Numbers are ops per sec

There are some cluster benchmarks. I don't know who added it. But its baked
as the type column
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.
>>>>
>>>
>>>
>

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