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[Pharo 8.0] Build #288: 3366-Do-not-run-testNoDeadWeakSubscriptionstestNoDeadSubscriptions

by ci-pharo-ci-jenkins2＠inria.fr

There is a new Pharo build available! The status of the build #288 was: FAILURE. The Pull Request #3367 was integrated: "3366-Do-not-run-testNoDeadWeakSubscriptionstestNoDeadSubscriptions" Pull request url: https://github.com/pharo-project/pharo/pull/3367 Issue Url: https://github.com/pharo-project/pharo/issues/3366 Build Url: https://ci.inria.fr/pharo-ci-jenkins2/job/Test%20pending%20pull%20request%2…

May 24, 2019

[Pharo 8.0] Build #287: 3388-Morphic-should-not-depend-on-Reflectivity

by ci-pharo-ci-jenkins2＠inria.fr

There is a new Pharo build available! The status of the build #287 was: SUCCESS. The Pull Request #3400 was integrated: "3388-Morphic-should-not-depend-on-Reflectivity" Pull request url: https://github.com/pharo-project/pharo/pull/3400 Issue Url: https://github.com/pharo-project/pharo/issues/3388 Build Url: https://ci.inria.fr/pharo-ci-jenkins2/job/Test%20pending%20pull%20request%2…

May 24, 2019

[Pharo 8.0] Build #286: 3385-Sync-Spec

by ci-pharo-ci-jenkins2＠inria.fr

There is a new Pharo build available! The status of the build #286 was: SUCCESS. The Pull Request #3387 was integrated: "3385-Sync-Spec" Pull request url: https://github.com/pharo-project/pharo/pull/3387 Issue Url: https://github.com/pharo-project/pharo/issues/3385 Build Url: https://ci.inria.fr/pharo-ci-jenkins2/job/Test%20pending%20pull%20request%2…

May 23, 2019

[Pharo 8.0] Build #285: 3147-Class-converted-to-trait-is-unusable

by ci-pharo-ci-jenkins2＠inria.fr

There is a new Pharo build available! The status of the build #285 was: SUCCESS. The Pull Request #3251 was integrated: "3147-Class-converted-to-trait-is-unusable" Pull request url: https://github.com/pharo-project/pharo/pull/3251 Issue Url: https://github.com/pharo-project/pharo/issues/3147 Build Url: https://ci.inria.fr/pharo-ci-jenkins2/job/Test%20pending%20pull%20request%2…

May 23, 2019

[Call for Topics] Pharo TechTalks

by Marcus Denker

Hi, Last year we had some âPharo Tech Talksââ¦ we want to start that again. Dates: June 20 July 18 Sept 19 Oct 17 Nov 21 Dec 12 The time would be 17h local time (Berlin/Paris). -> If you want to âdriveâ one of those dates â> send me a mail. -> Dates are flexible, if you want to do a tech talk you can propose another date, too -> For the dates without special talk, I think I will do a âlets fix something smallâ session as the default. (with screen sharing, while the people on Discord help and discuss how to do it) What can it be? -> you could present your project -> you could give a âlectureâ -> you could do some tutorial -> You could moderate a audio chat around a topic If someone wants to do something, send me a mail. I will for now already add these dates to the events list at https://association.pharo.org/events Marcus

May 23, 2019

[Pharo 8.0] Build #284: 3381-MorphhandlesMouseMove-does--not-works-if-using-an-event-handler

by ci-pharo-ci-jenkins2＠inria.fr

There is a new Pharo build available! The status of the build #284 was: SUCCESS. The Pull Request #3382 was integrated: "3381-MorphhandlesMouseMove-does--not-works-if-using-an-event-handler" Pull request url: https://github.com/pharo-project/pharo/pull/3382 Issue Url: https://github.com/pharo-project/pharo/issues/3381 Build Url: https://ci.inria.fr/pharo-ci-jenkins2/job/Test%20pending%20pull%20request%2…

May 22, 2019

Re: [Pharo-dev] FloatArray

by Nicolas Cellier

And my less performant 2.7 GHz Intel Core i5 MBP with Apple accelerated VecLib is way faster than naive netlib BLAS (I guess it's multi-threaded): | a b | a := LapackDGEMatrix randNormal: #(1000 1000). b := LapackDGEMatrix randNormal: #(1000 1000). [a * b] timeToRun. 45 | a b | a := LapackSGEMatrix randNormal: #(1000 1000). b := LapackSGEMatrix randNormal: #(1000 1000). [a * b] timeToRun 19 Le mar. 21 mai 2019 Ã 10:05, Nicolas Cellier < nicolas.cellier.aka.nice(a)gmail.com> a Ã©crit : > 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 Ã 05:13, Serge Stinckwich <serge.stinckwich(a)gmail.com> > a Ã©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(a)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 Ã 14:58, Jimmie <jlhouchin(a)gmail.com> a Ã©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 YaoundÃ© I, Cameroon >> "Programs must be written for people to read, and only incidentally for >> machines to execute." >> https://twitter.com/SergeStinckwich >> >

May 21, 2019

Re: [Pharo-dev] FloatArray

by Nicolas Cellier

Le mar. 21 mai 2019 Ã 18:55, Nicolas Cellier < nicolas.cellier.aka.nice(a)gmail.com> a Ã©crit : > 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. > > Oups, I posted to fast! ms are not Âµs! [c := b * a] bench. gives '1,690 per second. 593 microseconds per run.', that's about 6Âµs per sum, but including construction of multiplier, that's way too much (it's not accelerated!) > > Le mar. 21 mai 2019 Ã 12:51, Jimmie Houchin <jlhouchin(a)gmail.com> a > Ã©crit : > >> 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 Ã 05:13, Serge Stinckwich <serge.stinckwich(a)gmail.com> >> a Ã©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(a)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 Ã 14:58, Jimmie <jlhouchin(a)gmail.com> a Ã©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 YaoundÃ© I, Cameroon >>> "Programs must be written for people to read, and only incidentally for >>> machines to execute." >>> https://twitter.com/SergeStinckwich >>> >>

May 21, 2019

Re: [Pharo-dev] FloatArray

by Nicolas Cellier

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 Ã 12:51, Jimmie Houchin <jlhouchin(a)gmail.com> a Ã©crit : > 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 Ã 05:13, Serge Stinckwich <serge.stinckwich(a)gmail.com> > a Ã©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(a)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 Ã 14:58, Jimmie <jlhouchin(a)gmail.com> a Ã©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 >> âhâ >> >> Int. Research Unit >> on Modelling/Simulation of Complex Systems (UMMISCO) >> âSorbonne University >> (SU) >> French National Research Institute for Sustainable Development (IRD)â >> U >> âniversity of YaoundÃ© Iâ, Cameroon >> "Programs must be written for people to read, and only incidentally for >> machines to execute." >> https://twitter.com/SergeStinckwich >> â >> >

May 21, 2019

[Pharo 8.0] Build #283: 3377-RBRefactoryChangeManager-changeFactory-should-be-nil

by ci-pharo-ci-jenkins2＠inria.fr

There is a new Pharo build available! The status of the build #283 was: SUCCESS. The Pull Request #3378 was integrated: "3377-RBRefactoryChangeManager-changeFactory-should-be-nil" Pull request url: https://github.com/pharo-project/pharo/pull/3378 Issue Url: https://github.com/pharo-project/pharo/issues/3377 Build Url: https://ci.inria.fr/pharo-ci-jenkins2/job/Test%20pending%20pull%20request%2…

May 21, 2019