On my machine so this is the same. SQ5.3
Test with 10000000 elements Original #sum -> Time: 196 milliseconds, Total: 5.001448710680429e6 Naive #sum -> Time: 152 milliseconds, Total: 5.001448710680429e6 Inject #sum -> Time: 143 milliseconds, Total: 5.001448710680429e6 > On 8 Jan 2022, at 21:47, stephane ducasse <stephane.duca...@inria.fr> wrote: > > Thanks benoit for the snippet > I run it in Pharo 10 and I got > > Test with 10000000 elements > Original #sum -> Time: 195 milliseconds, Total: 4.999452880735064e6 > Naive #sum -> Time: 153 milliseconds, Total: 4.999452880735063e6 > Inject #sum -> Time: 198 milliseconds, Total: 4.999452880735063e6 > > > in Pharo 9 > Test with 10000000 elements > Original #sum -> Time: 182 milliseconds, Total: 4.999339450212771e6 > Naive #sum -> Time: 148 milliseconds, Total: 4.999339450212771e6 > Inject #sum -> Time: 203 milliseconds, Total: 4.999339450212771e6 > > I’m interested to understand why Pharo is slower. May be this is the impact > of the new full blocks. > We started to play with the idea of regression benchmarks. > > S > > >> On 7 Jan 2022, at 16:36, Benoit St-Jean via Pharo-dev >> <pharo-dev@lists.pharo.org <mailto:pharo-dev@lists.pharo.org>> wrote: >> >> Can you come up with a simple "base case" so we can find the >> bottleneck/problem? >> >> I'm not sure about what you're trying to do. >> >> What do you get if you try this in a workspace (adjust the value of n to >> what you want, I tested it with 10 million items). >> >> Let's get this one step at a time! >> >> >> >> | floatArray n rng t1 t2 t3 r1 r2 r3 | >> >> n := 10000000. >> >> rng := Random new. >> >> floatArray := Array new: n. >> floatArray doWithIndex: [:each :idx | floatArray at: idx put: rng next]. >> >> t1 := Time millisecondsToRun: [r1 := floatArray sum]. >> t2 := Time millisecondsToRun: [| total | >> >> total := 0. >> floatArray do: >> [:each | total := total + each ]. >> r2 := total]. >> >> t3 := Time millisecondsToRun: [r3 := floatArray inject: 0 into: [: total >> :each | total + each ]]. >> >> Transcript cr. >> Transcript cr; show: 'Test with ', n printString, ' elements'. >> Transcript cr;show: 'Original #sum -> Time: ', t1 printString, ' >> milliseconds, Total: ', r1 printString. >> Transcript cr;show: 'Naive #sum -> Time: ', t2 printString, ' milliseconds, >> Total: ', r2 printString. >> Transcript cr;show: 'Inject #sum -> Time: ', t3 printString, ' milliseconds, >> Total: ', r3 printString. >> >> -------------------------- >> >> Here are the results I get on Squeak 5.3 >> >> Test with 10000000 elements >> Original #sum -> Time: 143 milliseconds, Total: 4.999271889099622e6 >> Naive #sum -> Time: 115 milliseconds, Total: 4.999271889099622e6 >> Inject #sum -> Time: 102 milliseconds, Total: 4.999271889099622e6 >> >> >> >> ----------------- >> Benoît St-Jean >> Yahoo! Messenger: bstjean >> Twitter: @BenLeChialeux >> Pinterest: benoitstjean >> Instagram: Chef_Benito >> IRC: lamneth >> GitHub: bstjean >> Blogue: endormitoire.wordpress.com <http://endormitoire.wordpress.com/> >> "A standpoint is an intellectual horizon of radius zero". (A. Einstein) >> >> >> On Thursday, January 6, 2022, 03:38:22 p.m. EST, Jimmie Houchin >> <jlhouc...@gmail.com <mailto:jlhouc...@gmail.com>> wrote: >> >> >> I have written a micro benchmark which stresses a language in areas >> which are crucial to my application. >> >> I have written this micro benchmark in Pharo, Crystal, Nim, Python, >> PicoLisp, C, C++, Java and Julia. >> >> On my i7 laptop Julia completes it in about 1 minute and 15 seconds, >> amazing magic they have done. >> >> Crystal and Nim do it in about 5 minutes. Python in about 25 minutes. >> Pharo takes over 2 hours. :( >> >> In my benchmarks if I comment out the sum and average of the array. It >> completes in 3.5 seconds. >> And when I sum the array it gives the correct results. So I can verify >> its validity. >> >> To illustrate below is some sample code of what I am doing. I iterate >> over the array and do calculations on each value of the array and update >> the array and sum and average at each value simple to stress array >> access and sum and average. >> >> 28800 is simply derived from time series one minute values for 5 days, 4 >> weeks. >> >> randarray := Array new: 28800. >> >> 1 to: randarray size do: [ :i | randarray at: i put: Number random ]. >> >> randarrayttr := [ 1 to: randarray size do: [ :i | "other calculations >> here." randarray sum. randarray average ]] timeToRun. >> >> randarrayttr. "0:00:00:36.135" >> >> >> I do 2 loops with 100 iterations each. >> >> randarrayttr * 200. "0:02:00:27" >> >> >> I learned early on in this adventure when dealing with compiled >> languages that if you don’t do a lot, the test may not last long enough >> to give any times. >> >> Pharo is my preference. But this is an awful big gap in performance. >> When doing backtesting this is huge. Does my backtest take minutes, >> hours or days? >> >> I am not a computer scientist nor expert in Pharo or Smalltalk. So I do >> not know if there is anything which can improve this. >> >> >> However I have played around with several experiments of my #sum: method. >> >> This implementation reduces the time on the above randarray in half. >> >> sum: col >> | sum | >> sum := 0. >> 1 to: col size do: [ :i | >> sum := sum + (col at: i) ]. >> ^ sum >> >> randarrayttr2 := [ 1 to: randarray size do: [ :i | "other calculations >> here." >> ltsa sum: randarray. ltsa sum: randarray ]] timeToRun. >> randarrayttr2. "0:00:00:18.563" >> >> And this one reduces it a little more. >> >> sum10: col >> | sum | >> sum := 0. >> 1 to: ((col size quo: 10) * 10) by: 10 do: [ :i | >> sum := sum + (col at: i) + (col at: (i + 1)) + (col at: (i + 2)) + >> (col at: (i + 3)) + (col at: (i + 4)) >> + (col at: (i + 5)) + (col at: (i + 6)) + (col at: (i + 7)) + >> (col at: (i + 8)) + (col at: (i + 9))]. >> ((col size quo: 10) * 10 + 1) to: col size do: [ :i | >> sum := sum + (col at: i)]. >> ^ sum >> >> randarrayttr3 := [ 1 to: randarray size do: [ :i | "other calculations >> here." >> ltsa sum10: randarray. ltsa sum10: randarray ]] timeToRun. >> randarrayttr3. "0:00:00:14.592" >> >> It closes the gap with plain Python3 no numpy. But that is a pretty low >> standard. >> >> Any ideas, thoughts, wisdom, directions to pursue. >> >> Thanks >> >> Jimmie >> >
