On 11 Jan 2022, at 11:17, Sven Van Caekenberghe sven@stfx.eu wrote:

which would seem to be 3 times faster !

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
 

Hi Jimmie,

I was scanning through this thread and saw that the Python call uses
the sum function. If I remember correctly, in Python the built-in sum
function is directly implemented in C [1] (unless Python is compiled
with SLOW_SUM set to true). In that case on large arrays the function
can easily be several times faster than just iterating over the
individual objects as the Pharo code does. The benchmark seems to
compare summing numbers in C with summing numbers in Pharo. Would be
interesting to modify the Python code to use a similar loop as in
Pharo for doing the sum.

Cheers,
Andrei

[1] https://github.com/python/cpython/blob/135cabd328504e1648d17242b42b675cdbd0193b/Python/bltinmodule.c#L2461

On Mon, Jan 10, 2022 at 9:06 PM Jimmie Houchin jlhouchin@gmail.com wrote:

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

Hi,

don'tforget to weight in your time too.

The ease to develop AND evolve a program is an important aspect that
the benchmarks don't show.

Nowdays, developer time count often more than processing time because
you may easily spent days on a nasty bug or an unplanned evolution.

have a nice day

nicolas

On Mon, 2022-01-10 at 14:05 -0600, Jimmie Houchin wrote:

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

On 11 Jan 2022, at 10:08, Andrei Chis chisvasileandrei@gmail.com wrote:

Hi Jimmie,

I was scanning through this thread and saw that the Python call uses
the sum function. If I remember correctly, in Python the built-in sum
function is directly implemented in C [1] (unless Python is compiled
with SLOW_SUM set to true). In that case on large arrays the function
can easily be several times faster than just iterating over the
individual objects as the Pharo code does. The benchmark seems to
compare summing numbers in C with summing numbers in Pharo. Would be
interesting to modify the Python code to use a similar loop as in
Pharo for doing the sum.

Cheers,
Andrei

[1] https://github.com/python/cpython/blob/135cabd328504e1648d17242b42b675cdbd0193b/Python/bltinmodule.c#L2461

On Mon, Jan 10, 2022 at 9:06 PM Jimmie Houchin jlhouchin@gmail.com wrote:

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

On 12 Jan 2022, at 16:51, Sven Van Caekenberghe sven@stfx.eu wrote:

Yes that would certainly be useful.

But, AFAIU, FloatArray consists of 32-bit Float numbers, I think we also need a DoubleFloatArray since 64-bit Floats are the default nowadays.

On 12 Jan 2022, at 16:31, Henrik Sperre Johansen henrik.s.johansen@veloxit.no wrote:

We could also try modifying Pharo to use C by reintroducing the FloatArray plugin ;)

| fa r |
fa := FloatArray new: 28800.
r := Random new.
1 to fa size do: [ :i | fa at: i put: r next ].
[ 1 to: fa size do: [ :i | fa sum ] ] timeToRun

Pharo 9, no plugin:
0:00:01:14.777
Pharo 5, with plugin:
0:00:00:00.526

Cheers,
Henry

On 11 Jan 2022, at 10:08, Andrei Chis chisvasileandrei@gmail.com wrote:

Hi Jimmie,

I was scanning through this thread and saw that the Python call uses
the sum function. If I remember correctly, in Python the built-in sum
function is directly implemented in C [1] (unless Python is compiled
with SLOW_SUM set to true). In that case on large arrays the function
can easily be several times faster than just iterating over the
individual objects as the Pharo code does. The benchmark seems to
compare summing numbers in C with summing numbers in Pharo. Would be
interesting to modify the Python code to use a similar loop as in
Pharo for doing the sum.

Cheers,
Andrei

[1] https://github.com/python/cpython/blob/135cabd328504e1648d17242b42b675cdbd0193b/Python/bltinmodule.c#L2461

On Mon, Jan 10, 2022 at 9:06 PM Jimmie Houchin jlhouchin@gmail.com wrote:

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

On 12 Jan 2022, at 17:38, Henrik Sperre Johansen henrik.s.johansen@veloxit.no wrote:

True!
It’s a little bit of a naming conundrum, since the «Float» in Pharo is already 64-bit, but since we’re speaking «native» arrays,
DoubleArray
would be the best, I guess.

Speaking of, the related new (… to me, anyways) DoubleByte/DoubleWordArray classes have incorrect definitions in Pharo 9 AFAICT- variableByte/WordSubclasses, instead of variableDoubleByte/variableDoubleWordSubclasses…

On 12 Jan 2022, at 17:38, Henrik Sperre Johansen <
henrik.s.johansen@veloxit.no> wrote:

True!
It’s a little bit of a naming conundrum, since the «Float» in Pharo is
already 64-bit, but since we’re speaking «native» arrays,
DoubleArray
would be the best, I guess.

Speaking of, the related new (… to me, anyways) DoubleByte/DoubleWordArray
classes have incorrect definitions in Pharo 9 AFAICT-
variableByte/WordSubclasses, instead of
variableDoubleByte/variableDoubleWordSubclasses…

We finally fixed this in Pharo10 2 days ago:
https://github.com/pharo-project/pharo/pull/9792

Marcus