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> 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 > "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> 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 >
