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That property is the same reason you don't incur substantial overhead doing large matrix multiplications sharded over disks or many machines. You apply the same
by hansvm 2y ago
That property is the same reason you don't incur substantial overhead doing large matrix multiplications sharded over disks or many machines. You apply the same chunking strategies used to optimally use L1/L2/L3 caches, just instead at the level of numa nodes, physical disks, machines, and clusters. So long as each "cache" is big enough for the N^3/N^2 term to dominate communication overhead (especially if that communication can happen concurrently), the networked result is about as fast as the individual machines running at their max FLOPs for some smaller problem.
- hansvm 2y agoIt looks like a dozen people found this helpful. As a related idea, it's one of the main reasons batch inference is so much more efficient in ML. You transmute the problem from memory-bound to compute-bound.