Hmmm, yes, there must be some special case in the C code to avoid
recycling a length-1 logical vector:
dims <- c(4, 4, 4, 1e5)
arr <- array(rnorm(prod(dims)), dims)
dim(arr)
#> [1] 4 4 4 100000
i <- c(1, 3)
bench::mark(
arr[i, TRUE, TRUE, TRUE],
arr[i, , , ]
)[c("expression", "min", "mean", "max")]
#> # A tibble: 2 x 4
#> expression min mean max
#> <chr> <bch:tm> <bch:tm> <bch:tm>
#> 1 arr[i, TRUE, TRUE, TRUE] 41.8ms 43.6ms 46.5ms
#> 2 arr[i, , , ] 41.7ms 43.1ms 46.3ms
On Fri, Jun 8, 2018 at 12:31 PM, Berry, Charles <ccbe...@ucsd.edu> wrote:
>
>
>> On Jun 8, 2018, at 11:52 AM, Hadley Wickham <h.wick...@gmail.com> wrote:
>>
>> On Fri, Jun 8, 2018 at 11:38 AM, Berry, Charles <ccbe...@ucsd.edu> wrote:
>>>
>>>
>>>> On Jun 8, 2018, at 10:37 AM, Hervé Pagès <hpa...@fredhutch.org> wrote:
>>>>
>>>> Also the TRUEs cause problems if some dimensions are 0:
>>>>
>>>>> matrix(raw(0), nrow=5, ncol=0)[1:3 , TRUE]
>>>> Error in matrix(raw(0), nrow = 5, ncol = 0)[1:3, TRUE] :
>>>> (subscript) logical subscript too long
>>>
>>> OK. But this is easy enough to handle.
>>>
>>>>
>>>> H.
>>>>
>>>> On 06/08/2018 10:29 AM, Hadley Wickham wrote:
>>>>> I suspect this will have suboptimal performance since the TRUEs will
>>>>> get recycled. (Maybe there is, or could be, ALTREP, support for
>>>>> recycling)
>>>>> Hadley
>>>
>>>
>>> AFAICS, it is not an issue. Taking
>>>
>>> arr <- array(rnorm(2^22),c(2^10,4,4,4))
>>>
>>> as a test case
>>>
>>> and using a function that will either use the literal code
>>> `x[i,,,,drop=FALSE]' or `eval(mc)':
>>>
>>> subset_ROW4 <-
>>> function(x, i, useLiteral=FALSE)
>>> {
>>> literal <- quote(x[i,,,,drop=FALSE])
>>> mc <- quote(x[i])
>>> nd <- max(1L, length(dim(x)))
>>> mc[seq(4,length=nd-1L)] <- rep(TRUE, nd-1L)
>>> mc[["drop"]] <- FALSE
>>> if (useLiteral)
>>> eval(literal)
>>> else
>>> eval(mc)
>>> }
>>>
>>> I get identical times with
>>>
>>> system.time(for (i in 1:10000)
>>> subset_ROW4(arr,seq(1,length=10,by=100),TRUE))
>>>
>>> and with
>>>
>>> system.time(for (i in 1:10000)
>>> subset_ROW4(arr,seq(1,length=10,by=100),FALSE))
>>
>> I think that's because you used a relatively low precision timing
>> mechnaism, and included the index generation in the timing. I see:
>>
>> arr <- array(rnorm(2^22),c(2^10,4,4,4))
>> i <- seq(1,length = 10, by = 100)
>>
>> bench::mark(
>> arr[i, TRUE, TRUE, TRUE],
>> arr[i, , , ]
>> )
>> #> # A tibble: 2 x 1
>> #> expression min mean median max n_gc
>> #> <chr> <bch:t> <bch:t> <bch:tm> <bch:tm> <dbl>
>> #> 1 arr[i, TRUE,… 7.4µs 10.9µs 10.66µs 1.22ms 2
>> #> 2 arr[i, , , ] 7.06µs 8.8µs 7.85µs 538.09µs 2
>>
>> So not a huge difference, but it's there.
>
>
> Funny. I get similar results to yours above albeit with smaller differences.
> Usually < 5 percent.
>
> But with subset_ROW4 I see no consistent difference.
>
> In this example, it runs faster on average using `eval(mc)' to return the
> result:
>
>> arr <- array(rnorm(2^22),c(2^10,4,4,4))
>> i <- seq(1,length=10,by=100)
>> bench::mark(subset_ROW4(arr,i,FALSE), subset_ROW4(arr,i,TRUE))[,1:8]
> # A tibble: 2 x 8
> expression min mean median max `itr/sec`
> mem_alloc n_gc
> <chr> <bch:tm> <bch:tm> <bch:tm> <bch:tm> <dbl>
> <bch:byt> <dbl>
> 1 subset_ROW4(arr, i, FALSE) 28.9µs 34.9µs 32.1µs 1.36ms 28686.
> 5.05KB 5
> 2 subset_ROW4(arr, i, TRUE) 28.9µs 35µs 32.4µs 875.11µs 28572.
> 5.05KB 5
>>
>
> And on subsequent reps the lead switches back and forth.
>
>
> Chuck
>
--
http://hadley.nz
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