Some experiments and discoveries.
I am running my full language test every time. It is the only way I
can compare results. It is also what fully stresses the language.
The reason I wrote the test as I did is because I wanted to know a
couple of things. Is the language sufficiently performant on basic
maths. I am not doing any high PolyMath level math. Simple things
like moving averages over portions of arrays.
The other is efficiency of array iteration and access. This why #sum
is the best test of this attribute. #sum iterates and accesses every
element of the array. It will reveal if there are any problems.
The default test Julia 1m15s, Python 24.5 minutes, Pharo 2hour
4minutes.
When I comment out the #sum and #average calls, Pharo completes the
test in 3.5 seconds. So almost all the time is spent in those two
calls.
So most of this conversation has focused on why #sum is as slow as it
is or how to improve the performance of #sum with other
implementations.
So I decided to breakdown the #sum and try some things.
Starting with the initial implementation and SequenceableCollection's
default #sum time of 02:04:03
"This implementation does no work. Only iterates through the array.
It completed in 00:10:08"
sum
| sum |
sum := 1.
1 to: self size do: [ :each | ].
^ sum
"This implementation does no work, but adds to iteration, accessing
the value of the array.
It completed in 00:32:32.
Quite a bit of time for simply iterating and accessing."
sum
| sum |
sum := 1.
1 to: self size do: [ :each | self at: each ].
^ sum
"This implementation I had in my initial email as an experiment and
also several other did the same in theirs.
A naive simple implementation.
It completed in 01:00:53. Half the time of the original."
sum
| sum |
sum := 0.
1 to: self size do: [ :each |
sum := sum + (self at: each) ].
^ sum
"This implementation I also had in my initial email as an experiment
I had done.
It completed in 00:50:18.
It reduces the iterations and increases the accesses per iteration.
It is the fastest implementation so far."
sum
| sum |
sum := 0.
1 to: ((self size quo: 10) * 10) by: 10 do: [ :i |
sum := sum + (self at: i) + (self at: (i + 1)) + (self at:
(i + 2)) + (self at: (i + 3)) + (self at: (i + 4)) +
(self at: (i + 5)) + (self at: (i + 6)) + (self at: (i + 7)) + (self
at: (i + 8)) + (self at: (i + 9))].
((self size quo: 10) * 10 + 1) to: self size do: [ :i |
sum := sum + (self at: i)].
^ sum
Summary
For whatever reason iterating and accessing on an Array is expensive.
That alone took longer than Python to complete the entire test.
I had allowed this knowledge of how much slower Pharo was to stop me
from using Pharo. Encouraged me to explore other options.
I have the option to use any language I want. I like Pharo. I do not
like Python at all. Julia is unexciting to me. I don't like their
anti-OO approach.
At one point I had a fairly complete Pharo implementation, which is
where I got frustrated with backtesting taking days.
That implementation is gone. I had not switched to Iceberg. I had a
problem with my hard drive. So I am starting over.
I am not a computer scientist, language expert, vm expert or anyone
with the skills to discover and optimize arrays. So I will end my
tilting at windmills here.
I value all the other things that Pharo brings, that I miss when I am
using Julia or Python or Crystal, etc. Those languages do not have
the vision to do what Pharo (or any Smalltalk) does.
Pharo may not optimize my app as much as x,y or z. But Pharo
optimized me.
That said, I have made the decision to go all in with Pharo. Set
aside all else.
In that regard I went ahead and put my money in with my decision and
joined the Pharo Association last week.
Thanks for all of your help in exploring the problem.
Jimmie Houchin
