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August 10, 2009
Slow Moving Clouds Fast Enough for HPC
Ian Foster penned an interesting blog last week comparing the utility of a supercomputer to that of public cloud for HPC applications. Foster pointed out that while the typical supercomputer might be much faster than a generic cloud environment, the turnaround time might actually be much better for the cloud. He argues that "the relevant metric is not execution time but elapsed time from submission to the completion of execution."
Foster's premise is that the users must wait a significant amount of time for their job to run on a typical supercomputer, while the wait time for a cloud is usually much less. He writes:
For example, let's say we want to run the LU benchmark, which (based on the numbers in Ed's paper) when run on 32 processors takes ~25 secs on the supercomputer and ~100 secs on EC2. Now let's add in queue and startup time:
On EC2, I am told that it may take ~5 minutes to start 32 nodes (depending on image size), so with high probability we will finish the LU benchmark within 100 + 300 = 400 secs.
On the supercomputer, we can use Rich Wolksi's QBETS queue time estimation service to get a bound on the queue time. When I tried this in June, QBETS told me that if I wanted 32 nodes for 20 seconds, the probability of me getting those nodes within 400 secs was only 34%--not good odds.
So, based on the QBETS predictions, if I had to put money on which system my application would finish first, I would have to go for EC2.
This seems especially relevant in the case of shorter HPC jobs. In the example above, the time spent waiting for the system is the dominant metric for both the supercomputer and the cloud. But since the cloud is built for accessibility, it has the advantage over an HPC machine, which is built for raw speed. It's probably safe to say that, in general, the faster the supercomputer, the greater its demand will be, and thus the longer the wait time. If true, the purpose-built supercomputer may only be competitive for longer-running HPC jobs.
From the user's point of view, the key is to be able to estimate service times prior to selecting a platform. Ideally, you would like to be able to negotiate a service level agreement (SLA) dynamically with all potential providers, including your neighborhood supercomputer, at various price points. Right now, of course, this is science fiction. There are currently no standards in place to auto-negotiate SLAs across providers. But it's not too hard to imagine why such a feature would be a great thing for the cloud computing biz.
Posted by Michael Feldman - August 10, 2009 @ 4:10 PM, Pacific Daylight Time
Discussion
There are 2 discussion items posted.
Longer run times
Submitted by JohnWest
on Aug 11, 2009 @ 9:42 AM EDT
Michael - yes, I made exactly that same point about longer run times in this post: http://insidehpc.com/2009/08/05/queue-wait-time-assessing-cloud-hpc-performance/.
In my quick example, EC2 always starts sooner, but 70% of the time or so the supercomputer finishes in about the same time because it runs faster. So your decision could come down to sunk versus ongoing costs.
Post #1
Communication time
Submitted by jnapper
on Sep 25, 2009 @ 5:55 AM EDT
The relevant metric is surely time to completion, and for some types of jobs wait time has a significant effect. However, from our experiments on EC2 we concluded that communication might play a far greater role. As we scaled up the job size, we observed exponential cost increases without a performance increase.
More details are available in:
http://www.cs.utexas.edu/users/pauldj/pubs/uchpc09.pdf
Post #2
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