No, it is an array of floats. The only integers in the test are in the
indexes of the loops.
Number random. "generates a float 0.8188008774329387"
So in the randarray below it is an array of 28800 floats.
It just felt so wrong to me that Python3 was so much faster. I don't
care if Nim, Crystal, Julia are faster. But...
I am new to Iceberg and have never shared anything on Github so this is
all new to me. I uploaded my language test so you can see what it does.
It is a micro-benchmark. It does things that are not realistic in an
app. But it does stress a language in areas important to my app.
https://github.com/jlhouchin/LanguageTestPharo
Let me know if there is anything else I can do to help solve this problem.
I am a lone developer in my spare time. So my apologies for any ugly code.
Thanks for your help.
Jimmie
On 1/6/22 15:07, Guillermo Polito wrote:
Hi Jummie,
Is it possible that your program is computing a lot of **very** large integers?
I’m just trying the following with small numbers, and I don’t see the issue.
#sum executes on a 28k large collection around 20 million times per second on
my old 2015 i5.
a := (1 to: 28000).
[a sum] bench "'20256552.490 per second’"
If you could share with us more data, we could take a look.
Now i’m curious.
Thanks,
G
El 6 ene 2022, a las 21:37, Jimmie Houchin <[email protected]> escribió:
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
