[squeakdev] Interesting survey about smalltalk
Nicolas Cellier
nicolas.cellier.aka.nice at gmail.com
Mon Jun 21 12:17:41 UTC 2010
It is available in squeaksource.
However, there is a compiler hack to be installed before loading the
package, in order to handle function call with more than 15 arguments.
This hack is to be updated because of numerous Compiler changes
related to closure.
I have some unpublished update.
Which version of Squeak/pharo do you use ?
Nicolas
2010/6/21 arul selvan <arul.selvan at gmail.com>:
> nicolas,
>
> i will be interested to test if a port for squeak is available
>
> arul
>
>
> On Mon, Jun 21, 2010 at 12:41 PM, Nicolas Cellier
> <nicolas.cellier.aka.nice at gmail.com> wrote:
>> I started Smallapack for this purpose.
>> Smallapack interfaces BLAS+LAPACK thru FFI.
>> Very much like numpy...
>>
>> Smallapack works quite well in VW and Dolphin...
>> (In fact it did work very well since 1990 with st80 user primitives...)
>> ...Unfirtunately not so in Squeak (VM crach possibly).
>>
>> I'd like to put more time in it, but so far there has not been so much interest.
>>
>> Nicolas
>>
>> 2010/6/21 Jimmie Houchin <jdev at cyberhaus.us>:
>>> 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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