From predicting weather to discovering drugs to finding new energy sources, researchers use high performance computing (HPC) and large computing systems to simulate and predict our world. Improving the productivity and increasing the number of scientific simulation iterations can have a profound impact on the quantity and quality of breakthroughs.

Detailed knowledge of application workload characteristics can optimize performance of systems. This may sound daunting, with many HPC data centers hosting over 2,000 users running thousands of applications and millions of jobs per month.

However, at key supercomputing sites, a common pattern has emerged. Less than 2 percent of applications occupy most of the time on the system. This makes it easy to understand the benefit of accelerated computing. In short, a small amount of analysis can yield millions of dollars in savings—or the ability to buy a much more powerful and capable supercomputing system.

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