Decoupled DiLoCo: Resilient, Distributed AI Training at Scale

Generically speaking, yes, this has been done before. But it can take a lot of work to transform software that works with shared memory or other low-latency interprocess communication mechanisms so that it's practical to run across wide area networks. Sometimes that's not possible at all. Certain problems still require "high performance computing" architectures with all of their compute nodes in the same building, connected by high-bandwidth, low-latency links.

You're right: the MapReduce pattern is very old, and it is well-known that applying it to AI training to enable geographically distributed training runs would be very beneficial. We haven't done it yet because model training workloads are more difficult to parallelize with high intra-node latency than a lot of traditional workloads.

This paper proposes a work partitioning scheme that removes a constraint that makes parallelizing AI training inefficient. The idea of a work partitioning scheme isn't novel, but the scheme itself is.

The post specifically calls out prior art and explains very well why it is different a d what it adds.

No