HPC is Great for AI, But What Does Supercomputing Stand to Gain?

As we have written about extensively here at The Next Platform, there is no shortage of use cases in deep learning and machine learning where HPC hardware and software approaches have bled over to power next generation applications in image, speech, video, and other classification and learning tasks. Since we focus on high performance computing systems here in their many forms, that trend has been exciting to follow, particularly watching GPU computing and matrix math-based workloads find a home outside of the traditional scientific supercomputing center. This widened attention has been good for HPC as well since it has brought new attention to the field, which many outside tend to think of as academic (even if many of us know it is far broader than that). But what was interesting this week as we wrap up at the International Supercomputing Conference in Germany, which focused heavily on HPC and AI, was how much deep learning folks need from HPC, but how little there is (yet) to feed AI and machine learning back into supercomputing. To be fair, in our conversations, we asked users at several national labs and supercomputing sites if the emphasis this week at ISC '16 on deep learning was relevant for their workloads.