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ML Algorithms a Poor Fit for Predictive Caches

Pete Warden describes an interesting phenomenon:

I’ve been working on a new research paper, and a friend gave me the feedback that he was confused by the statement “memory accesses can be accurately predicted at the compilation stage” for machine learning workloads, and that this made them a poor fit for conventional processor architectures with predictive caches. I realized that this was received wisdom among the ML engineers I know, but I wasn’t aware of any papers that discuss this point. I put out a request for help on Twitter, but while there were a lot of interesting resources in the answers, I still couldn’t find any papers that focused on what feels like an important property for machine learning systems. With that in mind, I wanted to at least describe the issue as best as I can in this blog post, so there’s a trail of breadcrumbs for anyone else interested in how system designs might need to change to accommodate ML.

Read on for the explanation. My reading here is that this is a downside to having general-purpose compute: you run the risk of sub-optimal performance in certain circumstances, like training models using certain types of ML algorithms.