2015-01-16 0:48 GMT+01:00 Jimmie Houchin <[email protected]>: > Hello, > > Yes, I understand this is a micro-benchmark. And yes I know that > micro-benchmarks can be dangerous and sometimes worthless. However, > financial trading, which is the app I am working on, does this a lot. This > is precisely where most of the time is spent. Adding data (numbers) or > accessing data in arrays or matrices. This is where my app lives. And > performance is important. > > Regarding my running of the test and the variety of loop sizes. From what > I see in the test there is only one loop size. 10000 > There are differing initial OrderedCollection sizes. Unless I am > misreading the code. > > OrderedCollection new: 100. (capacity 100). > > I don't understand how this could be a capacity of 100 by what seems to me > to be a normal understanding when its initial size is 0 and it can grow > infinitely beyond 100. > > To me if it has a capacity of 100, immediately after creation its size > should be 100 > and its initial 100 elements should be accessible. > oc at: 1 put: 1 should work. > So I do not understand where this supposed capacity enters into anything. > It is neither immediately accessible, nor does it limit. > > It's not specific to OrderedCollection. Dictionary new: 100 and Set new: 100 also behave the same.
It's rather Array that behaves differently than the rest of Collection zoo, because an Array of capacity 100 is also an Array of size 100, same for other ArrayedCollection. So if you re-think of it, you should always understand new: as reserving a certain capacity, which can casually also result in a collection of same size. You should notice the #ofSize: message that was added once upon a time... When I create an Array, it has a capacity with an understanding that I have. > a := Array new: 100. > size a. "100" > a at: 1. "nil" "works" > a at: 1 put: 1. "works" > > I naively expect the same from another Collection which looks like > oc := OrderedCollection new: 100. > Since it is a collection with an initial size (capacity). But because it > is an OrderedCollection and not an Array, it is not limited by its initial > capacity. > Apparently my understanding faulty. I just wanted to express how this > would potentially/probably be understood by someone who does not know this > peculiarity of OrderedCollection. > > Regarding the comparison to Python and Julia. > Mea Culpa, mea culpa, maxima mea culpa. > > While I thought I duplicated the Pharo code. I did not. > I duplicated the inner loop which created and populated the array. > I completely left off the outer loop which ran the inner loop 10000 times. > > The Python code is simple. > > def t(n): > s = time.time() > for x in range(0,10000): > l = [] > for i in range(1,10001): > l.append(i) > e = time.time() > print(e-s, len(l)) > return l > > list_test(10000) # == 15.5 seconds > > Julia > function test_array(asize) > for i = 1:10000 > a = Array(Int64, asize) > for i = 1:asize > a[i]=i > end > end > end > > @time(test_array(10000)) # 0.9 seconds > > So Pharo at 1.6 seconds (Array time) is respectable compared to Julia's > highly optimized preallocated arrays and thoroughly keeps Python in its > place. > > And if I start Julia off with an Array(Int64,0) and do append!(a,[i,]). > Julia's time plummets to 19seconds. > > I feel much better. Thanks for challenging my numbers and insuring my > comparisons were correct. > > Jimmie > > > > > On 1/15/2015 1:13 PM, Sven Van Caekenberghe wrote: > >> Jimmy, >> >> On 15 Jan 2015, at 19:46, Jimmie Houchin <[email protected]> wrote: >>> >>> I do not understand what is happening here. >>> >>> oc := OrderedCollection new: 10000. >>> oc size. "0" >>> >>> a := Array new: 10000. >>> a size. "10000" >>> >>> So it seems that the #new: isn't creating an OrderedCollection of the >>> size we pass in. >>> >>> I discovered this when I attempted to do the identical test but with an >>> Array instead of an OrderedCollection. Since the Array doesn't #add: I >>> had to change the code to #at:put:. >>> >> This is all normal and by design. >> >> OrderedCollection is a growable collection, designed so you can add >> elements at either end. The thread's discussion is about growing strategies. >> >> Consider, >> >> (OrderedCollection new: 100) capacity = 100 >> >> it means it has preallocated room for a 100 elements, but the way it is >> managed is tricky, as people discovered. >> >>> When I noticed all of the OC code did #add: even if supposedly setting >>> its size to 10000(0). I tried to use #at:put: in the OrderedCollection but >>> it failed because their was no index 1, because the size was still 0. >>> >>> I naively would have thought that OrderedCollection new: 10000 would >>> return an OrderedCollection of 10000 empty/nil elements and that I could >>> #at:put: into any of those 10000 slots and #add if I needed to go beyond >>> those 10000. >>> >>> Tests done on a laptop with Windows 7, Pharo 4 latest image, latest >>> stable vm, 3rd gen i7 processor, 12gb ram. >>> >>> I did all of the tests in Pharo 4 and added my Array test. >>> 0:00:00:04.37 >>> 0:00:00:03.183 >>> 0:00:00:03.674 >>> 0:00:00:03.753 >>> 0:00:00:01.647 and with an Array instead of OrderedCollection >>> >>> So we can see if we know the max size and create the collection with >>> that size it should improve performance. >>> >>> I also do not understand why Pharo is so slow on this? >>> >>> With the preallocated Array above it took 1.647 seconds. >>> >>> Python 3.4.2, 0.003 seconds with 10,000 ints, and 0.17 with 1,000,000 >>> ints. >>> Julia 3.5, 0.005 seconds with 1,000,000 Int64. >>> >>> Now I don't expect Pharo to keep up with Julia. But we aren't even in >>> the neighborhood with Python. >>> >>> I really would like to use Pharo. It really is my favorite language and >>> environment. But these performance numbers give me pause. >>> >> There are differently sized loops in this thread, what exactly did you >> try ? >> >> What is the Python code that you ran ? >> >> Benchmarking is always dangerous, you must make absolutely clear what you >> are running and comparing. >> >> Sven >> >> Jimmie >>> >>> >>> On 1/14/2015 3:53 AM, Max Leske wrote: >>> >>>> Hi Alex >>>> >>>> I thought the following little experiment might interest you. >>>> >>>> I was trying to find out (as per your suggestion) how big the >>>> difference would be between: >>>> - setting the array in OrderedCollection to nil and initializing it to >>>> size 10 on first access >>>> - initializing the array in OrderedCollection to size 0. >>>> >>>> Expectation: #grow will take time, ergo, using an empty array should be >>>> slower than using >>>> a preallocated array. >>>> >>>> Experiment (Pharo 1.3 on SqueakVM): >>>> >>>> >>>> [ 10000 timesRepeat: [ | c | c := OrderedCollection new: 100000. >>>> 1 to: 10000 do: [ :i | c add: i ] ] ] timeToRun 17566 >>>> >>>> [ 10000 timesRepeat: [ | c | c := OrderedCollection new: 10000. >>>> 1 to: 10000 do: [ :i | c add: i ] ] ] timeToRun 19717 >>>> >>>> [ 10000 timesRepeat: [ | c | c := OrderedCollection new: 0. >>>> 1 to: 10000 do: [ :i | c add: i ] ] ] timeToRun 17598 >>>> >>>> >>>> >>>> [ 10000 timesRepeat: [ | c | c := OrderedCollection new: 1000. >>>> 1 to: 10000 do: [ :i | c add: i ] ] ] timeToRun 18084 >>>> >>>> >>>> >>>> Result: growing a collection is always faster (or at least nearly >>>> equally fast)! >>>> >>>> Explanation: What makes preinitialized collections so slow is the fact >>>> the the firstIndex variable will be set to >>>> the first third of the array (3333 in this case). When the last >>>> position in the array is reached, all elements >>>> are copied to the beginning of the array (2n operations). This >>>> copy operation is really slow (which can be seen >>>> from the first example with an oversized collection that does >>>> not require copying because the last position >>>> of the array is never reached). >>>> >>>> >>>> Experiment (Pharo 4 on PharoVM): >>>> (I’m using bigger numbers here to compensate for the faster VM :) ) >>>> >>>> >>>> [ 10000 timesRepeat: [ | c | c := OrderedCollection new: 1000000. >>>> 1 to: 100000 do: [ :i | c add: i ] ] ] timeToRun "0:00:01:09.225" >>>> >>>> [ 10000 timesRepeat: [ | c | c := OrderedCollection new: 100000. >>>> 1 to: 100000 do: [ :i | c add: i ] ] ] timeToRun "0:00:00:37.653" >>>> >>>> [ 10000 timesRepeat: [ | c | c := OrderedCollection new: 0. >>>> 1 to: 100000 do: [ :i | c add: i ] ] ] timeToRun "0:00:00:40.152" >>>> >>>> >>>> >>>> [ 10000 timesRepeat: [ | c | c := OrderedCollection new: 10000. >>>> 1 to: 100000 do: [ :i | c add: i ] ] ] timeToRun "0:00:00:42.121” >>>> >>>> >>>> >>>> Result: growing a collection is easily fast enough, although minute >>>> performance gains are possible with preinitialized collections. >>>> >>>> Explanation: #makeRoomAtLast has been improved. Also: firstIndex is now >>>> set to the beginning of the array, preventing the copy >>>> operation that made the former measurements so slow. >>>> The run with the oversized collection is only so slow because >>>> it apparently takes a lot of time to assign the large array (?). >>>> >>>> >>>> Consequence of my little experiment: My initial implementation of the >>>> idea in you paper set the array in OrderedCollection to nil >>>> under the assumption that grow operations are very costly. That >>>> choice leads to many changes in methods of OrderedCollection >>>> where it is necessary to check if the array has been >>>> initialized yet. This introduces overhead and makes the code more error >>>> prone. >>>> I see now that it makes much more sense to use an empty array, >>>> the performance lost by growing ist neglectable. >>>> >>>> It is also interesting to note that there almost never seems to >>>> be a good reason to initialize an OrderedCollection with a large array. >>>> >>>> >>>> Cheers, >>>> Max >>>> >>> >>> >> > >
