High performance computing (HPC) is a fundamental pillar of modern science. From predicting weather to discovering drugs to finding new energy sources, researchers use 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. Recent examples of scientific breakthroughs at supercomputing sites include modeling the formation of the HIV capsid, predicting weather, and accelerating life-saving drug discoveries.
Detailed knowledge of application workload characteristics can optimize performance of current and future 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 relatively 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.
HPC data centers need to be built to maximize throughput and enable more science achievements in a shorter amount of time. Maximizing throughput isn’t only about building the biggest, fastest system but also about anticipating future application workload demands. To ensure the data center is designed to provide the maximum throughput, it’s critically important to first understand the workload profile of the data center users. Adding the power of data science (AI) to the already powerful HPC data center will lead to science achievement once thought impossible. The HPC data center of the future will require the right amount for GPU accelerators to maximize throughput, reduce costs, and increase the amount of science it can achieve. Order your GPU from us today.
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