Virginia Tech unveils HokieSpeed, a powerful new supercomputer for the masses | Hacker News Reader

Virginia Tech unveils HokieSpeed, a powerful new supercomputer for the masses

vtnews.vt.edu

49 pointsalexobenauer17 comments

I don't understand these articles that assume you understand what "two 2.40-gigahertz Intel Xeon E5645 6-core... and two NVIDIA M2050/C2050 448-core .... which reside on a Supermicro 2026GT0TRF motherboard" means, but didn't already know what "CPU" and "GPU" stand for.

As a Hokie, this is good but SystemX was way more revolutionary. At the time, supercomputers weren't built on consumer hardware and the fact that it was in the top 5 (3?) with a bunch of G5s was like a shockwave. Basically, COTS supercomputers had finally arrived when SystemX came out.

As another VT alum (two times over, and the second time being last year), I can tell you that what they're probably most interested in is the energy efficiency. Feng's group does research with both GPUs and power efficiency. Wu Feng and Kirk Cameron (also at VT) were the ones who started the Green500 list.

I've noticed the last few years of supercomputer announcements has focused less on raw performance and more balanced performance/energy.

Small world, I'm also a '03 alum. I was just going through my address book and a Scott S. was in the list with a 540 area code. We might have been in a study group at one point.

Beowulf cluster? ASCI RED (sort of)?

ASCI Red was a supercomputer built for Sandia by Intel so not exactly COTS.

Beowulf was still in the experimental phase. Researchers were building 5-20 node clusters. IIRC, there weren't any Beowulf clusters you would consider supercomputer at that time. Keep in mind the fastest supercomputer at the time cost $400 million and this one cost just $5 million.

Too bad System X was a publicity stunt that was never used for science AFAIK.

I heard of at least one engineering grad student who made heavy use of it to get work done. (Which is not a great counter-point, I know.)

I left in 03 so AFAIK the researchers using SystemX were coming from ICAM and the Bioinformatics department. The professor I worked under (as a gopher) had a 4way G4 cluster doing protein synthesis. He was very excited to get processor time on this supercomputer.

But whatever, some publicity stunt. They were stupid enough to waste money upgrading and expanding it because no one was using it.

A bit of engineer's rough estimating:

System was built in 2003 - 8 years ago. Assume moore's law of doubles every 2 years - expectation of 16x more powerful Actual increase in performance - 22x in 1/4th the size

Given that it's then a quarter of the size of system X, that's an amazing increase in peak performance.

There's only one problem - that speed increase appears to owe a lot to the use of GPGPU. As I understand it, whilst research into GPGPU for HPC* is a hot area at the moment, the scale of the actual benefits it offers is still a matter of debate (especially when considering costs and power consumption).

From my perspective, the biggest limitation in using GPUs for more general purpose computations is the communication latency. I published a paper that came to that conclusion: http://people.cs.vt.edu/~scschnei/papers/debs2010.pdf

In short, parallelism is not enough to get benefit from using GPUs. You need parallelism and data reuse.

I am not trying to be difficult but how is a $1.4M anything for the masses? Are they allowing the public to run jobs on the machine? Is there anything new about HS that brings SC500 power closer to my home office?

If by 'SC500' you mean a supercomputer that has a high ranking on the Top500 list, it's already here: http://arstechnica.com/business/news/2011/11/amazons-cloud-i...

I meant how does the VT system bring SC500 to the masses. As in the "supercomputer for the masses"?

Sounds like they're interested in improving GPU development tools, and how to utilize them for tasks they're good for versus what a traditional CPU is good for. From http://www.wired.com/wiredenterprise/2011/12/vt-supercompute...

"The Virginia Tech team is figuring out how to best to farm out computing jobs so that the GPUs and CPUs do what they do best, without ever going idle, and without spending too much time communicating with one another.

It’s not easy, but they’re using HokieSpeed to build tools for designing and compiling software so that it can be tweaked to run fast on these systems. They’ve also built what they call an “automated runtime system,” which works with the supercomputer’s operating system to speed things up even further."

I think the 'supercomputing for the masses' part comes from the fact that the peak performance of a relatively low-cost GPU is large compared to a CPU, but actually taking advantage of that performance is still difficult. They're aiming to change that with improved software development tools. At least, that's what I gathered from the article.

Here's a link to research they're doing on GPU computing: http://synergy.cs.vt.edu/

Whilst your average person at home may not be able to fork out $1.4M, this is in the afordable range of many medium to large businesses, and opens up access to private compute clusters*

The highest cost for supercomputers isn't the raw hardware - it's the power consumption. This also includes the cooling needs of a large cluster. Over the lifespan of a cluster, these will add up to a significant multiple of their base cost.

* of course, this may not be the ideal way to provide access to these services

From Feng: “The next frontier is to take high-performance computing, in particular supercomputers such as HokieSpeed, and personalize it for the masses.”

This is one of my favorite things about technology. As few as five years ago I was drooling about an Intel C2D chip and looking forward to quad-core chips. Nowadays that's standard technology in smartphones. I'm amazed by the graphics performance in the new Asus Transformer Prime with the Tegra 3 chip which has up to five cores.

The biggest problem with packing more power into smaller spaces is the battery consumption issue. Maybe there's no silver bullet for that only more efficient software and hardware usage.

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