Ganging up Accelerators to Beat Scale Limits

October 11, 2016 Nicole Hemsoth It is not news that offloading work from CPUs to GPUs can grant radical speedups, but what can come as a surprise is that scaling of these workloads doesn’t change just because they run faster. Moving beyond a single node means encountering a performance wall, that is, unless something can glue everything together so it can scale at will. There are already technologies that can take multiple units of compute and have them share work from supercomputing and other areas (consider ScaleMP, for instance) but there are limitations to these approaches and thus far, they haven’t extended to meet the capabilities of accelerators and custom processors in a flexible, accessible way across different deployment mechanisms (cloud, bare metal, via containers, etc.). However, as we described…

Link to Full Article: Ganging up Accelerators to Beat Scale Limits

Pin It on Pinterest

Share This

Join Our Newsletter

Sign up to our mailing list to receive the latest news and updates about and the Informed.AI Network of AI related websites which includes Events.AI, Neurons.AI, Awards.AI, and Vocation.AI

You have Successfully Subscribed!