This is a very underappreciated point. For example, in pharmaceutical drug discovery, what often matters most is the ability to play around with different modeling assumptions, and to do large-scale &quot;search engine&quot; work on different compound properties and how they might affect desired overall properties of the final product.<p>I wonder if one reason why this highly specific computational architecture is only deployed in two places (as far as I can tell) is because it&#x27;s actually <i>not</i> very applicable to mainstream molecular dynamics use cases? A lot of the business around pharma drug discovery seems to be pushing for more cloud computing solutions that offer better analytics APIs for engineers, rather than beefing up hardware for larger-scale simulations.