Amazon EC2 and other cloud services are expanding the market for high-performance computing. Without access to a national lab or a supercomputer in your own data center, cloud computing lets businesses spin up temporary clusters at will and stop paying for them as soon as the computing needs are met.

A vendor called Cycle Computing is on a mission to demonstrate the potential of Amazon’s cloud by building increasingly large clusters on the Elastic Compute Cloud. Even with Amazon, building a cluster takes some work, but Cycle combines several technologies to ease the process and recently used them to create a 30,000-core cluster running CentOS Linux.

The cluster, announced publicly this week, was created for an unnamed “Top 5 Pharma” customer, and ran for about seven hours at the end of July at a peak cost of $1,279 per hour, including the fees to Amazon and Cycle Computing. The details are impressive: 3,809 compute instances, each with eight cores and 7GB of RAM, for a total of 30,472 cores, 26.7TB of RAM and 2PB (petabytes) of disk space. Security was ensured with HTTPS, SSH and 256-bit AES encryption, and the cluster ran across data centers in three Amazon regions in the United States and Europe. The cluster was dubbed “Nekomata.”

Spreading the cluster across multiple continents was done partly for disaster recovery purposes, and also to guarantee that 30,000 cores could be provisioned. “We thought it would improve our probability of success if we spread it out,” Cycle Computing’s Dave Powers, manager of product engineering, told Ars. “Nobody really knows how many instances you can get at any one time from any one [Amazon] region.”

Amazon offers its own special cluster compute instances, at a higher cost than regular-sized virtual machines. These cluster instances provide 10 Gigabit Ethernet networking along with greater CPU and memory, but they weren’t necessary to build the Cycle Computing cluster.

The pharmaceutical company’s job, related to molecular modeling, was “embarrassingly parallel” so a fast interconnect wasn’t crucial. To further reduce costs, Cycle took advantage of Amazon’s low-price “spot instances.” To manage the cluster, Cycle Computing used its own management software as well as the Condor High-Throughput Computing software and Chef, an open source systems integration framework.

Cycle demonstrated the power of the Amazon cloud earlier this year with a 10,000-core cluster built for a smaller pharma firm called Genentech. Now, 10,000 cores is a relatively easy task, says Powers. “We think we’ve mastered the small-scale environments,” he said. 30,000 cores isn’t the end game, either. Going forward, Cycle plans bigger, more complicated clusters, perhaps ones that will require Amazon’s special cluster compute instances.

The 30,000-core cluster may or may not be the biggest one run on EC2. Amazon isn’t saying.

“I can’t share specific customer details, but can tell you that we do have businesses of all sizes running large-scale, high-performance computing workloads on AWS [Amazon Web Services], including distributed clusters like the Cycle Computing 30,000 core cluster to tightly-coupled clusters often used for science and engineering applications such as computational fluid dynamics and molecular dynamics simulation,” an Amazon spokesperson told Ars.

Amazon itself actually built a supercomputer on its own cloud that made it onto the list of the world’s Top 500 supercomputers. With 7,000 cores, the Amazon cluster ranked number 232 in the world last November with speeds of 41.82 teraflops, falling to number 451 in June of this year. So far, Cycle Computing hasn’t run the Linpack benchmark to determine the speed of its clusters relative to Top 500 sites.

But Cycle’s work is impressive no matter how you measure it. The job performed for the unnamed pharma company “would take well over a week for them to run internally,” Powers says. In the end, the cluster performed the equivalent of 10.9 “compute years of work.”

The task of managing such large cloud-based clusters forced Cycle to step up its own game, with a new plug-in for Chef the company calls Grill.

“There is no way that any mere human could keep track of all of the moving parts on a cluster of this scale,” Cycle wrote in a blog post. “At Cycle, we’ve always been fans of extreme IT automation, but we needed to take this to the next level in order to monitor and manage every instance, volume, daemon, job, and so on in order for Nekomata to be an efficient 30,000 core tool instead of a big shiny on-demand paperweight.”

But problems did arise during the 30,000-core run.

“You can be sure that when you run at massive scale, you are bound to run into some unexpected gotchas,” Cycle notes. “In our case, one of the gotchas included such things as running out of file descriptors on the license server. In hindsight, we should have anticipated this would be an issue, but we didn’t find that in our prelaunch testing, because we didn’t test at full scale. We were able to quickly recover from this bump and keep moving along with the workload with minimal impact. The license server was able to keep up very nicely with this workload once we increased the number of file descriptors.”

Cycle also hit a speed bump related to volume and byte limits on Amazon’s Elastic Block Store volumes. But the company is already planning bigger and better things.

“We already have our next use-case identified and will be turning up the scale a bit more with the next run,” the company says. But ultimately, “it’s not about core counts or terabytes of RAM or petabytes of data. Rather, it’s about how we are helping to transform how science is done.”