Hi Serge,

this is good news, having tensor flow bindings is also a must!

I have this in Smallapack with pure CPU unaccelerated blas (no MKL, nor ATLAS, just plain and dumb netlib code)

| a b |
a := LapackDGEMatrix randNormal: #(1000 1000).
b := LapackDGEMatrix randNormal: #(1000 1000).
[a * b] timeToRun
��783

| a b |
a := LapackSGEMatrix randNormal: #(1000 1000).
b := LapackSGEMatrix randNormal: #(1000 1000).
[a * b] timeToRun
��448

Intel(R) Xeon(R) CPU E3-1245 v3 @ 3.40GHz

So I think that we can get much better with accelerated library!

Le��mar. 21 mai 2019 ����05:13, Serge Stinckwich <serge.stinckwich@gmail.com> a ��crit��:

There is another solution with my TensorFlow Pharo binding:

https://github.com/PolyMathOrg/libtensorflow-pharo-bindings

You can do a matrix multiplication like that :

| graph t1 t2 c1 c2 mult session result |
graph := TF_Graph create.
t1 := TF_Tensor fromFloats: (1 to:1000000) asArray shape:#(1000 1000).
t2 := TF_Tensor fromFloats: (1 to:1000000) asArray shape:#(1000 1000).
c1 := graph const: 'c1' value: t1.
c2 := graph const: 'c2' value: t2.
mult := c1 * c2.
session := TF_Session on: graph.
result := session runOutput: (mult output: 0).
result asNumbers

Here I'm doing a multiplication between 2 matrices of 1000X1000 size in 537 ms on my computer.

All operations can be done in a graph of operations that is run outside Pharo, so could be very fast.

Operations can be done on CPU or GPU. 32 bits or 64 bits float operations are possible.

This is a work in progress but can already be used.

Regards,

On Tue, May 21, 2019 at 6:54 AM Jimmie Houchin <jlhouchin@gmail.com> wrote:

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 ����14:58, Jimmie <jlhouchin@gmail.com> a ��crit��:

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.

--

Serge Stinckwic

h

Int. Research Unit

��on Modelling/Simulation of Complex Systems (UMMISCO)

Sorbonne University

��(SU)

French National Research Institute for Sustainable Development (IRD)

U

niversity of Yaound�� I, Cameroon

"Programs must be written for people to read, and only incidentally for machines to execute." https://twitter.com/SergeStinckwich