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Author: admin, 04.12.2013
At the Lawrence Livermore National Laboratory in California, a supercomputer named "Sequoia" puts nearly every other computer on the planet to shame. One of the first to take advantage of this opportunity was Stanford University's Center for Turbulence Research—and it wasn't hesitant about seeing what this machine is really capable of. It's part of a project to test noise generated by supersonic jet engines and help design engines that are a bit quieter.
Using giant supercomputers to solve complex scientific problems is by no means unique these days. Believe it or not, three hours with a million cores wasn't enough to make a real dent in the jet noise project.
The problems are all over the place, including "How do you write into one file from a million processors that are all trying to step on each other?" Nichols said. Of course, going for "more cores" isn't necessarily the best way to tackle a supercomputing problem. Besides speeding up lengthy calculations, more cores and faster supercomputers will let scientists tackle even more complicated problems.
Nichols and team use the Navier-Stokes equations and codenamed CharLES for the jet noise simulations (LES stands for large eddy simulation). We've written before about large supercomputing runs—for example, one using 50,000 cores on the Amazon Elastic Compute Cloud.
Performance scaled almost at a one-to-one ratio with the increase in cores, with 83 percent efficiency. The real speed-up was 6.6, which is "83 percent of the ideal speed-up we would like to see. The simulations allow Stanford to test how changes to the engine nozzle and chevrons impact noise.


The study of scramjets and the conditions under which they might fail involves similar complexity.
For now, the researchers will have to continue this work with paltry sub-million-core supercomputers. As a high school student in 1994, Nichols attended a summer program at Lawrence Livermore and worked on the Cray Y-MP. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of Conde Nast.
For three hours on Tuesday of last week, researchers from the center remotely logged in to Sequoia to run a computational fluid dynamics (CFD) simulation on a million cores at once—1,048,576 cores, to be exact. The work is sponsored in part by the US Navy, which is concerned about "hearing loss that sailors on aircraft carrier decks encounter because of jet noise," Research Associate Joseph Nichols of the Center for Turbulence Research told Ars. Larger numbers of cores don't necessarily translate to the fastest speed, either, because of differences between processors and the designs of supercomputers. Despite preparation work aimed at cutting out bottlenecks, it was just enough time to make sure the code ran properly and to get a sense of the possibilities that million-core computers can offer.
Sequoia later fell to second place behind a 17.59-petaflop system at the Oak Ridge National Laboratory, but it is still the only system on the Top 500 supercomputers list with a million or more cores. With both CPUs and GPUs in a system, Titan's CPUs guide the simulations but hand off work to the GPUs, which can handle many more calculations at once despite using only slightly more electricity.
Code has to be carefully prepared to account for the bottlenecks that arise when information is passed from one core to another (unless the application is so parallel that each core can work on separate calculations without ever talking to each other). The same calculation could be done on a million Sequoia cores in about 8 to 12 hours, he said.
They're using the same code for separate projects to research scramjets (supersonic combustion ramjets), which could travel at 10 times the speed of sound.


That one was "embarrassingly parallel," in which the calculations are all independent of each other. Nichols explains that since going from 131,000 cores to 1 million multiplies the number of cores by 8, one would want a speed-up of 8 as well.
It means that as we're adding more cores the code is getting faster and faster, even at the million level. With supercomputers these simulations can be done without building physical models or testing in wind tunnels. But perhaps it won't be long before supercomputers as powerful as Sequoia are the standard for such research. But for a limited time, the machine is being made available to outside researchers to perform all sorts of tests, a few hours at a time. The million-core run is intriguing, but it also poses extreme challenges in trying to use all those cores at once without things going wrong.
The Titan system was built by the supercomputer manufacturer Cray, and it uses Nvidia graphics processing units in addition to traditional CPUs to gain dramatic increases in speed.
That means the speed of the interconnect, the connections between each processing core, didn't really matter. The calculation uses the interconnect in such a way that communication overhead was very minimal even at a million processors.



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