No, just was never written I suppose back in the day. The way it is structured now it creates a test split for each user, which is also slow, and may be challenging to memory limitations as that's N data models in memory. You could take a crack at a patch.
When I rewrote this aspect in a separate project it was certainly threaded and relied on a single test split. It's much faster indeed. On Mon, Apr 1, 2013 at 11:26 AM, Gabor Bernat <[email protected]> wrote: > Hello, > > Is there any good reason why the *GenericRecommenderIRStatsEvaluator* does > not support parallel (multi-CPU) evaluation. Today is quite common to have > CPUs with more than one core, and IR evaluation on any reasonably sized > data set takes forever to finish. I'm thinking if we could parallelize the > evaluation, by breaking down the input into subsets, and accumulating at > the end the measurements of each subset, the evaluation time could be > heavily improved. > > For example I have a data set with 2+ million ratings, and evaluating IR > with even 10% of this with a simple recommender takes more than 3 hours > with just a single core of my CPU being kept busy... > > So? > > > Bernát GÁBOR
