—track-allocation still requires guesswork, as optimizations can move the allocation to a different place than you would expect. On April 19, 2015 at 4:36:19 PM, Peter Brady ([email protected]) wrote:
So I discovered the --track-allocation option and now I am really confused: Here's my session: $ julia --track-allocation=all _ _ _ _(_)_ | A fresh approach to technical computing (_) | (_) (_) | Documentation: http://docs.julialang.org _ _ _| |_ __ _ | Type "help()" for help. | | | | | | |/ _` | | | | |_| | | | (_| | | Version 0.3.8-pre+13 (2015-04-17 18:08 UTC) _/ |\__'_|_|_|\__'_| | Commit 0df962d* (2 days old release-0.3) |__/ | x86_64-redhat-linux julia> include("test.jl") test_all (generic function with 1 method) julia> test_unsafe(5) And here's the relevant part of the resulting test.jl.mem file. Note that I commented out some calls to 'size' and replaced with the appropriate hard-coded values but the resulting allocation is the same... Can anyone shed some light on this while I wait for 0.4 to compile? - function update(a::AbstractArray, idx, off) 8151120 for i=1:320 #size(a, idx) 0 a[i] = -10*off+i - end 0 a - end - - function setk_UnSafe{T}(a::Array{T,3}) 760 us = UnsafeSlice(a, 3) 0 for j=1:size(a,2),i=1:size(a,1) 8151120 us.start = (j-1)*320+i #size(a,1)+i - #off = sub2ind(size(a), i, j, 1) 0 update(us, 3, us.start) - end 0 a - end - function test_unsafe(n) 0 a = zeros(Int, (320, 320, 320)) - # warmup 0 setk_UnSafe(a); 0 clear_malloc_data() - #@time ( 0 for i=1:n; setk_UnSafe(a); end - end On Sunday, April 19, 2015 at 2:21:56 PM UTC-6, Peter Brady wrote: @Dahua, thanks for adding an unsafeview! I appreciate how quickly this community responds. I've added the following function to my test.jl script function setk_unsafeview{T}(a::Array{T,3}) for j=1:size(a,2),i=1:size(a,1) off = sub2ind(size(a), i, j, 1) update(unsafe_view(a, i, j, :), 3, off) end a end But I'm not seeing the large increase in performance I was expecting. My timings are now julia> test_all(5); test_stride elapsed time: 2.156173128 seconds (0 bytes allocated) test_view elapsed time: 9.30964534 seconds (94208000 bytes allocated, 0.47% gc time) test_unsafe elapsed time: 2.169307471 seconds (16303000 bytes allocated) test_unsafeview elapsed time: 8.955876793 seconds (90112000 bytes allocated, 0.41% gc time) To be fair, I am cheating a bit with my custom 'UnsafeSlice' since I make only one instance and simply update the offset on each iteration. If I make it immutable and create a new instance on every iteration (as I do for the view and unsafeview), things slow down a little and the allocation goes south: julia> test_all(5); test_stride elapsed time: 2.159909265 seconds (0 bytes allocated) test_view elapsed time: 9.029025282 seconds (94208000 bytes allocated, 0.43% gc time) test_unsafe elapsed time: 2.621667854 seconds (114606240 bytes allocated, 2.41% gc time) test_unsafeview elapsed time: 8.888434466 seconds (90112000 bytes allocated, 0.44% gc time) These are all with 0.3.8-pre. I'll try compiling master and see what happens. I'm still confused about why allocating a single type with a pointer, 2 ints and a tuple costs so much memory though. On Sunday, April 19, 2015 at 11:38:17 AM UTC-6, Tim Holy wrote: It's not just escape analysis, as this (new) issue demonstrates: https://github.com/JuliaLang/julia/issues/10899 --Tim On Sunday, April 19, 2015 12:33:51 PM Sebastian Good wrote: > Their size seems much decreased. I’d imagine to totally avoid allocation in > this benchmark requires an optimization that really has nothing to do with > subarrays per se. You’d have to do an escape analysis and see that Aj never > left sumcols. Not easy in practice, since it’s passed to slice and length, > and you’d have to make sure they didn’t squirrel it away or pass it on to > someone else. Then you could stack allocate it, or even destructure it into > a bunch of scalar mutations on the stack. After eliminating dead code, > you’d end up with a no-allocation loop much like you’d write by hand. This > sort of optimization seems to be quite tricky for compilers to pull off, > but it’s a common pattern in numerical code. > > In Julia is such cleverness left entirely to LLVM, or are there optimization > passes in Julia itself? On April 19, 2015 at 6:49:21 AM, Tim Holy > ([email protected]) wrote: > > Sorry to be slow to chime in here, but the tuple overhaul has landed and > they are still not zero-cost: > > function sumcols(A) > s = 0.0 > for j = 1:size(A,2) > Aj = slice(A, :, j) > for i = 1:length(Aj) > s += Aj[i] > end > end > s > end > > Even in the latest 0.4, this still allocates memory. On the other hand, > while SubArrays allocate nearly 2x more memory than ArrayViews, the speed > of the two (replacing `slice` with `view` above) is, for me, nearly > identical. > > --Tim > > On Friday, April 17, 2015 08:30:27 PM Sebastian Good wrote: > > This was discussed a few weeks ago > > > > https://groups.google.com/d/msg/julia-users/IxrvV8ABZoQ/uWZu5-IB3McJ > > > > I think the bottom line is that the current implementation *should* be > > 'zero-cost' once a set of planned improvements and optimizations take > > place. One of the key ones is a tuple overhaul. > > > > Fair to say it can never be 'zero' cost since there is different inherent > > overhead depending on the type of subarray, e.g. offset, slice, > > re-dimension, etc. however the implementation is quite clever about > > allowing specialization of those. > > > > In a common case (e.g. a constant offset or simple stride) my > > understanding > > is that the structure will be type-specialized and likely stack allocated > > in many cases, reducing to what you'd write by hand. At least this is what > > they're after. > > > > On Friday, April 17, 2015 at 4:24:14 PM UTC-4, Peter Brady wrote: > > > Thanks for the links. I'll check out ArrayViews as it looks like what I > > > was going to do manually without wrapping it in a type. > > > > > > By semi-dim agnostic I meant that the differencing algorithm itself only > > > cares about one dimension but that dimension is different for different > > > directions. Only a few toplevel routines actually need to know about the > > > dimensionality of the problem. > > > > > > On Friday, April 17, 2015 at 2:04:39 PM UTC-6, René Donner wrote: > > >> As far as I have measured it sub in 0.4 is still not cheap, as it > > >> provides the flexibility to deal with all kinds of strides and offsets, > > >> and > > >> the view object itself thus has a certain size. See > > >> https://github.com/rened/FunctionalData.jl#efficiency for a simple > > >> analysis, where the speed is mostly dominated by the speed of the > > >> "sub-view" mechanism. > > >> > > >> To get faster views which require strides etc you can try > > >> https://github.com/JuliaLang/ArrayViews.jl > > >> > > >> What do you mean by semi-dim agnostic? In case you only need indexing > > >> along the last dimension (like a[:,:,i] and a[:,:,:,i]) you can use > > >> > > >> https://github.com/rened/FunctionalData.jl#efficient-views-details > > >> > > >> which uses normal DenseArrays and simple pointer updates internally. It > > >> can also update a view in-place, by just incrementing the pointer. > > >> > > >> Am 17.04.2015 um 21:48 schrieb Peter Brady <[email protected]>: > > >> > Inorder to write some differencing algorithms in a semi-dimensional > > >> > > >> agnostic manner the code I've written makes heavy use of subarrays > > >> which > > >> turn out to be rather costly. I've noticed some posts on the cost of > > >> subarrays here and that things will be better in 0.4. Can someone > > >> comment > > >> on how much better? Would subarray (or anything like it) be on par with > > >> simply passing an offset and stride (constant) and computing the index > > >> myself? I'm currently using the 0.3 release branch.
