[R-SIG-Win] R4.2.3 slower than R4.1.3 on Windows only

[Tomas Kalibera]

On 5/17/23 16:07, Tomas Kalibera wrote:
>
> On 4/18/23 14:16, Fredrik Skoog wrote:
>> Hi,
>>
>> If you run:
>>
>> library(microbenchmark)
>> m <- matrix(rnorm(28000000), nrow=7000, byrow=TRUE)
>> rownames(m) <- rownames(m, do.NULL = FALSE, prefix = "this is a row 
>> name")
>> colnames(m) <- colnames(m, do.NULL = FALSE, prefix = "this is a column
>> name")
>> microbenchmark(df <- as.data.frame(m, keep.rownames=TRUE), times=10)
>>
>> The results shows worse performance in R4.2.3 (also bigger variations)
>> compared to v4.1.3. Also v4.2.0 shows worse performance, so it looks 
>> like
>> it's 4.2.0 and later that has this issue. On Linux it's all good, so it
>> seems to be a Windows only issue.
>>
>> Version 4.2.3
>> ==============
>>
>> Run 1
>> ------
>> Unit: seconds
>>                                           expr      min lq     mean
>>   median       uq      max neval
>>   df <- as.data.frame(m, keep.rownames = TRUE) 1.324839 2.411304 
>> 2.760553
>> 2.593452 3.290228 4.263175    10
>>
>> Run 2
>> ------
>> Unit: milliseconds
>>                                           expr      min lq     mean
>>   median       uq     max neval
>>   dt <- as.data.frame(m, keep.rownames = TRUE) 967.5651 1054.8 1155.453
>> 1149.767 1194.742 1451.14    10
>>
>>
>> Version 4.1.3
>> ===============
>>
>> Run 1:
>> ------
>>
>> Unit: milliseconds
>>                                           expr      min lq     mean
>>   median       uq      max neval
>>   df <- as.data.frame(m, keep.rownames = TRUE) 274.5478 298.2477 
>> 320.3988
>> 320.9164 342.8119 375.6841    10
>>
>> Run 2:
>> -------
>> Unit: milliseconds
>>                                           expr      min lq     mean
>>   median       uq      max neval
>>   df <- as.data.frame(m, keep.rownames = TRUE) 278.5369 310.0312 
>> 313.0745
>> 313.3275 320.0294 343.7539    10
>>
>> I have tried it on two different machines, with the same result.
>>
>> -----
>>
>> The above example is just trying to do something simple that exposes the
>> issue, but as.data.table behaves similarly. Also it shows huge 
>> variations
>> in time. We had a script that ran in 12 minutes in v3.6.3 and it took 18
>> min with v4.2.3, with v4.1.3 it takes around 9 minutes.
>>
>> Has anyone else noticed this? I noticed in the release notes that 
>> Doug Leas
>> malloc was replaced in v4.2.0 and that's a windows only change.
>
> Thanks for the report. I confirm the slowdown with this example and I 
> confirm it is due to the change in memory allocator: I've switched my 
> working copy of R-devel back to the original version of dlmalloc, 
> which removed the slowdown.
>
> Windows 10 (build 19041 and later) allows to choose a more recent 
> SegmentHeap allocator instead of the default Low Fragmentation Heap 
> allocator. It gives almost the same performance with this example as 
> the original version of dlmalloc, without the maintenance overhead of 
> using a custom allocator, so this might be one possible solution.

Hi Fredrik,

we made R-devel use Segment Heap on recent Windows as an experiment. 
Could you please check the performance implications on some real 
application, on which you based the example micro-benchmark? Did it 
improve performance for you?

Indeed, if you have access to some other memory intensive real 
applications with real data, it would be useful to check using that as 
well.

Microbenchmarks are tricky. While yours works much better with Segment 
Heap, my colleague found another one which works much better with Low 
Fragmentation Heap.

Thanks
Tomas

>
> Best
> Tomas
>
>>
>> Best regards,
>>
>> Fredrik
>>
>>     [[alternative HTML version deleted]]
>>
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