I have updated Smallapack to version 1.6.1 so as to accelerate sum.
| a b c |
a := LapackSGEMatrix randNormal: #(10000 1).
b := a as: FloatArray.
c := a asAbstractMatrix.
{a sum. b sum. c sum.}.
{[a sum] bench. [b sum] bench. [c sum] bench.}.
'27,500 per second. 36.3 microseconds per run.' "LapackMatrix"
'117,000 per second. 8.54 microseconds per run.' "FloatArray"
'1,180 per second. 845 microseconds per run.' "Un-accelerated
AbstractMatrix"
I measured with single precision for everything so as to have fair
comparisons.
As you can see, LapackMatrix sum is still slower than FloatArray sum.
This is because we have to create a Matrix of ones first (xLASET) before
calling BLAS dot-product primitive (xDOTU).
In Squeak, profiling can be obtained thru (I guess not much different in
Pharo):
AndreasSystemProfiler spyOn: [[a sum] bench].
which gives:
**Leaves**
37.19 (1,865) LapackSLibrary xlasetWithuplo:m:n:alpha:beta:a:lda:length:
27.12 (1,360) BlasSLibrary dotF2CWithn:x:incx:y:incy:
9.31 (467) Behavior basicNew:
2.42 (121) ByteString class compare:with:collated:
1.43 (72) ByteString hashWithInitialHash:
Note that this is un-accelerated BLAS (no Intel Math Kernel Library or
other accelerated version), but it does not make much difference for those
BLAS level-1 functions (those with cost O(N))
But we can avoid that cost and compute all the cumulative sums, or some of
them at once with a single Matrix operation (xTRMM / xGEMM):
| a b c |
a := LapackSGEMatrix randUniform: #(10000 1).
b := LapackSGEMatrix nrow: 10000 ncol: 10000 withAll: 1. "huge matrix,
that's not cheap, don't even bench it!"
"cumsum"
c := b lowerTriangle * a.
| a b c |
a := LapackSGEMatrix randUniform: #(10000 1).
b := LapackSGEMatrix nrow: 10000 ncol: 10000 withAll: 1. "huge matrix,
that's not cheap, don't even bench it!"
"cumsum 1 out of 100"
c := (b lowerTriangle atRows: (100 to: 10000 by: 100)) * a.
| a b c |
a := LapackSGEMatrix randUniform: #(10000 1).
"cheaper construction of partial cum sum"
[b := LapackSGEMatrix rows: ( (100 to: a nrow by: 100) collect: [:n | (
LapackSGEMatrix nrow: 1 ncol: n withAll: 1.0) , (LapackSGEMatrix nrow: 1
ncol: a nrow - n)]).
c := b * a.] bench.
'3.5 per second. 286 milliseconds per run.'
So as you see, with some moderate effort, we have computed 100 partial
cumulative sums in about 286ms, that's 2.86ms per cumsum on average, not
too bad.
Maybe not as straightforward as numpy, and maybe still not as fast, but not
completely at west.
Le mar. 21 mai 2019 à 12:51, Jimmie Houchin <jlhouc...@gmail.com> a écrit :
> Not a problem. I greatly respective other peoples time and priorities and
> their personal lives.
>
> Just for the record I am using 64bit Pharo on a fast i7, 16gb ram, laptop
> running Xubunut 18.04 64bit.
>
> I do not remember any problems loading. And within the small amount of
> experimenting that I did, it seemed to operate fine.
>
> Again, thanks for your contribution. I know it is a lot of work and a
> pretty large area to cover. Python/Numpy has armies of people working on
> this.
>
> Jimmie
>
>
> On 5/21/19 2:54 AM, Nicolas Cellier wrote:
>
> Hi Jimmie,
> I didn't take time yesterday to analyze your specific example because it
> was quite late, but here are some remarks:
> 1) First, I recommend using 64bits Pharo, because number crunching and
> Float operations will be faster (not FloatArray though).
> 2) it would be nice to use a profiler to analyze where time is spent
> I would not be amazed that (Float readFrom:...) takes a non neglectable
> percentage of it
> 3) ExternalDoubleArray only add overhead if no bulk-operation is performed
> (like reading raw binary data or serving as storage area passed to
> Lapack/blas primitives)
> it does not provide accelerated features by itself indeed.
> I think that it is too low level to serve as a primary interface.
> 4) LapackXXXMatrix sum has effectively not been optimized to use BLAS, and
> this can be easily corrected, thanks for giving this example.
> With some cooperation, we could easily make some progress, there are low
> hanging fruits.
> But I understand if you prefer to stick with more mature numpy solution.
> Thanks for trying. At least, you were able to load and use Smallapack in
> Pharo, and this is already a good feedback.
> If you have time, I'll publish a small enhancement for accelerating sum,
> and ask you to retry.
> Thanks again
>
>
> Le mar. 21 mai 2019 à 05:13, Serge Stinckwich <serge.stinckw...@gmail.com>
> a écrit :
>
>> 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 <jlhouc...@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 à 14:58, Jimmie <jlhouc...@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.
>>>>
>>>>
>>>>
>>
>> --
>> 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 Yaoundé I, Cameroon
>> "Programs must be written for people to read, and only incidentally for
>> machines to execute."
>> https://twitter.com/SergeStinckwich
>>
>>
>
