Re: [Pharo-dev] FloatArray

I wasn't worried about how to do sliding windows. My problem is that using LapackDGEMatrix in my example was 18x slower than FloatArray, which is slower than Numpy. It isn't what I was expecting. What I didn't know is if I was doing something wrong to cause such a tremendous slow down. Python and Numpy is not my favorite. But it isn't uncomfortable. So I gave up and went back to Numpy. Thanks. On 5/20/19 5:17 PM, Nicolas Cellier wrote:

Hi Jimmie, effectively I did not subsribe... Having efficient methods for sliding window average is possible, here is how I would do it:

"Create a vector with 100,000 rows filles with random values (uniform distrubution in [0,1]" v := LapackDGEMatrix randUniform: #(100000 1).

"extract values from rank 10001 to 20000" w1 := v atIntervalFrom: 10001 to: 20000 by: 1.

"create a left multiplier matrix for performing average of w1" a := LapackDGEMatrix nrow: 1 ncol: w1 nrow withAll: 1.0 / w1 size.

"get the average (this is a 1x1 matrix from which we take first element)" avg1 := (a * w1) at: 1.

[ "select another slice of same size" w2 := v atIntervalFrom: 15001 to: 25000 by: 1.

"get the average (we can recycle a)" avg2 := (a * w2) at: 1 ] bench.

This gives:  '16,500 per second. 60.7 microseconds per run.' versus: [w2 sum / w2 size] bench.  '1,100 per second. 908 microseconds per run.'

For max and min, it's harder. Lapack/Blas only provide max of absolute value as primitive: [w2 absMax] bench.  '19,400 per second. 51.5 microseconds per run.'

Everything else will be slower, unless we write new primitives in C and connect them... [w2 maxOf: [:each | each]] bench.  '984 per second. 1.02 milliseconds per run.'

Le dim. 19 mai 2019 à 14:58, Jimmie <jlhouchin@gmail.com <mailto:jlhouchin@gmail.com>> a écrit :

On 5/16/19 1:26 PM, Nicolas Cellier wrote:> Any feedback on this?  > Did someone tried to use Smallapack in Pharo?  > Jimmie?  >

I am going to guess that you are not on pharo-users. My bad. I posted this in pharo-users as I it wasn't Pharo development question.

I probably should have posted here or emailed you directly.

All I really need is good performance with a simple array of floats. No matrix math. Nothing complicated. Moving Averages over a slice of the array. A variety of different averages, weighted, etc. Max/min of the array. But just a single simple array.

Any help greatly appreciated.

Thanks.

On 4/28/19 8:32 PM, Jimmie Houchin wrote: Hello,

I have installed Smallapack into Pharo 7.0.3. Thanks Nicholas.

I am very unsure on my use of Smallapack. I am not a mathematician or scientist. However the only part of Smallapack I am trying to use at the moment is something that would  be 64bit and compare to FloatArray so that I can do some simple accessing, slicing, sum, and average on the array.

Here is some sample code I wrote just to play in a playground.

I have an ExternalDoubleArray, LapackDGEMatrix, and a FloatArray samples. The ones not in use are commented out for any run.

fp is a download from http://ratedata.gaincapital.com/2018/12%20December/EUR_USD_Week1.zip and unzipped to a directory.

fp := '/home/jimmie/data/EUR_USD_Week1.csv' index := 0. pricesSum := 0. asum := 0. ttr := [      lines := fp asFileReference contents lines allButFirst.      a := ExternalDoubleArray new: lines size.      "la := LapackDGEMatrix allocateNrow: lines size ncol: 1.      a := la columnAt: 1."      "a := FloatArray new: lines size."      lines do: [ :line || parts price |          parts := ',' split: line.          index := index + 1.          price := Float readFrom: (parts last).          a at: index put: price.          pricesSum := pricesSum + price.          (index rem: 100) = 0 ifTrue: [              asum := a sum.       ]]] timeToRun. { index. pricesSum. asum. ttr }.   "ExternalDoubleArray an Array(337588 383662.5627699992 383562.2956199993 0:00:01:59.885)"   "FloatArray  an Array(337588 383662.5627699992 383562.2954441309 0:00:00:06.555)"

FloatArray is not the precision I need. But it is over 18x faster.

I am afraid I must be doing something badly wrong. Python/Numpy is over 4x faster than FloatArray for the above.

If I am using Smallapack incorrectly please help.

Any help greatly appreciated.

Thanks.