[squeakdev] Interesting survey about smalltalk
Jimmie Houchin
jdev at cyberhaus.us
Mon Jun 21 04:52:02 UTC 2010
On 6/20/2010 10:41 AM, Lawson English wrote:
> On 6/20/10 6:08 AM, Nicolas Cellier wrote:
>> 2010/6/20 Michael Haupt<mhaupt at gmail.com>:
>>> Hi Nicolas,
>>>
>>> On Sun, Jun 20, 2010 at 11:17 AM, Nicolas Cellier
>>> <nicolas.cellier.aka.nice at gmail.com> wrote:
>>>> About 8) : True, every single operation results in memory allocation
>>>> / garbage collection, a burden for number crunching.
>>> really?
>>>
>>> There is this nice book by Didier Besset called "ObjectOriented
>>> Implementation of Numerical Methods. An Introduction with Java and
>>> Smalltalk.: An Introduction with Java and Smalltalk". It can't be
>>> *that* bad. :)
>> Agree, "not worse than Matlab" was the meaning of my message.
>>>> My own answer was: use C/FORTRAN for optimized number crunching
>>>> functions. Use Smalltalk for any higher level/GUI function (via
>>>> DLLCC/FFI). We may have more than 1 hammer in your toolset!
>>> With GPU connectivity things emerging, number crunching might even be
>>> an interesting area for Smalltalk.
>>>
>>> Best,
>>> Michael
>> Yes, this falls in vectorizing the operations.
>> But I would go for a GPUBLAS implementation available to any language
>> (Smalltalk and C as well).
>>
>> Nicolas
> How many parallel squeak processes would be required to = the speed of
> one native library for arbitrary precision math, or for other math
> intensive purposes?
>
> Lawson
Hello,
I would love to be using Squeak for my financial application. Numerical
performance isn't currently what is stopping me. My problem is that I
require interfacing with a Windows COM dll and in a future version with
a Java library. Hopefully at some point I will be able to port to
Squeak. I would much prefer it to using Python, which is what I am
currently using.
I didn't even know Squeak was in the running until I discovered the
Matrix class. And for what I need to do it performs reasonably
adequately. However Squeak does not to my knowledge have a comprehensive
collection of mathematics methods to be able to be applied to a variety
of data. Currently I am using Python and Numpy which has a nicely
optimized Mathematics/Scientific set of functions using optimized
C/Fortran libraries. I would love to see Squeak compete in this area. In
fact the Numpy people are currently refactoring the library to turn it
into a C library usable by other languages.
Here is some samples from my experimentation.
Some of what I am doing is doing rolling calculations over my dataset.
dataset is one weeks worth of OHLC data of a currency pair.
In Squeak I have.
ttr := [
1 to: ((m rowCount) 500) do: [:i  row rowSum rowMax rowMin
rowMedian rowAverage 
row := (m atRows: i to: (499+i) columns: 5 to: 5).
rowSum := row sum.
rowMax := row max.
rowMin := row min.
rowMedian := row median.
rowAverage := row average.
omd add: {rowSum . rowMax . rowMin . rowMedian . rowAverage}]] timeToRun.
Squeak: 17 seconds, with Cog 4.2 seconds (nice work guys
(Eliot/Teleplace)
In Python/Numpy I have.
import numpy as np
def speedtest(array,omd):
t1 = time.time()
for i in range(0, (len(a)500)):
rowmax = np.max(a['bidclose'][i:i+500])
rowmin = np.min(a['bidclose'][i:i+500])
rowsum = np.sum(a['bidclose'][i:i+500])
rowmedian = np.median(a['bidclose'][i:i+500])
rowmean = np.mean(a['bidclose'][i:i+500])
omd.append((rowsum, rowmax, rowmin, rowmedian, rowmean))
return time.time()t1
Python: .7 seconds
Python/Numpy performs well, is reasonably nice to work with. But I would
give up the performance to be able to use Squeak. The live environment
and debugging would be invaluable for experimentation.
Hopefully this will give you some idea.
Jimmie
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