Hi Josh, Did you run the patch from the jira issue or did you run the trunk? I made some follow up changes after uploading the patch. I can't imagine why those small changes would lead to an increase of 50% in the runtime.
/s On 07.03.2013 15:02, Josh Devins wrote: > So the good news is that the patch runs ;) The bad news is that it's > slower, going from 1600-1800ms to ~2500ms to calculate a single users' topK > recommendations. For kicks, I ran a couple other experiments, progressively > removing code to isolate the problem area. Results are detailed here: > https://gist.github.com/joshdevins/5106930 > > Conclusions thus far: > * the patch is not helpful (for performance) and should be reverted or > fixed again (sorry Sebastian) > * the dot product operation in `Vector` is not efficient enough for large > vectors/matrices, when used as it is in the ALS `RecommenderJob`, inside a > loop over `M` > > I've tried a few other experiments with Colt (for example) but there was no > noticeable gain. Parallelizing inside the map task (manually or with > Parallel Colt) is possible but obviously is not ideal in an environment > like Hadoop -- this would save memory since you only need a few map tasks > loading the matrices, but isn't playing very nicely within a shared cluster > :) > > Next step at this point is to look at either reducing the number of items > to recommend over, LSH or a third secret plan that "the PhD's" are thinking > about. Paper forthcoming, no doubt :D > > @Sebastian, happy to run any patches on our cluster/dataset before making > more commits. > > > > On 6 March 2013 20:58, Josh Devins <[email protected]> wrote: > >> Got sidetracked today but I'll run Sebastian's version in trunk tomorrow >> and report back. >> >> >> On 6 March 2013 17:07, Sebastian Schelter <[email protected]> wrote: >> >>> I already committed a fix in that direction. I modified our >>> FixedSizePriorityQueue to allow inspection of its head for direct >>> comparison. This obviates the need to instantiate a Comparable and offer >>> it to the queue. >>> >>> /s >>> >>> >>> On 06.03.2013 17:01, Ted Dunning wrote: >>>> I would recommend against a mutable object on maintenance grounds. >>>> >>>> Better is to keep the threshold that a new score must meet and only >>>> construct the object on need. That cuts the allocation down to >>> negligible >>>> levels. >>>> >>>> On Wed, Mar 6, 2013 at 6:11 AM, Sean Owen <[email protected]> wrote: >>>> >>>>> OK, that's reasonable on 35 machines. (You can turn up to 70 reducers, >>>>> probably, as most machines can handle 2 reducers at once). >>>>> I think the recommendation step loads one whole matrix into memory. >>> You're >>>>> not running out of memory but if you're turning up the heap size to >>>>> accommodate, you might be hitting swapping, yes. I think (?) the >>>>> conventional wisdom is to turn off swap for Hadoop. >>>>> >>>>> Sebastian yes that is probably a good optimization; I've had good >>> results >>>>> reusing a mutable object in this context. >>>>> >>>>> >>>>> On Wed, Mar 6, 2013 at 10:54 AM, Josh Devins <[email protected]> >>> wrote: >>>>> >>>>>> The factorization at 2-hours is kind of a non-issue (certainly fast >>>>>> enough). It was run with (if I recall correctly) 30 reducers across a >>> 35 >>>>>> node cluster, with 10 iterations. >>>>>> >>>>>> I was a bit shocked at how long the recommendation step took and will >>>>> throw >>>>>> some timing debug in to see where the problem lies exactly. There >>> were no >>>>>> other jobs running on the cluster during these attempts, but it's >>>>> certainly >>>>>> possible that something is swapping or the like. I'll be looking more >>>>>> closely today before I start to consider other options for calculating >>>>> the >>>>>> recommendations. >>>>>> >>>>>> >>>>> >>>> >>> >>> >> >
