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<font size="-1"><font face="Helvetica, Arial, sans-serif">Hi Chide,<br>
<br>
For data-intensive programming it will be worthwhile to design
your data structures carefully. This depends of course on your
application.<br>
The Clean compiler offers you unique arrays that might be pretty
suited here. The uniqueness enforces restrictions on the way you
write your algorithms. The benefit is that all updates will be
done in-place, i.e. without creating garbage.<br>
When you use an unboxed array the elements become strict, but
access and storage is very efficient.<br>
<br>
Below is an example of a program that creates matrix of the
suggested size and applies a function to all its elements. My
old laptop executes this in 0.25 s.<br>
<br>
Have fun,<br>
<br>
Pieter<br>
<br>
:: *Matrix :== {#{#Int}}<br>
<br>
matrix :: !Int !Int -> Matrix<br>
matrix n m = {{ j \\ j <- [i..i+n-1]} \\ i <- [0..m-1]}<br>
<br>
mapMatrix :: (Int -> Int) !Matrix -> Matrix<br>
mapMatrix f matrix = {{f e \\ e <-: a} \\ a <-: matrix}<br>
<br>
test = (mapMatrix ((+) 7) myMatrix).[m-1].[n-1]<br>
where<br>
myMatrix = matrix n m<br>
n = 30; m = 100000<br>
<br>
Start = test<br>
</font></font><br>
On 15/12/2011 10:57 AM, Groenouwe, C. wrote:
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I'm trying to find out:<br>
- How, in general, functional programming languages perform on
data-intensive tasks (manipulation of large datasets, e.g.:
doing some statistical analysis on a table with 100.000
instances and 30 columns) (regarding speed and memory usage)<br>
- Which functional language performs best?<br>
<br>
A quick glance at the following benchmark, gave me the
impression that Clean and Caml seem to perform best with
regard to memory consumption:<br>
<br>
<a moz-do-not-send="true"
href="http://shootout.alioth.debian.org/" target="_blank">http://shootout.alioth.debian.org/</a><br>
<br>
Is that true?<br>
<br>
Additional question: which functional languages exploits
(hardware) parallelism running on a multi core CPU best? (Or
more CPU's)?
<br>
<br>
Thanks in advance,<br>
<br>
Chide<br>
<br>
<br>
<br>
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