And... they failed. I broke some DistributedRowMatrix tests.
On Mon, Apr 22, 2013 at 8:14 PM, Dan Filimon <[email protected]>wrote: > The tests are still running. I want to make sure they all pass before > another round of benchmarks. :) > > > On Mon, Apr 22, 2013 at 8:00 PM, Robin Anil <[email protected]> wrote: > >> Can you update/create a spreadsheet of where you are right now v/s trunk >> On Apr 22, 2013 11:51 AM, "Dan Filimon" <[email protected]> >> wrote: >> >>> In fact the issue I was referring to turns out to be because the very >>> fast case was in fact wrong. >>> When merging two sparse vectors I wasn't updating the number of mappings >>> in the result. >>> >>> Performance is now better for the more "tuned" vectors. >>> I have noticed some random regressions with dense vectors ... this is >>> pretty odd. :/ >>> >>> Anyway, can you give me some insight into: >>> - what exactly the numbers in the spreadsheet mean? >>> - what is the "Cluster" score for some benchmarks? There don't seem to >>> be explicit calls to any cluster vectors. >>> >>> Thanks! >>> >>> >>> >>> On Mon, Apr 22, 2013 at 5:24 PM, Robin Anil <[email protected]>wrote: >>> >>>> Yes every time you replace primitive call you are at the mercy of jit >>>> to inline the method. Choose primitive wherever possible to reduce >>>> variability >>>> On Apr 22, 2013 7:15 AM, "Dan Filimon" <[email protected]> >>>> wrote: >>>> >>>>> Thanks! >>>>> >>>>> So, I'm running more benchmark and it's a mixed bag. There are >>>>> regressions and gains, but what surprises me the most is that after >>>>> replacing every "primitive" call with calls to assign/aggregate, the >>>>> clustering behaves much worse. >>>>> >>>>> As in, dozens (literally) of times worse. I'm surprised it's so bad, >>>>> yet doesn't show in the benchmarks. >>>>> Any ideas why this might be, or what I should look into? >>>>> >>>>> >>>>> On Sat, Apr 20, 2013 at 9:14 PM, Robin Anil <[email protected]>wrote: >>>>> >>>>>> >>>>>> https://docs.google.com/spreadsheet/ccc?key=0AhewTD_ZgznddGFQbWJCQTZXSnFULUYzdURfWDRJQlE#gid=2 >>>>>> >>>>>> Here you go. There are some regressions and some improvements. One of >>>>>> the major reasons I think is replacing inline math with foo.apply(). JVM >>>>>> might not have optimized it yet. You might be better off but just adding >>>>>> an >>>>>> AggregateBenchmark and working on it for your functions before replacing >>>>>> entire AbstractVector methods. >>>>>> >>>>> >>>>> >>> >
