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!

Tuning InfiniBand Interconnects Using Congestion Control

July 26, 2017

InfiniBand is among the most common and well-known cluster interconnect technologies. However, the complexities of an InfiniBand (IB) network can frustrate the most experienced cluster administrators. Maintaining a balan Read more…

By Adam Dorsey

NSF Project Sets Up First Machine Learning Cyberinfrastructure – CHASE-CI

July 25, 2017

Earlier this month, the National Science Foundation issued a $1 million grant to Larry Smarr, director of Calit2, and a group of his colleagues to create a community infrastructure in support of machine learning research Read more…

By John Russell

DARPA Continues Investment in Post-Moore’s Technologies

July 24, 2017

The U.S. military long ago ceded dominance in electronics innovation to Silicon Valley, the DoD-backed powerhouse that has driven microelectronic generation for decades. With Moore's Law clearly running out of steam, the Read more…

By George Leopold

HPE Extreme Performance Solutions

HPE Servers Deliver High Performance Remote Visualization

Whether generating seismic simulations, locating new productive oil reservoirs, or constructing complex models of the earth’s subsurface, energy, oil, and gas (EO&G) is a highly data-driven industry. Read more…

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in 2017 with scale-up production for enterprise datacenters and Read more…

By Tiffany Trader

Tuning InfiniBand Interconnects Using Congestion Control

July 26, 2017

InfiniBand is among the most common and well-known cluster interconnect technologies. However, the complexities of an InfiniBand (IB) network can frustrate the Read more…

By Adam Dorsey

NSF Project Sets Up First Machine Learning Cyberinfrastructure – CHASE-CI

July 25, 2017

Earlier this month, the National Science Foundation issued a $1 million grant to Larry Smarr, director of Calit2, and a group of his colleagues to create a comm Read more…

By John Russell

Graphcore Readies Launch of 16nm Colossus-IPU Chip

July 20, 2017

A second $30 million funding round for U.K. AI chip developer Graphcore sets up the company to go to market with its “intelligent processing unit” (IPU) in Read more…

By Tiffany Trader

Fujitsu Continues HPC, AI Push

July 19, 2017

Summer is well under way, but the so-called summertime slowdown, linked with hot temperatures and longer vacations, does not seem to have impacted Fujitsu's out Read more…

By Tiffany Trader

Researchers Use DNA to Store and Retrieve Digital Movie

July 18, 2017

From abacus to pencil and paper to semiconductor chips, the technology of computing has always been an ever-changing target. The human brain is probably the com Read more…

By John Russell

The Exascale FY18 Budget – The Next Step

July 17, 2017

On July 12, 2017, the U.S. federal budget for its Exascale Computing Initiative (ECI) took its next step forward. On that day, the full Appropriations Committee Read more…

By Alex R. Larzelere

Women in HPC Luncheon Shines Light on Female-Friendly Hiring Practices

July 13, 2017

The second annual Women in HPC luncheon was held on June 20, 2017, during the International Supercomputing Conference in Frankfurt, Germany. The luncheon provid Read more…

By Tiffany Trader

Satellite Advances, NSF Computation Power Rapid Mapping of Earth’s Surface

July 13, 2017

New satellite technologies have completely changed the game in mapping and geographical data gathering, reducing costs and placing a new emphasis on time series Read more…

By Ken Chiacchia and Tiffany Jolley

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 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

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. Just how close real-wo 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 a Read more…

By Tiffany Trader

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 cam 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

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a 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

Leading Solution Providers

How ‘Knights Mill’ Gets Its Deep Learning Flops

June 22, 2017

Intel, the subject of much speculation regarding the delayed, rewritten or potentially canceled “Aurora” contract (the Argonne Lab part of the CORAL “ Read more…

By Tiffany Trader

Reinders: “AVX-512 May Be a Hidden Gem” in Intel Xeon Scalable Processors

June 29, 2017

Imagine if we could use vector processing on something other than just floating point problems.  Today, GPUs and CPUs work tirelessly to accelerate algorithms Read more…

By James Reinders

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurrencies like Bitcoin, along with classified government communications and other sensitive digital transfers. Read more…

By Doug Black

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

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of Read more…

By Alex Woodie

Six Exascale PathForward Vendors Selected; DoE Providing $258M

June 15, 2017

The much-anticipated PathForward awards for hardware R&D in support of the Exascale Computing Project were announced today with six vendors selected – AMD Read more…

By John Russell

Top500 Results: Latest List Trends and What’s in Store

June 19, 2017

Greetings from Frankfurt and the 2017 International Supercomputing Conference where the latest Top500 list has just been revealed. Although there were no major Read more…

By Tiffany Trader

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