Yet, be careful, that way of benchmarking will have a lot of variation and noise. Remember there is an OS, other apps open, even the CPU getting hot/cold can introduce performance differences... At least, that snippet should be run so many times (I do 100 iterations in general), and the averages should be compared taking into account standard deviation. El dom., 9 ene. 2022 10:14, Stéphane Ducasse <stephane.ducasse@inria.fr> escribió:
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.ducasse@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
Iâm 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 < jlhouchin@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 donât 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