As it is a *sparse matrix*, C++ library unavailable on JVM, I don't
consider it relevant for comparison as these are really apples and
pineapples. For now, at least.

On Tue, Jun 23, 2015 at 12:13 AM, A <aael...@gmail.com> wrote:

>
> Here's another benchmark for comparison:
> https://code.google.com/p/redsvd/wiki/English
>
> -A
>
>
>
> On Monday, June 22, 2015 at 12:27:57 PM UTC-7, Dragan Djuric wrote:
>>
>> core.matrix claims that it is fast on its project page (with which I
>> agree in some cases). I expected from that, and from the last couple of
>> your posts in this discussion, that there are some concrete numbers to
>> show, which I can't find.
>>
>> My claim to win "ALL benchmarks" (excluding maybe tiny objects) came only
>> as a response to mike's remarks that I have only proven that neanderthal is
>> faster for dgemm etc.
>>
>> OK, maybe the point is that other libraries do not care that much about
>> speed, or that current speed is enough, or whatever, and I am ok with that.
>> I would just like it to be explicitly said, so I do not lose time arguing
>> about what is not important. Or it would be nice to see some numbers shown
>> to draw at least rough picture of what can be expected. I am glad if my
>> raising this issue would improve the situation, but I do not insist...
>>
>> On Monday, June 22, 2015 at 9:16:15 PM UTC+2, Christopher Small wrote:
>>>
>>> Well, we also weren't claiming to win "ALL benchmarks" compared to
>>> anything :-)
>>>
>>> But your point is well taken, better benchmarking should be pretty
>>> valuable to the community moving forward.
>>>
>>> Chris
>>>
>>>
>>> On Mon, Jun 22, 2015 at 12:10 PM, Dragan Djuric <drag...@gmail.com>
>>> wrote:
>>>
>>>> So, there are exactly two measurements there: matrix multiplication and
>>>> vector addition for dimension 100 (which is quite small and should favor
>>>> vectorz). Here are the results on my machine:
>>>>
>>>> Matrix multiplications are given at the neanderthal web site at
>>>> http://neanderthal.uncomplicate.org/articles/benchmarks.html in much
>>>> more details than that, so I won't repeat that here.
>>>>
>>>> Vector addition according to criterium: 124ns vectorz vs 78ns
>>>> neanderthal on my i7 4790k
>>>>
>>>> Mind you that the project you pointed uses rather old library versions.
>>>> I updated them to the latest versions. Also, the code does not run for both
>>>> old and new versions properly (it complains about :clatrix) so I had to
>>>> evaluate it manually in the repl.
>>>>
>>>> I wonder why you complained that I didn't show more benchmark data
>>>> about my claims when I had shown much more (and relevant) data than it is
>>>> available for core.matrix, but I would use the opportunity to appeal to
>>>> core.matrix community to improve that.
>>>>
>>>> On Monday, June 22, 2015 at 8:13:29 PM UTC+2, Christopher Small wrote:
>>>>>
>>>>> For benchmarking, there's this:
>>>>> https://github.com/mikera/core.matrix.benchmark. It's pretty simple
>>>>> though. It would be nice to see something more robust and composable, and
>>>>> with nicer output options. I'll put a little bit of time into that now, 
>>>>> but
>>>>> again, a bit busy to do as much as I'd like here :-)
>>>>>
>>>>> Chris
>>>>>
>>>>>
>>>>> On Mon, Jun 22, 2015 at 9:14 AM, Dragan Djuric <drag...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>>
>>>>>>> As for performance benchmarks, I have to echo Mike that it seemed
>>>>>>> strange to me that you were claiming you were faster on ALL benchmarks 
>>>>>>> when
>>>>>>> I'd only seen data on one. Would you mind sharing your full benchmarking
>>>>>>> analyses?
>>>>>>>
>>>>>>
>>>>>> I think this might be a very important issue, and I am glad that you
>>>>>> raised it. Has anyone shared any core.matrix (or, to be precise,
>>>>>> core.matrix) benchmark data? I know about Java benchmark code project 
>>>>>> that
>>>>>> include vectorz, but I think it would help core.matrix users to see the
>>>>>> actual numbers. One main thing vectorz (and core.matrix) is claiming is
>>>>>> that it is *fast*. Mike seemed a bit (pleasantly) surprised when I shared
>>>>>> my results for vectorz mmul...
>>>>>>
>>>>>> So, my proposal would be that you (or anyone else able and willing)
>>>>>> create a simple Clojure project that simply lists typical core.matrix use
>>>>>> cases, or just the core procedures in core.matrix code that you want to
>>>>>> measure and that you are interested to see Neanderthal doing. Ready
>>>>>> criterium infrastructure is cool, but I'm not even ask for that if you do
>>>>>> not have time. Just a setup with matrix objects and core.matrix function
>>>>>> calls that you want measured. Share your numbers and that project on 
>>>>>> Github
>>>>>> and I will contribute comparative code for Neanderthal benchmarks, and
>>>>>> results for both codes run on my machine. Of course, that would be micro
>>>>>> benchmarks, but useful anyway for you, one Neanderthal user (me :) and 
>>>>>> for
>>>>>> all core.matrix users.
>>>>>>
>>>>>> You interested?
>>>>>>
>>>>>> With all that out of the way... I'm glad that you're willing to play
>>>>>>> ball here with the core.matrix community, and thank you for what I think
>>>>>>> has been a very productive discussion. I think we all went from talking
>>>>>>> _past_ each other, to understanding what the issues are and can now
>>>>>>> hopefully start moving forward and making things happen. While I think 
>>>>>>> we'd
>>>>>>> all love to have you (Dragan) personally working on the core.matrix
>>>>>>> implementations, I agree with Mars0i that just having you agree to
>>>>>>> work-with/advise others who would do the actual work is great. I'd
>>>>>>> personally love to take that on myself, but I already have about a half
>>>>>>> dozen side projects I'm working on which I barely have time for. Oh, 
>>>>>>> and a
>>>>>>> four month old baby :scream:! So if there's anyone else who's willing, I
>>>>>>> may leave it to them :-)
>>>>>>>
>>>>>>
>>>>>> I'm also glad we understand each other better now :)
>>>>>>
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