Uncovering Results in the Magellan Testbed

By Nicole Hemsoth

June 22, 2010

While it’s getting easier to find case studies of cloud deployments in the enterprise, cloud deployments in the scientific computing arena are a bit more nebulous to track with some exceptions. Accordingly, those in the scientific computing community who are looking for news about cloud computing are paying close attention to the Magellan testbed, which is set to deliver some results that will be of value as researchers tackle the tough question of buying time versus investing in their own private clusters.

The Magellan cloud computing project is delivering some interesting results as it continues to alter the cloud environment in order to take different cloud models to task. The National Energy Research Scientific Computing Centers (NERSC) in conjunction with Argonne National Laboratory launched a computational cloud testbed called Magellan in October, 2009 with funds from the American Recovery and Reinvestment Act via the U.S. Department of Energy. The goal of the joint effort is to look at the cost and energy benefits and drawbacks to the cloud computing paradigm for scientists, specifically those working on government-funded projects. The range of application areas that are either already being explored or are set to enter the cloud covers several scientific computing arenas, including genomics and climate research and applied mathematics.

To evaluate the current progress and challenges for Magellan users and NERSC, HPC in the Cloud discussed the status of the project with NERSC Director, Kathy Yelick.

HPCc: As a testbed, what variables will be added or subtracted on a hardware and application level to test for differences in performance and benchmarking?

Yelick: The actual testbed is static in the hardware sense; we’ve installed a cluster that’s the hardware basis for the cloud testbed and that’s IBM iDataPlex System with an InfiniBand network and Nehelem processors, which is really the high end of cloud when you compare it to what you’d see in a commercial setting. From a software perspective we’ll be doing a lot of experimentation with different types of virtualization and different uses of operating system virtualization. We’ll also be looking at some of the programming models that are available in cloud computing, including the Hadoop implementation of the map reduce program model idea and we’ll also be looking at different configurations of the system to provide people with either the idea of virtual clusters or more of a shared resource environment where the boundaries between the jobs are more dynamic.

HPCc: For now it seems that there is a distinct focus on genomics research given your collaboration with the Joint Genome Institute (JGI) but from your initial releases about the aims of Magellan it appeared that there would be a broader focus. What other scientific areas will be you examining?

Yelick: We are actually looking at a broader DOE science focus, but just it turned out there was immediate need for some work in the genomics area so we set up a virtual cluster within our cloud testbed for the Joint Genome Institute, which was a short-term project. They’re still using that virtual cloud but it will be trailing off in the next few months as they install some of their own hardware. After that we’ll be looking at some other science projects in high energy physics, applied mathematics, and some climate data analysis. There’s a project in the Earth Systems Grid that’s looking at climate data; it’s not a climate modeling simulation platform, it’s more of a data analysis platform. So in addition to the compute tests we also bought close to a petabyte of disk storage to create a storage cloud that’s integrated into our file system.

HPCc: What are some of the results you’ve seen thus far in terms of your goals of examining overall energy-efficiency and cost effectiveness and what goals or comparison points do you have to determine overall efficiency?

Yelick: We selected an energy-efficient system, IBM iDataPlex Linux Cluster with liquid cooled doors, which is very energy-efficient and did some novel things in the installation of that system to pack it into a tighter space and make more effective use of the cooling system. We’re actually using water that’s returned from other computers in the system that go into the cooling system, which allows us to save energy.

It’s hard to do an energy efficiency comparison to Amazon for example because they don’t open their configurations to the public but we’re always looking for ways to make it more energy efficient. Our real comparison point is not to the commercial clouds, but to private clusters that individual researchers go out and purchase for their scientific applications. So what we’re exploring is the question of whether the DOE or other government agencies should be buying their own clusters (they’ll go out and buy a rack or even a system of 64 nodes, for instance to run their own scientific applications on) or whether those kinds of purchases should be done in a more consolidated way. In other words, we’re looking at the efficiency of running private separate clusters that are run by individual researchers throughout the lab and university system compared to a setup like what we have at NERSC, which is a consolidated testbed.

HPCc: In one of your releases about the use of Amazon EC2 for the metagenomics project, it seemed that there were some pricing surprises that you didn’t anticipate, which might mean that there are some unexpected underlying issues in public clouds for scientific users?

Yelick: It is true that what we found is that there are some costs in the commercial clouds that are not as obvious when you just look at the pricing models. Those costs can also include applications running more slowly on a shared environment with a relatively low-speed network (Ethernet networks rather than our InfiniBand network). Scientific applications that are using more than one processor per job can run much more slowly in an environment like that because of the network performance, which we think is the most significant factor, but also perhaps because of the sharing of the system and the virtualization. If you just look at a price per CPU-hour, you need to be careful because you really want to look at the application performance as well.

The other big factor is the storage, which you pay for separately. For instance, in climate modeling in our experience, there’s a lot of data storage and manipulation that goes along with this application; it’s not just a computationally-intensive problem. You really have to look at both the cost of the storage but also the type of bandwidth you get from say a storage cloud into a compute cloud if you’re doing data analysis. Those are some of the places where I think that some of the commercial options are not really configured for high-parallel I/O bandwidth between the storage and compute clouds. Moving massive scientific datasets around is not really what these are optimized for. Then again, we’re really looking at a different workload than those in the commercial setting—that’s one of the things we were trying to understand—to what extent can the systems be identical and in what ways do they need to be configured differently for a scientific environment.

HPCc: One of the big issues for scientific users is application performance—what have you noticed in this area; in other words, which scientific applications seem best-suited for the cloud? What have developers noticed?

Yelick: BLAST is one example of an application we’ve looked at on a number of systems. I think in terms of the application development there are advantages and disadvantages of the cloud model and what I’m referring to here is really the virtualization model. The advantage is that it is really flexible because you can choose an OS version you’re going to install and run but then again, as an application developer you’re also responsible for doing that in some sense in a hardware as a service model—you get the raw hardware but then you need to configure your environment with your operating system environment, your libraries, and so on. For an application like BLAST, which is an application that has a tremendous amount of throughput required and runs many jobs per day throughout the year, the time to configure a system like that for a cloud environment makes a lot of sense. With that done we’ve been able to run some of the metagenomics pipelines on this cloud environment. There are positives and negatives—some of the users, I was talking to someone looking at detector data in a physics application area—in that case they wanted the control you get from a cloud environment, that is, they wanted the ability to run a particular version of the operating system so they could go back and run versions of the application that had been developed several years ago in order to do validation against current versios of the code. That’s a big attraction.

I should also mention that the Hadoop programming model is something we have used a lot on the Berkeley campus, we are just in the process of being able to provide that to the users on the NERSC testbed. There’s been some work in the case of BLAST on top of Hadoop.

HPCc: Although this it is still early to get an overall picture of the suitability of cloud for scientific computing, what are some of the more surprising findings thus far, especially as they relate to any expectations or benchmarks you might have had in mind before the launch of Magellan?

Yelick: I think the biggest is the difference in performance is visible even when running fairly modest-sized applications across different cloud environments; especially when looking at pricing models—what can seem like a very attractive environment and can actually be very effective at something like the BLAST workload, which is basically independent serial jobs in large numbers, that are running on that kind of environment—each one s running independently, that works very well in a lot of different environments both commercial and on our in-house cluster and would probably also work well on a lower-cost cluster.

But looking at some of the other kinds of scientific applications, even at fairly modest job sizes, there are significant performance differences between running a batch schedule system where jobs run in a synchronous manner across a sub-cluster and on a higher-speed network and in an environment that is not designed for that kind of synchronous parallel work, which is what you get in a commercial cloud.

The other thing has been the sociological question of what it is that the scientists find attractive about cloud computing. This is less quantitative, but having talked to various scientific groups about what makes cloud more attractive than what we already have for scientists, I would say the first issue is really the control of the time—the primary reason why they go out and buy their own hardware in the first palce. It’s really a scheduling issue. It’s about how heavily utilized a system is.

The effect would be a system that’s not as well utilized as our other systems at NERSC (which are around 95% utilized) and if you give a sub-cluster of 64 nodes to a science group, most likely they’re not going to run that full on throughout the year. So there is an interesting question about the utilization of systems, which then goes back to energy efficiency. You need to look at the work that gets done per kilowatt of energy that’s used as opposed to looking at it as how efficient the computer system is.

Another thing is that there is some interest in the virtualization for some of these groups that want to run particular OS versions because, for example they’re running large international project and there are particular software version requirements—the map reduce model is also interesting to some, often who have fairly independent kinds of serial work they want to perform and I think some of the data analysis problems such as genomics will fit in that category as well. Other data analysis problems, including detector data (coming out of CERN for instance or the Earth Systems Grid) those are massively serial jobs, and there are a lot in the data analysis area, those will be the best examples for the cloud environment but that depends on us having an architecture that provides the high-speed I/O between the storage and compute part of the cloud.

HPCc: To expand on that, do you think it’s still too early for large-scale scientific computing in the cloud? Better yet, do you think it’s too early for HPC in the cloud?

Yelick: I would rephrase that ask how useful is cloud to scientific computation–and I think there’s a part of the workload in scientific computing that’s well-suited to the cloud, but it’s not the HPC end, it’s really the bulk aggregate serial workload that often comes up in scientific computing that is not really the traditional arena of high-performance computing. If you look at some of the commercial offerings like SGI’s cloud cluster they’re certainly providing a system and environment that will be competitive for HPC and then it will come down to cost issues—how to most cost effectively run systems and the question of the service level and what the scientists are willing to go pay for versus want to have.

There are lots of questions about anything other than the large serial work for a cloud environment —the biggest sticking point in the cloud is the integration of the network (having a high-speed network that allows you to run parallel work and along with that being able to schedule parallel jobs in a batch way so they can do frequent synchronization across the parallel job.

This can be overcome; we look at cloud as business model. It’s not about HPC and clouds it’s about the individual private clusters that people are still buying versus cloud. It’s really about trying to figure out if you can get rid of the private clusters and replace that with a cloud environment.

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!

Musk’s Latest Startup Eyes Brain-Computer Links

April 21, 2017

Elon Musk, the auto and space entrepreneur and severe critic of artificial intelligence, is forming a new venture that reportedly will seek to develop an interface between the human brain and computers. Read more…

By George Leopold

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

Nvidia P100 Shows 1.3-2.3x Speedup Over K80 GPU on Financial Apps

April 20, 2017

When it comes to the true performance of the latest silicon, every end user knows that the best processor is the one that works best for their application. Read more…

By Tiffany Trader

Quantum Adds Global Smarts to StorNext File System

April 20, 2017

Companies that use Quantum’s StorNext platform to store massive amounts of data this week got a glimpse of new storage capabilities that should make it easier to access their data horde from anywhere in the world. Read more…

By Alex Woodie

HPE Extreme Performance Solutions

HPC-Driven Weather Simulations Improving Forecasting Capabilities

In September of 1938, a massive hurricane traversed the Atlantic Ocean and made landfall in New England. Due to inadequate and incorrect forecasting, the storm struck farther north and with greater intensity than had been predicted, leaving residents and authorities with virtually no warning or time to properly prepare. Read more…

Scaling an HPC Career in Nepal Can Be a Steep Climb

April 20, 2017

Umesh Upadhyaya works as an IT Associate at the International Centre for Integrated Mountain Development (ICIMOD) in Nepal, which supports the country’s one and only HPC facility. He is directly involved in an initiative that focuses on climate change and atmosphere modeling Read more…

By Nages Sieslack

Hyperion (IDC) Paints a Bullish Picture of HPC Future

April 20, 2017

Hyperion Research – formerly IDC’s HPC group – yesterday painted a fascinating and complicated portrait of the HPC community’s health and prospects at the HPC User Forum held in Albuquerque, NM. HPC sales are up and growing ($22 billion, all HPC segments, 2016). Read more…

By John Russell

Intel Open Sources All Lustre Work, Brent Gorda Exits

April 19, 2017

In a letter to the Lustre community posted on the Intel website, Vice President of Intel's Data Center Group Trish Damkroger writes that effective immediately the company will be contributing all Lustre development to the open source community. Damkroger also announced that Brent Gorda, General Manager, High Performance Data Division at Intel is leaving the company. Read more…

By Tiffany Trader

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

Hyperion (IDC) Paints a Bullish Picture of HPC Future

April 20, 2017

Hyperion Research – formerly IDC’s HPC group – yesterday painted a fascinating and complicated portrait of the HPC community’s health and prospects at the HPC User Forum held in Albuquerque, NM. HPC sales are up and growing ($22 billion, all HPC segments, 2016). Read more…

By John Russell

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" processors. The infrastructure is based on the 68-core Intel Knights Landing processor with integrated Omni-Path fabric (the 7250F Xeon Phi). Read more…

By Tiffany Trader

CERN openlab Explores New CPU/FPGA Processing Solutions

April 14, 2017

Through a CERN openlab project known as the ‘High-Throughput Computing Collaboration,’ researchers are investigating the use of various Intel technologies in data filtering and data acquisition systems. Read more…

By Linda Barney

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of “quantum supremacy,” researchers are stretching the limits of today’s most advanced supercomputers. Read more…

By Tiffany Trader

Penguin Takes a Run at the Big Cloud Providers

April 12, 2017

HPC specialist Penguin Computing recently re-ran benchmarks from a study of its larger brethren and says the results show its ‘public cloud’ – Penguin on Demand (POD) – is among the leaders in cost and performance. Read more…

By John Russell

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

HPC and the Colocation Datacenter – a Bridge Too Far?

April 7, 2017

A more standardised HPC platform approach is making the running of HPC projects within increasing financial reach. Read more…

By Clive Longbottom, Quocirca

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 phase of neural networks (NN). Read more…

By Tiffany Trader

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 phase of neural networks (NN). 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. Read more…

By John Russell

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 campaign. 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 assets. 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

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. 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

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Leading Solution Providers

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

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