This is a continuation of our look at future trends in high performance computing. In part 1 we covered the first five of the top ten trends. In this installment we’ll wrap up with the remaining five.
Future Trends in HPC, part 2
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Category Archives: Academic
Future trends in HPC, part 1
As we near the end of 2011, we take a moment to reflect on the past year. It’s been a busy year for IT across virtually all verticals, from mobile and search to enterprise servers and cloud computing. When we attended HPC360 a few weeks ago, we had the pleasure to attend a keynote presentation by Addison Snell, CEO of Intersect Research in which he discussed the most important trends in high performance computing (HPC).
HPC is an exciting and growing industry that ICC has been moving into the past couple years. The traditional HPC space revolved around high-end research facilities particularly in science and engineering. However, with each year technological innovations and tailored systems such as our Supermicro GPU Simcluster have brought the realm of HPC closer to reality for many small/medium-sized business and organizations.
In this 2-part series we will look at the top 10 future trends in HPC from Intersect360′s research, coupled with our own analysis and thoughts. No better way for us computer nerds to close the year right? Let’s get started.
Top 10 HPC Trends for 2012 and Beyond
HPC360 Conference Recap
We just returned from R Systems HPC360, a conference on high performance computing down in Champaign, Illinois which brought together leading industry professionals, academics, scientists, and enthusiasts.
The conference was titled HPC360 “Innovation through Modeling and Simulation”. The event took place at the i Hotel and Conference Center in Champaign, hosted by R Systems and sponsored by a number of companies including Dell, AMD, Intel, and yours truly, ICC!
Advanced Manufacturing Partnership (AMP) to spur innovation
On June 24, President Obama announced the Advanced Manufacturing Partnership (AMP) between the federal government, academia, and businesses to help stimulate the manufacturing sector of the U.S. economy. We have been following the so-called “Missing Middle” of small- to medium-sized manufacturers (SMMs) on this blog, and I’d like to describe some of the recent initiatives to engage this high-potential segment of our economy.
Speaking at Carnegie Mellon University, Obama described that AMP would allocate $500 million of federal money to help make U.S. manufacturing more competitive around the world.
Inspired by a report drafted by the President’s Council of Advisors on Science and Technology (PCAST), which found that there are market failures in the advanced manufacturing space that need to be overcome by government intervention, AMP will focus on five initiatives:
- Manufacturing for national security
- Materials science
- Robotics
- Energy efficiency
- Developing partnerships and consortia between government, universities, and industry Continue reading
National Center for Supercomputing Applications turns 25
Miles south of Chicago, amid the wind-swept flatlands of central Illinois, is the home of perhaps the world’s next fastest supercomputer. The National Center for Supercomputing Applications (NCSA) at the University of Illinois in Urbana-Champaign, which is co-developing the Blue Waters supercomputer, is today at the forefront of petascale computing. But 25 years ago, when the Center revved up its first machine, the computing world looked much different.
This article looks back at the highlights in NCSA’s 25-year history, which illustrate well how far computing technology has come in such a short span of time and the various innovations that supercomputers have made possible that we now take for granted. These highlights are based on the slideshow posted on NCSA’s website. (In the interest of full disclosure, ICC works with NCSA on the Dark Energy Survey, so we’re a little biased).
The first supercomputer at NCSA, which went operational in 1986, was a dual-processor machine that performed at about 400 megaflops. In comparison, the upcoming Blue Waters supercomputer will have 300,000 CPUs and a peak performance of 10 petaflops (that’s 25 million times faster than NCSA’s first supercomputer).
In 1998, NCSA came out with its first “cluster”, which connected 128 workstations together and was known as the NT Supercluster. This aggregation of towers looks somewhat comical today, and it wasn’t long before rack servers replaced these bulky form factors. Continue reading
HPC and the life sciences
This week, a team from our company visited a large laboratory located in the Chicago area. IT representatives there told us how a major focus for them has been migrating their computing resources from a model of individual workgroups using separate clusters to a shared private cloud that all research teams in the facility can access for running their jobs. This shift to private clouds for getting the most out of dedicated clusters is a hot topic of conversation in the HPC world.
HPC in the Cloud recently published an article responding to a case study written by Platform Computing about the implementation of a private cloud at the Harvard Medical School. Both are worth a read if you are interested in the challenges encountered by small- and medium-sized life sciences organizations when they try to adopt HPC clusters.
HPC holds much promise for organizations such as the Harvard Medical School. With middleware such as Platform Computing (we are biased, I must admit, since this is what HPC clusters by ICC deploy as well) it is getting easier to operate an HPC cluster with hosts running different operating systems and applications. It used to be that this multiplicity of software on the same cluster would cause extensive compatibility and usability problems, but not so much anymore. End-users in the life sciences (such as medical researchers) are benefiting from computing applications that are productive and easy to use.
So Harvard Medical School, as the HPC in the Cloud article describes, has migrated from an inefficient computing model of unshared individual computers scattered across various laboratories to a centralized private cloud that can be accessed by any of those users and managed as one unit. Simplifying maintenance while maximizing accessibility to HPC resources by medical school staff is most likely going to save money and increase the pace of innovation in the long run.
While this is a hopeful case study that sheds light on how other organizations can pool their computing resources to great effect, challenges remain for spreading this model to other small- and medium-size laboratories and businesses. For one, private medical companies are heavily regulated by the government and their IT infrastructure has to incorporate many time-consuming applications to store detailed records.
HPC is becoming more affordable and easier to use, but software has to continue evolving to accommodate the particular context of each industry. Only then will the life sciences (not to mention other markets) have a truly turn-key HPC solution that can benefit labs and private companies of every size.
Einstein@Home volunteers discover rare star
When I was a freshman in college, I helped the professor of my introductory astronomy class to conduct some of his research. The job wasn’t hard: I had to look at digital maps of the sky and try to find a particular type of rare star. Open map segment, click on pixels around a light source (star), evaluate if pixels have sufficient contrast, repeat. I never found the kind of star my professor was searching for.
Looking back on this experience, my job could have easily been done by a computer program. It would probably have been magnitudes more efficient that I was at analyzing the thousands of pixels on the map, and my professor wouldn’t have had to pay it $8.50/hr. Of course, as a college freshman, I was grateful for the research experience and the cash.
These days, universities are becoming more sophisticated in the way they let amateurs help them with research. A project called Einstein@Home recently had a breakthrough when a rotating pulsar was discovered by volunteer scientists, the first such accomplishment of its kind.
As the press release by the National Science Foundation describes, Einsten@Home is a collaborative project that allows lay people to donate the computing power of their PCs and laptops to help search the sky for celestial objects that have not been discovered yet. Over a quarter of a million volunteers from almost every country on earth participate in this venture, and now it has payed off.
Some of the volunteers’ computers recently unearthed a rare star that had not been documented before. This type of star is very important to researchers studying Einstein’s general theory of relativity, one of the most complicated paradigms in science. For such a star to be formed, there are many preconditions that must occur. As the press release noted above explains:
When two massive stars are born close together from the same cloud of gas, they can form a binary system and orbit each other from birth. If those two stars are at least a few times as massive as our sun, their lives will both end in supernova explosions. The more massive star explodes first, leaving behind a neutron star. If the explosion does not kick the second star away, the binary system survives. The neutron star can now be visible as a radio pulsar, and it slowly loses energy and spins down. Later, the second star can swell up, allowing the neutron star to suck up its matter. The matter falling onto the neutron star spins it up and reduces its magnetic field. This is called “recycling” because it returns the neutron star to a quickly-spinning state. Finally, the second star also explodes in a supernova, producing another neutron star. If this second explosion also fails to disrupt the binary, a double neutron star binary is formed. Otherwise, the spun-up neutron star is left with no companion and becomes a “disrupted recycled pulsar“, spinning between a few and 50 times per second.
Quite a find! With this recent success, collaborative computing projects such as Einstein@Home, which require very little involvement on the part of the lay user, will become more and more popular. There are many such opportunities available, and the page to download BOINC, the program that allows your computer to facilitate scientific research, even has a special option for using a GPU if your computer has one. With NVIDIA releasing their new GeForce GTS 450 GPU today for just $129, beefy gaming computers can now be easily used to scan the heavens when they’re not being honed to shoot alien mutants.
I know the first thing I’m going to do when I load up my personal laptop is install Einstein@Home. If I could not find the stars I was looking for when I was in astronomy class, maybe my computer can.
Extending Moore’s Law
According to Science News, researchers at Rice University have created the first two-terminal, pure silicon memory chips, easily adaptable to nanoelectric manufacturing.
Researchers discovered that silicon oxide could replace carbon in the process. When an electric charge is sent through silicon oxide-an insulator-between semiconducting sheets of polycrystalline silicon, it forms a conductive pathway as small as 5 nanometers (billionths of a meter) wide. This process creates a two-terminal resistive switch, far smaller than current circuits in computer architectures.
By continuously breaking and connecting these nano strips, one creates robust and reliable memory bits.
“The beauty of it is its simplicity,” said Professor James Tour. That, he said, will be key to the technology’s scalability. Silicon oxide switches or memory locations require only two terminals, not three (as in flash memory), because the physical process doesn’t require the device to hold a charge.
The implications for chip manufacturers and the continuation of Moore’s Law holds the promise for this technology.
Platform Computing – innovator in cloud computing software
When Platform Computing began as a company in 1992, computers were used by large organizations much like they had been for several previous decades. If a complex computing job needed to be run, it would be run on one machine, often only during certain times in the day. There was no widespread use of clusters or computing clouds. Computational research that now takes several days or weeks to complete used to take many months or years.
The first commercial project that Platform Computing undertook, according to an interview on HPCintheCloud.com with Platform CEO Songnian Zhou, was to help the engine manufacturer Pratt & Whitney design the engines for the new Boeing 777 airliner. Zhou describes how supercomputers were used back then:
At that time, they were using one Cray supercomputer rather than IBM mainframes to do it — and every night they would run one job. One job! Per night! Using that one Cray they had to explore all the parameters — how big or small, how many blades, and so on — all the design alternatives; that takes dozens and dozens of runs. They had to run half a year, which is of course a big problem for their product cycle to serve the airlines and their customers.
Platform Computing sought to change the way organizations such as Pratt & Whitney used supercomputers. Instead of running one job on one computer at a time, Platform pioneered the use of software to break up complex jobs to be performed on many computers connected together in one computer cluster. The airline and automotive industries, according to Zhou, were the early adopters of this technology and used it to speed up and simplify their design simulations.
Today, cloud computing and clustering have become industry standards, and there are now many companies that offer software and other services to facilitate them. Platform Computing still specializes in private clouds (a network of computers in the same facility most likely owned by the same organization) and community clouds (a network of computers owned and shared exclusively by a few organizations) which means that they cater towards large corporations and organizations that can afford purchasing large clusters.
Public clouds, the type most people think of when they talk about cloud computing, is facilitated mostly by other companies, although Platform has made an effort to reach out to smaller businesses and organizations in 2010.
If you are interested in Platform Computing software for clustering or private clouds, feel free to contact ICC and we can talk to you about the different options they have available to take full advantage of your computing hardware resources.
AMD Fusion processors – from GPU to APU
GPUs (graphics processing units) are a favorite topic on this blog. It is an innovative and powerful computing idea with an almost awkward origin: the graphics card, which has in the past been used to perform calculations necessary for visual rendering, is now used in GPU applications to help the processor perform millions of general computations. In effect, the GPU becomes a specialized processor in the computer.
GPU products have been soaring in popularity recently, especially for scientific uses. But the CPU-GPU arrangement still retains the old CPU-graphics card relationship. That is, the way a CPU and GPU are connected is still the same way that a CPU used to interact with a graphics card, through the PCI-E slot on the motherboard. GPUs are fast, but their speed is limited by this type of connectivity, a remnant from the days when GPUs were just graphics cards. In effect, PCI-E communication between the GPU and motherboard is a bottleneck on performance.
AMD is tackling this problem head-on with their upcoming Fusion line of processors. Instead of connecting a GPU to to the motherboard like an add-on card, AMD proposes to make the GPU part of the same silicon chip as the CPU, eliminating the need for PCI-E communication. They have dubbed this combination of CPU and GPU technology “Accelerated Processing Unit” (APU).
Currently, the AMD Fusion processor is going to be released for the consumer market in desktop and laptop computers. But, while AMD is working on bringing this technology to server boards, many issues (mostly with coding) need to be resolved before this can happen, as John Fruehe of AMD explains in his blog article, “Fusion for Servers”.
Nevertheless, this innovation carries some promise for the future evolution of GPU technology. It has the potential to eliminate the PCI-E bottleneck and make that remnant of GPU’s original function as a graphics card a thing of the past.
But this won’t be easy for AMD. Although it has the advantage of being the only processor manufacturer to also produce graphics cards (AMD bought ATI in 2006) NVIDIA is still the leader in GPU technology. Many commercial battles will still be fought for the future of GPUs between these and other manufacturers, among them the competition between the coding languages of CUDA and OpenCL.
Despite these hurdles, AMD’s plans for using Fusion processors in servers is an nascent idea with a lot of potential to improve the GPU market and make computers – and supercomputers – even faster.