Berend, et. al.:
"I don't quite understand why I get a very different ranking."
Nor do I. But, as Peter noted, my matrix multiplication solution
"should" be worst in terms of efficiency, and it clearly is. The other
two *should* be "similar", and they are. So your results are
consistent with what one should expect, whereas, as Peter noted,
Jeff's are not.
But as we all know, *actual* efficiency in use can be tricky to
determine (asymptopia is always questionable), as it may depend on
state, problem details, exact algorithms in use, etc. Which is why the
standard first principle in code optimization is: don't.
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Fri, Nov 4, 2016 at 11:27 AM, Berend Hasselman <b...@xs4all.nl> wrote:
>
>> On 4 Nov 2016, at 16:41, Jeff Newmiller <jdnew...@dcn.davis.ca.us> wrote:
>>
>> Sara wins on memory use.
>>
>> Rui wins on speed.
>>
>> Bert wins on clarity.
>>
>> library(microbenchmark)
>>
>> N <- 1000
>> x <- matrix( runif( N*N ), ncol=N )
>> y <- seq.int( N )
>>
>> microbenchmark( { t( y * t(x) ) }
>> , { x %*% diag( y ) }
>> , { sweep( x, 2, y, `*` ) }
>> )
>> Unit: milliseconds
>> expr min lq median uq max
>> neval
>> { t(y * t(x)) } 6.659562 7.475414 7.871341 8.182623 47.01105
>> 100
>> { x %*% diag(y) } 9.859292 11.014021 11.281334 11.733825 48.79463
>> 100
>> { sweep(x, 2, y, `*`) } 16.535938 17.682175 18.283572 18.712342 55.47159
>> 100
>
>
> I get different results with R3.2.2 on Mac OS X (using reference BLAS).
>
>
> library(rbenchmark)
>
> N <- 1000
> x <- matrix( runif( N*N ), ncol=N )
> y <- seq.int( N )
>
> benchmark( t( y * t(x) ), x %*% diag( y ), sweep( x, 2, y, `*` ) ,
> columns=c("test","elapsed","relative"), replications=10
> )
>
> # test elapsed relative
> # 3 sweep(x, 2, y, `*`) 0.132 1.000
> # 1 t(y * t(x)) 0.189 1.432
> # 2 x %*% diag(y) 7.928 60.061
>
> library(microbenchmark)
> microbenchmark( { t( y * t(x) ) }
> , { x %*% diag( y ) }
> , { sweep( x, 2, y, `*` ) },
> times=10, unit="s"
> )
>
> # Unit: seconds
> # expr min lq mean median uq
> max neval cld
> # { t(y * t(x)) } 0.01099410 0.01132096 0.01597058 0.01332414
> 0.01430672 0.04447432 10 a
> # { x %*% diag(y) } 0.76588887 0.78581717 0.79878255 0.79811626
> 0.80607877 0.82903945 10 b
> # { sweep(x, 2, y, `*`) } 0.01177478 0.01246777 0.01409457 0.01314718
> 0.01600818 0.01802171 10 a
>
>
> sweep appears to very good.
>
> I don't quite understand why I get a very different ranking.
>
> Berend
>
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