[Beowulf] $1, 279-per-hour, 30, 000-core cluster built on Amazon EC2 cloud

Douglas Eadline deadline at eadline.org
Mon Oct 3 11:17:33 PDT 2011

I think everyone has a similar thoughts, but the presentation
provides some real data and experiences.

BTW, for those interested, I have new poll on ClusterMonkey asking
about clouds and HPC. (http://www.clustermonkey.net/)

The last poll was on GP-GPU use.


> Doug,
> Thanks for posting that video. It confirmed what I always suspected
> about clouds for HPC.
> Prentice
> On 10/03/2011 08:25 AM, Douglas Eadline wrote:
>> Interesting and pragmatic HPC cloud presentation, worth watching
>> (25 minutes)
>>  http://insidehpc.com/2011/09/30/video-the-real-future-of-cloud-computing/
>> --
>> Doug
>>> http://arstechnica.com/business/news/2011/09/30000-core-cluster-built-on-amazon-ec2-cloud.ars
>>> $1,279-per-hour, 30,000-core cluster built on Amazon EC2 cloud
>>> By Jon Brodkin | Published September 20, 2011 10:49 AM
>>> Amazon EC2 and other cloud services are expanding the market for
>>> high-performance computing. Without access to a national lab or a
>>> supercomputer in your own data center, cloud computing lets businesses
>>> spin
>>> up temporary clusters at will and stop paying for them as soon as the
>>> computing needs are met.
>>> A vendor called Cycle Computing is on a mission to demonstrate the
>>> potential
>>> of Amazon’s cloud by building increasingly large clusters on the
>>> Elastic
>>> Compute Cloud. Even with Amazon, building a cluster takes some work,
>>> but
>>> Cycle combines several technologies to ease the process and recently
>>> used
>>> them to create a 30,000-core cluster running CentOS Linux.
>>> The cluster, announced publicly this week, was created for an unnamed
>>> “Top 5
>>> Pharma” customer, and ran for about seven hours at the end of July at
>>> a
>>> peak
>>> cost of $1,279 per hour, including the fees to Amazon and Cycle
>>> Computing.
>>> The details are impressive: 3,809 compute instances, each with eight
>>> cores
>>> and 7GB of RAM, for a total of 30,472 cores, 26.7TB of RAM and 2PB
>>> (petabytes) of disk space. Security was ensured with HTTPS, SSH and
>>> 256-bit
>>> AES encryption, and the cluster ran across data centers in three Amazon
>>> regions in the United States and Europe. The cluster was dubbed
>>> “Nekomata.”
>>> Spreading the cluster across multiple continents was done partly for
>>> disaster
>>> recovery purposes, and also to guarantee that 30,000 cores could be
>>> provisioned. “We thought it would improve our probability of success
>>> if
>>> we
>>> spread it out,” Cycle Computing’s Dave Powers, manager of product
>>> engineering, told Ars. “Nobody really knows how many instances you
>>> can
>>> get at
>>> any one time from any one [Amazon] region.”
>>> Amazon offers its own special cluster compute instances, at a higher
>>> cost
>>> than regular-sized virtual machines. These cluster instances provide 10
>>> Gigabit Ethernet networking along with greater CPU and memory, but they
>>> weren’t necessary to build the Cycle Computing cluster.
>>> The pharmaceutical company’s job, related to molecular modeling, was
>>> “embarrassingly parallel” so a fast interconnect wasn’t crucial.
>>> To
>>> further
>>> reduce costs, Cycle took advantage of Amazon’s low-price “spot
>>> instances.” To
>>> manage the cluster, Cycle Computing used its own management software as
>>> well
>>> as the Condor High-Throughput Computing software and Chef, an open
>>> source
>>> systems integration framework.
>>> Cycle demonstrated the power of the Amazon cloud earlier this year with
>>> a
>>> 10,000-core cluster built for a smaller pharma firm called Genentech.
>>> Now,
>>> 10,000 cores is a relatively easy task, says Powers. “We think
>>> we’ve
>>> mastered
>>> the small-scale environments,” he said. 30,000 cores isn’t the end
>>> game,
>>> either. Going forward, Cycle plans bigger, more complicated clusters,
>>> perhaps
>>> ones that will require Amazon’s special cluster compute instances.
>>> The 30,000-core cluster may or may not be the biggest one run on EC2.
>>> Amazon
>>> isn’t saying.
>>> “I can’t share specific customer details, but can tell you that we
>>> do
>>> have
>>> businesses of all sizes running large-scale, high-performance computing
>>> workloads on AWS [Amazon Web Services], including distributed clusters
>>> like
>>> the Cycle Computing 30,000 core cluster to tightly-coupled clusters
>>> often
>>> used for science and engineering applications such as computational
>>> fluid
>>> dynamics and molecular dynamics simulation,” an Amazon spokesperson
>>> told
>>> Ars.
>>> Amazon itself actually built a supercomputer on its own cloud that made
>>> it
>>> onto the list of the world’s Top 500 supercomputers. With 7,000
>>> cores,
>>> the
>>> Amazon cluster ranked number 232 in the world last November with speeds
>>> of
>>> 41.82 teraflops, falling to number 451 in June of this year. So far,
>>> Cycle
>>> Computing hasn’t run the Linpack benchmark to determine the speed of
>>> its
>>> clusters relative to Top 500 sites.
>>> But Cycle’s work is impressive no matter how you measure it. The job
>>> performed for the unnamed pharma company “would take well over a week
>>> for
>>> them to run internally,” Powers says. In the end, the cluster
>>> performed
>>> the
>>> equivalent of 10.9 “compute years of work.”
>>> The task of managing such large cloud-based clusters forced Cycle to
>>> step
>>> up
>>> its own game, with a new plug-in for Chef the company calls Grill.
>>> “There is no way that any mere human could keep track of all of the
>>> moving
>>> parts on a cluster of this scale,” Cycle wrote in a blog post. “At
>>> Cycle,
>>> we’ve always been fans of extreme IT automation, but we needed to
>>> take
>>> this
>>> to the next level in order to monitor and manage every instance,
>>> volume,
>>> daemon, job, and so on in order for Nekomata to be an efficient 30,000
>>> core
>>> tool instead of a big shiny on-demand paperweight.”
>>> But problems did arise during the 30,000-core run.
>>> “You can be sure that when you run at massive scale, you are bound to
>>> run
>>> into some unexpected gotchas,” Cycle notes. “In our case, one of
>>> the
>>> gotchas
>>> included such things as running out of file descriptors on the license
>>> server. In hindsight, we should have anticipated this would be an
>>> issue,
>>> but
>>> we didn’t find that in our prelaunch testing, because we didn’t
>>> test
>>> at full
>>> scale. We were able to quickly recover from this bump and keep moving
>>> along
>>> with the workload with minimal impact. The license server was able to
>>> keep
>>> up
>>> very nicely with this workload once we increased the number of file
>>> descriptors.”
>>> Cycle also hit a speed bump related to volume and byte limits on
>>> Amazon’s
>>> Elastic Block Store volumes. But the company is already planning bigger
>>> and
>>> better things.
>>> “We already have our next use-case identified and will be turning up
>>> the
>>> scale a bit more with the next run,” the company says. But
>>> ultimately,
>>> “it’s
>>> not about core counts or terabytes of RAM or petabytes of data. Rather,
>>> it’s
>>> about how we are helping to transform how science is done.”
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