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The power HPC facility

Jack Wells, director of science for the Oak Ridge Leadership Computing Facility


he drug discovery community is a new one for us, but we have some indications that it will become a

growing user base. ORNL is a production computing facility, but we try to be at the leading edge of what’s possible in terms of size and capability. In 2009 we had the world’s fastest supercomputer on the Top500 list, Jaguar, and in August 2012 we began to upgrade the system to Titan. We used the same cabinets and in order to get the 10x improvement in peak computational capability we went with GPUs. Although this has as meant a marginal increase of 15 per cent in our electricity consumption, it is drawing in these new communities as they are already exploring general-purpose GPU computing (GPGPU) on desktop machines, workstations or clusters. Pharmaceuticals is an aggressive industry

that faces big problems. Getting potential drugs through the three-stage review process isn’t easy, in fact most fail in the third round of testing, aſter the investment of considerable amounts of time, effort and money have been made. If the cause of that failure, such as some unexpected interaction, could have been identified earlier, that investment could have been saved. Tis is one clear driver for high-performance computing resources being used in drug discovery. But there are a lot of changes occurring at the high-end of computing right now and it’s causing some users to be aggressive and others to be hesitant in terms of writing new soſtware. Almost all our applications have parallelism implicit, but our codes are not always written to express that and that’s the test.


Sumit Gupta, general manager of the Tesla accelerated computing business unit at Nvidia

G is a distributed computing platform that was born out of the realisation that as millions of PCs all over

the world spend much of their time idling, these cycles could be used to further biomedical research. Te challenge is that when conducting molecular dynamics simulations, these large jobs must first be broken down into smaller pieces before they are distributed, the data received and the results aggregated. Te issue is that work given to any one PC may not be completed as someone may turn it off or the


network may go down. Tat’s the difficultly with distributed systems, but the genius of the soſtware is that you can actually get useful work out of them. GPU accelerators play a significant part as instead of being able to run one simulation on a single PC, per day, researchers can run up hundreds simultaneously – an order of magnitude improvement that shortens the time to discovery.

I would say that four

discrete steps in the life sciences pipeline make use of GPU accelerators: gene sequencing; sequence analysis; molecular modelling; and diagnostic imaging. In fact, there are a number of gene sequencing machines that now contain GPUs within them. Gene sequencing, sequencing analysis and molecular modelling are emerging scientific disciplines within pharmaceuticals and nearly all large pharma companies have departments focused on computational methods. Many companies still focus on theory and

experimentation, however, due to the fact that there are a lot of challenges associated with the use of computational methods. One reason why it’s not more pervasive in the industry is that it’s not detailed enough. As GPUs reduce the amount of time it takes to run a simulation, more complex and accurate models can be developed, and I believe this will lead to a wider adoption of computational methods within the pharma pipeline. I would say that the use of computer simulations have been held back in the pharma world because they simply haven’t been fast enough. GPUs are changing this.

behind pharmaceuticals Is HPC becoming an integral part of drug discovery? Industry experts explore the trends


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