle scientiic workloads as well as considering grid technologies and their relevant role in the area.

The SNfactory cloud computing evaluation project in question is important as it provides not only a case study of using HPC in a public cloud, but also because of the specificity of design tests to maximize performance outside of the physical infrastructure. The paper presenting their findings, entitled “Seeking Supernovae in the Clouds: A Performance Study,” won the top honor at the First Workshop on Scientific Cloud Computing this summer. This is not a surprise as the paper provides an in-depth examination of the benefits and drawbacks of public clouds in specific context along with detailed descriptions of the various configurations that produced their conclusions.

Getting Scientific Computing Off the Ground

Until just recently, the Supernova Factory’s complex pipeline was fed into a local cluster. With the oversight and alterations on the part of Berkeley researchers to refine the environment from application-level up, the pipeline was fed into a Amazon’s EC2 after significant experimentation, all of which is discussed at length in the paper. These experimental designs were for the specific purpose of determining what options were available on a design level to suit application data placement and more generally, to provide a distinct view of the performance results in a virtualized cluster environment.

Overall, the authors concluded that “cloud computing offers many features that make it an attractive alternative. The ability to completely control the software environment in a cloud is appealing when dealing with a community-developed science pipeline with many unique library and platform requirements.” While this is a bright statement about the use of the cloud for a project like this, according to Lavanya Ramakrishnan, who spoke with HPC in the Cloud recently about the results of the Berkeley team’s work, the cloud, at least as offered by EC2 is not an out of the box solution for scientific computing users and there were a number of challenges along the way that present some meaty discussion bits for those who debate that the cloud is not ready for HPC.

Ramakrishnan is not the first scientific HPC user to comment on the complexity that is involved when first preparing to send applications into the cloud and setting up the environment. She noted that while it was difficult to determine how long it took them to get started since their purpose was to test multiple designs and models, she advised that it was not a quick or easy process. Before even getting to the point where one would be ready to make the leap, there would have to be exhaustive research about how to best tailor their environment to the specific applications.

In addition to being a complex task to undertake, once the ideal environment is created and the applications and virtual machines have been synched into what might appear to be the best configuration, there are also some troubles with the predictable enemies of HPC and cloud — performance and reliability. The authors of the study encountered a number of failures throughout their experiments with EC2 that would not have been matters of concern with the traditional environment. As Ramakrishnan stated, “A lot of these [scientific] applications have not been designed with these commodity clusters in mind so the reliability issue, which wasn’t a major problem before, is now important.”

The Big Picture for Scientific Computing in the Public Cloud

The full paper provides deep specifics for those looking to design their cloud environment for scientific computing that can be of immense value and save a great deal of time and frustration. It is critical reading for anyone looking to use the cloud for similar (although chances are, on much smaller-scale) workloads.

What is important here in the scientific computing sense bears repeating. There are many questions about the suitability of public clouds for HPC-type applications and while there are many favorable experiences that bode well for the future of this area, some of the barriers and problems need to be addressed in a major way before the clouds will be a paradigm shift for scientific computing.

Ramakrishnan, who as it was noted earlier, spends much of her research time investigating alternatives to traditional HPC, sees how cloud computing is a promising technology in theory for researchers. For instance, as she noted, in physical environments “applications suffer because the people running the machines need to upgrade their packages and software to run in these environments. Sometimes there are compatibility issues and this gets even more complicated when they have collaborations across groups because everyone needs to upgrade to a different version. Software maintenance becomes a big challenge. Cloud has therefore become attractive to a lot of scientific computing users, including the Supernova Factory — cloud lets them maintain this entire stack they need and this alone is very attractive.”

Based on her experiences using a number of different configurations and models for cloud in scientific computing, Ramakrishnan indicated that while there is a class of scientific applications that are well-suited to the cloud, there are indeed many challenges. Furthermore, the important point is that researchers understand that this solution, even if the applications fit well with clouds, cannot be undertaken lightly. A great deal of preparation is required, especially if one is operating on the large scale, before making the leap into the cloud.

Scientific computing and cloud computing are not at odds; they live on the same planet but there is a vast ocean that separates the two at this point — at least if we are talking about public clouds. Performance and reliability — two keys to successfully running applications on bare metal systems — are in question in the public cloud and until ideal configurations can be presented across a wide range of application types more research like that being performed by Ramakrishnan and her colleagues is critical.

Many of the points that Ramakrishnan made about the suitability of the public cloud at large for this kind of workload correspond with what Kathy Yelick discussed in an overview of current progress at the Magellan Testbed, another research endeavor out of Berkeley. The consensus is that there is promise — but only for certain types of applications — at least until more development on the application and cloud levels takes place.

Still, Amazon insists with great ferocity that the future of scientific computing lies in their cloud offering, and this is echoed by Microsoft and others with Azure and EC2-like services. Until the scientific computing community fully experiments with the public cloud to determine how best to configure the enviornment for their applications, we will probably hear a great deal more conflicting information about the suitability of the public clouds for large-scale scientific workloads.

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