Recently, MedCity News spoke with a pair of top executives at global supercomputing company Cray: Per Nyberg, Senior Director, Artificial Intelligence and Analytics and Ted Slater, Global Head of Healthcare and Life Sciences.
Cray provides powerful computational tools to address a range of challenging workloads from modeling and simulation, to artificial intelligence and analytics. “We strive to deliver the tools that our customers need, whether on-premise or hosted in the cloud as a service,” Slater said.
The company’s long-term vision is built around the convergence of simulation and data analytics, combining multiple technologies into a single integrated system. With more than forty years of experience, Cray is focused on solving the most complex computing and analytics challenges.
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AI and life sciences
The application of machine learning, and in particular deep learning, to biological problems will continue to develop and require ever more data and ever larger, more complex models. Training these models will require much more classic HPC computation.
In the life sciences, “Machine learning is applied to a wide variety of problems, including biomedical image analysis, molecular structure analysis, protein function prediction, and characterization of genotype-phenotype relationships, to name just a few,” reports Slater. Life sciences researchers working on problems like these routinely generate vast amounts of data. “This is where big data comes into play and where strong supercomputing capabilities flourish,” Slater explained.”
Convergence of supercomputing and big data
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Slater points to cancer research as one area where there is an enormous amount of data being studied as scientists try to better understand the disease. Cray has provided high-performance computing to many organizations that have cancer research as a major focus, including the Broad Institute of MIT and Harvard where some researchers are conducting genome-wide variant analysis at scale.
Cray’s approach is especially well suited to artificial intelligence. “For several years we have been anticipating and preparing for a general convergence of data analytics and simulation and modeling HPC problems,” said Nyberg.
As data markets mature, Nyberg said the convergence of supercomputing and big data means that more businesses are going to see higher value in their data. Cray has built a suite of big data and AI software called , Urika-XC, for its flagship XC Series supercomputers. Urika-XC powers analytics and AI workloads that run alongside scientific modeling and simulations on the supercomputer, eliminating the need to move data between differing systems. Cray can run converged analytics and simulation workloads across a variety of scientific and commercial initiatives, such as precision medicine.
“At our core, we are technology pioneers who strive to deliver tools into the hands of researchers.,” Slater said. “We work with organizations to understand what they’re trying to do and what problems they aren’t able to solve with existing technologies. Then we develop complete supercomputing technologies that help them address their otherwise unanswerable questions.”
