Petascale Coming Down the Pike
Supercomputing is on the cusp of a new era, offering researchers processing power never seen before. Here’s a look at machines poised to help the life sciences community chip away at the building blocks of biology
By Matthew Dublin
Given the combination of the everincreasing power of compute hardware and researchers’ desire to unlock the mysteries of life, it’s no surprise that high-performance computing in the early 21st century is now talking in terms of a whole new scale of computation. While the life sciences community has for some time now been concerned with terrifying amounts of data in terabytescale proportions — that’s 1,024 gigabytes — there is an even larger scale on the computational horizon: petascale computing. One petabyte is 1,024 terabytes, and to provide some perspective, Google processes an average of about 20 petabytes of data per day.
Gee-whiz factor aside, should petascale and near-petascale systems even be on the radar screen of the life sciences community? So far, there is a resounding yes from many in the molecular simulation research community. “With petaflop-scale performance, [molecular] simulations will reach the time scale of a submillisecond,” says Makoto Taiji, a team leader at the RIKEN Yokohama Institute. “This time scale will cover various interesting biological events, including large fluctuations in proteins. … Petascale computing will provide scientific breakthroughs.”
Taiji and his team use RIKEN’s petaflop-capable supercomputer called MDGRAPE-3 to conduct a range of molecular dynamic simulations, including “post-docking” — a protocol he uses to choose drug candiate compunds with more precision after using the normal molecular docking technique. “We have already found a few successful seed compounds for real drug targets confirmed by the experimental assays,” says Taiji. “Their optimization is [underway], and we are trying to use our machine also for the optimizations of the seeds.” In the case of MDGRAPE-3, which is not really a programmable machine, porting popular molecular dynamics software to run on its architecture is not that difficult, but it does require a deep knowledge of the software and people power. So far, Taiji says, they do not have enough researchers and programmers to study that many molecular dynamics software packages, although they have managed to port Amber and CHARMM, two popular simulation applications.
On the other side of the globe, Nick Grishin, a professor of biophysics at the University of Texas Southwest Medical Center and a Howard Hughes Medical Institute investigator, recently used Ranger, the sixth most powerful supercomputer in the world according to the Top500 list, to solve some very difficult threedimensional protein folding problems. Housed at the Texas Advanced Computing Center, Ranger came online in February 2008 and Grishin got to use its roughly 62,976 processing cores to help secure top honors in the most recent Critical Assessment of Techniques for Protein Structure Prediction competition, a worldwide contest to predict the structures of a select number of unknown proteins.
“It’s embarrassing to say, but because our algorithms are stochastic, they are not particularly fast to run, and protein chains are very long so it just takes an incredible amount of computer resource to compute those energies,” Grishin says. Without Ranger, Grishin says he never would have been able to accomplish the task. “A typical cluster is just too small; it needs to be many more processors. A hundred or 200 processors is clearly not enough for this kind of job. … And the more computations we make, the more likelihood there is that we will hit the right energy function and have something with some medical importance,” he says.
Still a rarity
Despite the growing number of petascale machines, it’s not as if just anyone can waltz down the hallway of an institute and find one to use. These systems are still relatively rare; there are only two supercomputer sites currently capable of achieving one petaflop peak performance in the US. Los Alamos National Laboratory unveiled its Roadrunner supercomputer only last year, which is listed on the Top500 supercomputing sites list as the world’s most powerful supercomputer with a whopping 129,600 processing cores. In late January of this year, the Oak Ridge National Laboratory announced that its Cray XT supercomputer, known as Jaguar, is now capable of a peak performance of 1.6 petaflops. “Highperformance computing affects all areas of computational science, including biological research … [and] more and more petascale systems will be coming online,” says Jack Dongarra, a professor of electrical engineering and computer science at the University of Tennessee. Dongarra is one of the developers of the LINPACK Benchmark, a series of dense linear equations used to measure a compute system’s processing capacity. Dongarra helps run the Top500 list, a semiannual listing of the most powerful computing sites in the world compiled by computer scientists in the US and Germany. According to Dongarra, all high-performance systems will reach petascale in the very near future. “The projections say that all of the Top500 fastest computers will be at petascale in 2015,” he says.
More petaflop machines are already on the way. The National Center for Supercomputing Applications has teamed up with IBM to create Blue Waters, a beast of a machine that contains more than 200,000 processing cores and is capable of sustained multi-petaflop performance. Although exact performance figures are still being kept confidential by IBM, all involved claim that Blue Waters will far exceed the performance capabilities of the two formerly mentioned machines by a long shot when it comes online in the summer of 2011.
“I think petascale computing comes at a very good time for biology, especially genomics, which has to deal with … increasingly large data sets trying to do a lot of correlation between the data that’s held in several massive datasets,” says Thomas Dunning, director of the NCSA at University of Illinois, Urbana-Champaign. “This is the time that biology is now going to need this kind of computing capability — and the good thing is that it’s going to be here.”
David Bader, a professor of computer science at the Georgia Institute of Technology, has been heavily involved in promoting awareness of petascale computing. In 2006, he co-chaired a workshop that attempted both to lay out a roadmap of recommendations for making petascale computing a reality for the life sciences and also to address its many challenges, the biggest of which is scaling algorithms to run on these mega architectures.
“First and foremost, this is a scale of system that has not been seen before,” Bader says. “Just in June, we saw Roadrunner using accelerators like the Cell processor and now we see the Cray XT-5 system at Oak Ridge, so I think that can lead to more experience on how to scale algorithms that can run on all those processors.”
In addition to scalability, reliability is another major hurdle. When a system crash causes you to lose a few hundred gigabytes on a simulation or analysis job, that really hurts — but just think of the gnashing of teeth when you’re talking terabytes or petabytes of data gone haywire. Efforts such as the Berkeley Lab Checkpoint/Restart project have focused on how to ensure a high level of reliability in these monolithic systems. At the start of the year, the group released a new and improved version of its software, an open-source solution that uses checkpointing to take hourly snapshots of MPI-enabled applications running jobs on large-scale compute systems. The software “works transparently and users do not need to make source code changes to their applications to work with BLCR,” says Eric Roman, a member of the Future Technologies Group at Lawrence Berkeley National Laboratory. “On a petascale system with possibly thousands of users and applications, this feature should not be overlooked.”
Planning for peta
Given that eco-consciousness now pervades the computing world, supercomputing sites are following suit and are continuing to make moves toward more environmentally friendly infrastructure. For Blue Waters, energy-efficient design is not so much a choice as a necessity. “I think we’ve gotten to a point where that has to be a priority — where if you don’t pay careful attention to that, the power budget can just become overwhelming, so in fact we’re at the point right now where that has to be part of the consideration,” says NCSA’s Dunning. “For example, Blue Waters will be a water-cooled, not air-cooled, machine and the simple reason for that is that it’s 40 percent more efficient.” NCSA also brought in a specialized team to look at ways to minimize the footprint of the building itself so that most of the power coming into the building is actually used to run the computer rather than all the ancillary things needed for the facility.
Bader also hopes to see petascale computing assisting with big ideas in genomics. “When I think of petascale machines, I think of doing complex operations. So once I can assemble whole genomes and sequence whole genomes and get much richer data sets and combine that with microarray data and other data sources, what I want to be able to do is understand the evolution of whole genomes and compare both the organisms and genes across whole and entire genomes,” he says. “And that’s a problem that needs both an army of data and also the computational requirements of a petascale system. So rather than just taking our current techniques and running them a bit faster, I think that developing new algorithms … is really where we’re headed.”
And Bader reminds researchers that this isn’t something only computer scientists should be thinking about. “Biologists will need to be aware of this technology because if you push out the road map 10 years, these are the capability class machines that they’ll have in their laboratories,” Bader says. “There are a lot of biological problems that are still in their infancy and we [now understand] to solve those problems we’ll have to bring in a lot of data collected from a lot of sites and a lot of laboratories. … [There will] be a growing number [of biologists] who need access to massive volumes of data and the computational capabilities to solve their particular scientific inquiry.”