[julia-users] FE solver in Julia fast and faster
Hello all, Big thanks to Tim Holy and Andreas Noack. The FE solver implemented in Julia as described previously (https://groups.google.com/forum/?fromgroups=#!searchin/julia-users/Krysl/julia-users/dgNqrJBZx5U/oGeXMZFjToMJ) has been further optimized with their helpful hints and pointers. The comparison problem now runs in around 9.6 seconds, compared to 16 seconds solve phase achieved with the commercial FEA software . Not bad, Julia! Petr
Re: [julia-users] Re: Sort performance depends on Array type in a strange way
For arrays of numbers, it's assumed that an unstable sort is ok, so
quicksort is used and no temporary array needs to be allocated. For other
kinds of objects, we default to mergesort, which needs a temporary array.
On Fri, Jan 9, 2015 at 7:23 PM, Páll Haraldsson pall.haralds...@gmail.com
wrote:
I'm not sure why it matters if I added Numbers:
type Pair : Number
x
y
end
I got a little lower time and 0 bytes allocated:
julia @time sort_Pair()
elapsed time: 0.006614108 seconds (488 bytes allocated)
elapsed time: 0.041584405 seconds (31991872 bytes allocated)
elapsed time: 1.732269097 seconds (0 bytes allocated)
Array{Pair,1}
elapsed time: 1.951577918 seconds (35993216 bytes allocated, 8.71% gc time)
--
Palli.
On Saturday, January 10, 2015 at 12:05:03 AM UTC, Páll Haraldsson wrote:
Hi,
This is my first answer here, I'm not an expert on Julia.. Seems tuple is
slow. It's more general,
On my machine this beats Python:
function sort_Pair()
gc(); @time temp = rand(500_000)
gc(); @time temp2 = [Pair(temp[i],i) for i=1:length(temp)]
gc(); @time sort!(temp2); println(typeof(temp2))
end
julia @time sort_pair()
elapsed time: 0.006823427 seconds (488 bytes allocated)
elapsed time: 0.035921609 seconds (31991872 bytes allocated)
elapsed time: 1.975542283 seconds (448 bytes allocated)
Array{Pair,1}
elapsed time: 2.20708157 seconds (40892612 bytes allocated, 7.46% gc time)
compared to (or 2.7 sec total in Python):
julia @time sort_any()
elapsed time: 0.006287604 seconds (488 bytes allocated)
elapsed time: 0.047933539 seconds (35991872 bytes allocated)
elapsed time: 5.989875809 seconds (191154832 bytes allocated)
Array{Any,1}
elapsed time: 6.247331383 seconds (231803748 bytes allocated, 2.76% gc
time)
I used:
type Pair
x
y
end
import Base.isless
function isless(a, b)
if a.x b.x
true
elseif a.x == b.x a.y b.y
true
else
false
end
end
You can maybe think of a tuple as a type, but I'm not sure where to look
for code for it nor looked into the profiler (use @profile, not @time). A
tuple can of course be a 2-tupel (pair), 3-tuple, etc. And I could change
the function for each case, just not sure how I would make my own type that
would handle n-tuples.. Julia does that and maybe just has to be optimized.
Maybe in 0.4 it is..
Types in Julia are supposed to be abstraction-free, but included tuples
seem to have a 231803748/40892612 = 5.6 times overhead compared to my Pair
judging by the memory allocations.
--
Palli.
On Friday, January 9, 2015 at 10:06:30 AM UTC, Andras Niedermayer wrote:
The performance of the sort algorithm varies largely with the element
type of an Array, in an unexpected (at least for me) way. Sorting time is
ordered like this: Vector{(Any,Any)} Vector{Any}
Vector{(Float64,Int64}). Is this a bug or is this behavior intended? Here's
the code:
---
julia function sort_floatint_tuple()
temp = rand(500_000)
temp2 = (Float64,Int64)[(temp[i],i) for i=1:length(temp)]
sort!(temp2)
end
sort_floatint_tuple (generic function with 1 method)
julia gc(); @time sort_floatint_tuple();
elapsed time: 5.371570706 seconds (2187115768 bytes allocated, 44.77%
gc time)
julia gc(); @time sort_floatint_tuple();
elapsed time: 7.522759215 seconds (2184593304 bytes allocated, 57.48%
gc time)
julia function sort_any_tuple()
temp = rand(500_000)
temp2 = (Any,Any)[(temp[i],i) for i=1:length(temp)]
sort!(temp2)
end
sort_any_tuple (generic function with 1 method)
julia gc(); @time sort_any_tuple();
elapsed time: 2.705974449 seconds (526135400 bytes allocated, 23.32% gc
time)
julia gc(); @time sort_any_tuple();
elapsed time: 3.215082563 seconds (523241560 bytes allocated, 37.72% gc
time)
julia function sort_any()
temp = rand(500_000)
temp2 = Any[(temp[i],i) for i=1:length(temp)]
sort!(temp2)
end
sort_any (generic function with 1 method)
julia gc(); @time sort_any();
elapsed time: 3.591829528 seconds (231327824 bytes allocated, 6.01% gc
time)
julia gc(); @time sort_any();
elapsed time: 3.932805966 seconds (231203560 bytes allocated, 12.54% gc
time)
---
Python is much faster here:
---
%timeit temp=np.random.rand(50); temp2=zip(temp,range(len(temp)));
temp2.sort()
1 loops, best of 3: 1.33 s per loop
---
PS: Type inference gives Vector{(Float64,Int64)} if I write the for
comprehension in a function and Vector{(Any,Any)} if I run it outside of a
function.
PPS: After looking at this in detail, I found out that sortperm would
have been a better option for this. But it is still surprising to me that
losing type information ((Any,Any) instead of (Float64,Int64)) speeds up
the code.
[julia-users] Re: Adding Compose circles/lines/polygons to Gadfly plots?
Julia 0.3.5 Gadfly 0.3.10 julia plot(sin, 0, 2pi, Guide.annotation(compose(context(), circle([pi/2, 3*pi/2], [1.0, -1.0], [2mm]), fill(nothing), stroke(orange ERROR: annotation not defined ?? Kaj On Friday, January 9, 2015 at 6:28:03 AM UTC+2, Sheehan Olver wrote: I came across this which answers my question: http://gadflyjl.org/guide_annotation.html On Wednesday, December 10, 2014 at 8:05:02 AM UTC+11, Sheehan Olver wrote: I want to add, say, a triangle or circle to a Gadfly plot. The following works compose(render(plot(x=1:10,y=1:10)),circle(0.5,0.5,0.1)) But I want the circle to use the same coordinate system as the plot. Any advice?
Re: [julia-users] Running mean/median in Julia
I assume you're on 0.3.x – this may simply be due to SubArrays kind of sucking for performance. The new ArrayView stuff is much better. On Fri, Jan 9, 2015 at 7:33 PM, Tomas Mikoviny tomas.mikov...@gmail.com wrote: yes, that was my negligence, change type is first thing I usually check... however it has no significant impact on performance... I've got caught with some different things last days but continuing to play with online methods tm On Wednesday, January 7, 2015 at 7:49:43 PM UTC+1, Stefan Karpinski wrote: Try deleting the line where you initially assign to subset – it isn't necessary and causes that variable to change type over the course of your loop. On Tue, Jan 6, 2015 at 6:50 PM, Tomas Mikoviny tomas.m...@gmail.com wrote: Hi Stefan, you are right. But the moment I try to do this for any subset along the array it gets inefficient. Here is the simplest code for running median of one dimensional array. Time consuming... function runmed(input::Array, w::Int) L = length(input) output = zeros(input) subset = zeros(w+1) for i in 1:L-w subset = sub(input, i:i+w) output[i] = median!(subset) end return output end a = zeros(30); runmed(a, 200); @time runmed(a, 200); elapsed time: 1.460171594 seconds (43174976 bytes allocated) On Tuesday, January 6, 2015 10:10:39 PM UTC+1, Stefan Karpinski wrote: You may not need online methods at all. Sorting the rows of a 200 x 30 matrix doesn't take very long on my laptop: julia X = randn(200,30); julia @time X = sortrows(X); elapsed time: 0.297998739 seconds (480053384 bytes allocated) On Tue, Jan 6, 2015 at 2:33 PM, Tomas Mikoviny tomas.m...@gmail.com wrote: Hi Kevin, generally I'm trying to do baseline correction on mass spectra with ~30 bins. I've tried several algorithms to evaluate baseline but the ones working the best implement running median and mean. I've just got mean sorted out via cumsum trick in coincidence with Tim's suggestion (found some MATLAB discussion on that). Although I'll check Tamas' suggestion too. I've got stacked with running median that would have reasonable performance since computer has to crunch runmed of array of 200 x 30 within couple of seconds (max) to manage online analysis. On Tuesday, January 6, 2015 6:18:02 PM UTC+1, Kevin Squire wrote: Hi Tomas, I'm bit aware of any (though they might exist). It might help if you gave a little more context--what kind of data are you working with? Cheers, Kevin On Tuesday, January 6, 2015, Tomas Mikoviny tomas.m...@gmail.com wrote: Hi, I was just wondering if anyone knows if there is a package that implements *fast* running median/mean algorithms? Thanks a lot...
Re: [julia-users] Why is memory allocated to run loop?
Thanks, that was very helpful. The problem was eliminated by parameterizing wrt the type S:FESet. The lesson I learned was that knowing the abstract type is not enough: the compiler needs to know the concrete type. That is something I was not used to and it tripped me up again this time. I hope I will remember from now on. Thanks again, Petr On Friday, January 9, 2015 at 12:08:58 PM UTC-8, Tim Holy wrote: On Friday, January 09, 2015 11:51:15 AM Petr Krysl wrote: The size() function is from the base. fes.conn is a 2D array of ints. No it's not, it's a JFIntMat :-). But seriously, _you_ might know that corresponds to a 2D array of ints, but the key question is if you've given enough information in your declaration of JFIntMat for the _compiler_ to know that. Bottom line is that the behavior you're observing isn't normal. It's also extremely unlikely to be a bug in julia, because we do this kind of stuff all over julia without the kind of allocations you're observing. So it's almost certainly something in your code. @code_warntype is your best way of finding out. --Tim P On Friday, January 9, 2015 at 11:25:34 AM UTC-8, Tim Holy wrote: I wonder if your `size` function isn't returning an Int, or that the compiler can't infer that. Did you try this on julia 0.4 with @code_warntype? --Tim On Friday, January 09, 2015 08:06:43 AM Petr Krysl wrote: Actually, I realized that the example shows the same problem, but for a different function. This time the allocation occurs in the function CALLING getconn!. P On Friday, January 9, 2015 at 8:03:00 AM UTC-8, Petr Krysl wrote: Tim, Your explanation is very helpful. There is a complication though: The function is actually called from another function (from a chain of functions). Here is the measurement from a simplified situation: - using JFFoundationModule - using FESetModule - - n=100; - function test(n) 0 fes=FESetH8(conn=rand(JFInt,n, 8)) 112 conn1::JFIntMat=zeros(JFInt,nfense(fes),1); 6383704 for j=1:size(fes.conn,1) 0 getconn!(fes,conn1,j); - end 0 return true - end - - test(n) - clear_malloc_data() - n=10; - test(n) As you can see the loop over the rows of the array fes.conn allocates substantial amount of memory. Here is the type: https://gist.github.com/PetrKryslUCSD/794f521a8e5b057e5e4e Petr
Re: [julia-users] Why is memory allocated to run loop?
Thanks Andreas, that was helpful. See message to Tim in this thread. P On Friday, January 9, 2015 at 11:55:20 AM UTC-8, Andreas Noack wrote: Sometimes Julia can have problems inferring the type of fields of types which is what conn is. Using @code_warntype as suggested by Tim could give an indication if it is the case. 2015-01-09 14:51 GMT-05:00 Petr Krysl krysl...@gmail.com javascript:: The size() function is from the base. fes.conn is a 2D array of ints. P On Friday, January 9, 2015 at 11:25:34 AM UTC-8, Tim Holy wrote: I wonder if your `size` function isn't returning an Int, or that the compiler can't infer that. Did you try this on julia 0.4 with @code_warntype? --Tim On Friday, January 09, 2015 08:06:43 AM Petr Krysl wrote: Actually, I realized that the example shows the same problem, but for a different function. This time the allocation occurs in the function CALLING getconn!. P On Friday, January 9, 2015 at 8:03:00 AM UTC-8, Petr Krysl wrote: Tim, Your explanation is very helpful. There is a complication though: The function is actually called from another function (from a chain of functions). Here is the measurement from a simplified situation: - using JFFoundationModule - using FESetModule - - n=100; - function test(n) 0 fes=FESetH8(conn=rand(JFInt,n, 8)) 112 conn1::JFIntMat=zeros(JFInt,nfense(fes),1); 6383704 for j=1:size(fes.conn,1) 0 getconn!(fes,conn1,j); - end 0 return true - end - - test(n) - clear_malloc_data() - n=10; - test(n) As you can see the loop over the rows of the array fes.conn allocates substantial amount of memory. Here is the type: https://gist.github.com/PetrKryslUCSD/794f521a8e5b057e5e4e Petr
Re: [julia-users] Re: Julia 0.3.4 - problem installing packages
No, I had not run Pkg.update on that computer. But as I said in my message, deleting ~/.julia, and then trying to install them again, worked. Thanks so much for creating/maintaining these binaries. On Thursday, January 8, 2015 at 3:25:24 PM UTC-5, Elliot Saba wrote: Have you run `Pkg.update()` recently? On Thu, Jan 8, 2015 at 10:34 AM, Test This curiou...@gmail.com javascript: wrote: This might have been because I did not delete ~/.julia. I have not tried yet by deleting it on my Mac. However, I just installed 0.3.4 on Ubuntu and installed the above packages without any problem. On Ubuntu, I did delete ~/.julia before installing the packages, so I am thinking that will work on Mac too. On Thursday, January 8, 2015 11:35:23 AM UTC-5, Test This wrote: Hello, I downloaded Julia 0.3.4 (dmg version) recently for Mac. I am using OS X 10.9.5. Today I tried installing some packages. Package Distributions and ArgParse installed fine (have not used them yet, but they did not throw any error). However, DataFrames and PyCall did not install. Here is the error output form repl: Pkg.add(PyCall) ERROR: unknown package PyCall in wait at task.jl:51 in sync_end at /Applications/Julia-0.3.4.app/ Contents/Resources/julia/lib/julia/sys.dylib in add at pkg/entry.jl:319 in add at pkg/entry.jl:71 in anonymous at pkg/dir.jl:28 in cd at /Applications/Julia-0.3.4.app/Contents/Resources/julia/lib/ julia/sys.dylib in __cd#227__ at /Applications/Julia-0.3.4.app/ Contents/Resources/julia/lib/julia/sys.dylib in add at pkg.jl:20 Any solutions? Thank you.
Re: [julia-users] Re: Julia v0.3.5
Now matches the tagged commit 'n all. Thanks Lex On Saturday, January 10, 2015 at 9:19:35 AM UTC+10, Elliot Saba wrote: We noticed that as well https://github.com/JuliaLang/julia/commit/a05f87b79ad62beb033817fdfdefa270c9557aaf. An `apt-get update apt-get upgrade` should fix it. -E On Fri, Jan 9, 2015 at 3:15 PM, cdm cdmcle...@gmail.com javascript: wrote: yeah ... that parenthetical has been static since at least version 0.3.3 curious ... On Friday, January 9, 2015 at 3:06:29 PM UTC-8, ele...@gmail.com wrote: Thank you all the people who bring these updates. One question, the versioninfo() for theJulia release on the Ubuntu PPA says: Version 0.3.5 Commit 21d5433* (2014-10-21 20:18 UTC) The commit and date/time hasn't changed. Cheers Lex On Saturday, January 10, 2015 at 5:11:13 AM UTC+10, Elliot Saba wrote: Hello all! The latest bugfix release of the 0.3.x Julia line has been released. Binaries are available from the usual place http://julialang.org/downloads/, and as is typical with such things, please report all issues to either the issue tracker https://github.com/JuliaLang/julia/issues, or email this list. As this is a bugfix release, there are not too many new big-item features to announce, but if you are interested in the bugs fixed since 0.3.4, this commit log https://github.com/JuliaLang/julia/compare/v0.3.4...v0.3.5 should give you an idea of the effort put in by our team of backporters. This is a recommended upgrade for anyone using any of the previous 0.3.x releases, and should act as a drop-in replacement for any of the 0.3.x line. We would like to get feedback if someone has a correctly working program that doesn't work after this upgrade. Happy Hacking, -E
[julia-users] Re: Sort performance depends on Array type in a strange way
Hi,
This is my first answer here, I'm not an expert on Julia.. Seems tuple is
slow. It's more general,
On my machine this beats Python:
function sort_Pair()
gc(); @time temp = rand(500_000)
gc(); @time temp2 = [Pair(temp[i],i) for i=1:length(temp)]
gc(); @time sort!(temp2); println(typeof(temp2))
end
julia @time sort_pair()
elapsed time: 0.006823427 seconds (488 bytes allocated)
elapsed time: 0.035921609 seconds (31991872 bytes allocated)
elapsed time: 1.975542283 seconds (448 bytes allocated)
Array{Pair,1}
elapsed time: 2.20708157 seconds (40892612 bytes allocated, 7.46% gc time)
compared to (or 2.7 sec total in Python):
julia @time sort_any()
elapsed time: 0.006287604 seconds (488 bytes allocated)
elapsed time: 0.047933539 seconds (35991872 bytes allocated)
elapsed time: 5.989875809 seconds (191154832 bytes allocated)
Array{Any,1}
elapsed time: 6.247331383 seconds (231803748 bytes allocated, 2.76% gc time)
I used:
type Pair
x
y
end
import Base.isless
function isless(a, b)
if a.x b.x
true
elseif a.x == b.x a.y b.y
true
else
false
end
end
You can maybe think of a tuple as a type, but I'm not sure where to look
for code for it nor looked into the profiler (use @profile, not @time). A
tuple can of course be a 2-tupel (pair), 3-tuple, etc. And I could change
the function for each case, just not sure how I would make my own type that
would handle n-tuples.. Julia does that and maybe just has to be optimized.
Maybe in 0.4 it is..
Types in Julia are supposed to be abstraction-free, but included tuples
seem to have a 231803748/40892612 = 5.6 times overhead compared to my Pair
judging by the memory allocations.
--
Palli.
On Friday, January 9, 2015 at 10:06:30 AM UTC, Andras Niedermayer wrote:
The performance of the sort algorithm varies largely with the element type
of an Array, in an unexpected (at least for me) way. Sorting time is
ordered like this: Vector{(Any,Any)} Vector{Any}
Vector{(Float64,Int64}). Is this a bug or is this behavior intended? Here's
the code:
---
julia function sort_floatint_tuple()
temp = rand(500_000)
temp2 = (Float64,Int64)[(temp[i],i) for i=1:length(temp)]
sort!(temp2)
end
sort_floatint_tuple (generic function with 1 method)
julia gc(); @time sort_floatint_tuple();
elapsed time: 5.371570706 seconds (2187115768 bytes allocated, 44.77% gc
time)
julia gc(); @time sort_floatint_tuple();
elapsed time: 7.522759215 seconds (2184593304 bytes allocated, 57.48% gc
time)
julia function sort_any_tuple()
temp = rand(500_000)
temp2 = (Any,Any)[(temp[i],i) for i=1:length(temp)]
sort!(temp2)
end
sort_any_tuple (generic function with 1 method)
julia gc(); @time sort_any_tuple();
elapsed time: 2.705974449 seconds (526135400 bytes allocated, 23.32% gc
time)
julia gc(); @time sort_any_tuple();
elapsed time: 3.215082563 seconds (523241560 bytes allocated, 37.72% gc
time)
julia function sort_any()
temp = rand(500_000)
temp2 = Any[(temp[i],i) for i=1:length(temp)]
sort!(temp2)
end
sort_any (generic function with 1 method)
julia gc(); @time sort_any();
elapsed time: 3.591829528 seconds (231327824 bytes allocated, 6.01% gc
time)
julia gc(); @time sort_any();
elapsed time: 3.932805966 seconds (231203560 bytes allocated, 12.54% gc
time)
---
Python is much faster here:
---
%timeit temp=np.random.rand(50); temp2=zip(temp,range(len(temp)));
temp2.sort()
1 loops, best of 3: 1.33 s per loop
---
PS: Type inference gives Vector{(Float64,Int64)} if I write the for
comprehension in a function and Vector{(Any,Any)} if I run it outside of a
function.
PPS: After looking at this in detail, I found out that sortperm would
have been a better option for this. But it is still surprising to me that
losing type information ((Any,Any) instead of (Float64,Int64)) speeds up
the code.
Re: [julia-users] How to define help text for a module function so that it displays in REPL
Hi Jan, If you run into any problems or have any suggestions for improvements feel free to open an issue in the Docile repo. — Mike On Friday, 9 January 2015 16:49:53 UTC+2, Ján Dolinský wrote: Thanks a lot. I'll check it out. Jan Dňa piatok, 9. januára 2015 10:53:10 UTC+1 Tim Holy napísal(-a): If you're running julia 0.3, see the Docile package. If you're running julia 0.4, there's some in-progress documentation here: https://github.com/JuliaLang/julia/pull/9447 --Tim On Friday, January 09, 2015 12:41:33 AM Ján Dolinský wrote: Hi, I would like to know how to write a help (comment) text e.g. for a module function so that once module is loaded it is displayed if in REPL help mode. E.g. for sum function help?sum displays brief help text for sum Thanks, Jan
Re: [julia-users] Why is memory allocated to run loop?
Actually, I realized that the example shows the same problem, but for a different function. This time the allocation occurs in the function CALLING getconn!. P On Friday, January 9, 2015 at 8:03:00 AM UTC-8, Petr Krysl wrote: Tim, Your explanation is very helpful. There is a complication though: The function is actually called from another function (from a chain of functions). Here is the measurement from a simplified situation: - using JFFoundationModule - using FESetModule - - n=100; - function test(n) 0 fes=FESetH8(conn=rand(JFInt,n, 8)) 112 conn1::JFIntMat=zeros(JFInt,nfense(fes),1); 6383704 for j=1:size(fes.conn,1) 0 getconn!(fes,conn1,j); - end 0 return true - end - - test(n) - clear_malloc_data() - n=10; - test(n) As you can see the loop over the rows of the array fes.conn allocates substantial amount of memory. Here is the type: https://gist.github.com/PetrKryslUCSD/794f521a8e5b057e5e4e Petr
Re: [julia-users] Why is memory allocated to run loop?
Tim, Your explanation is very helpful. There is a complication though: The function is actually called from another function (from a chain of functions). Here is the measurement from a simplified situation: - using JFFoundationModule - using FESetModule - - n=100; - function test(n) 0 fes=FESetH8(conn=rand(JFInt,n, 8)) 112 conn1::JFIntMat=zeros(JFInt,nfense(fes),1); 6383704 for j=1:size(fes.conn,1) 0 getconn!(fes,conn1,j); - end 0 return true - end - - test(n) - clear_malloc_data() - n=10; - test(n) As you can see the loop over the rows of the array fes.conn allocates substantial amount of memory. Here is the type: https://gist.github.com/PetrKryslUCSD/794f521a8e5b057e5e4e Petr
Re: [julia-users] Re: Where is documentation on { } notation for creating an Array(Any,1) ??
Yes, but he is looking at the 0.4 documentation, so naturally he won't find it there. Try the 0.3 documentation.
Re: [julia-users] Installing packages system-wide
In addition, I assume if JULIA_PKGDIR is identical for all users that
everyone installs packages in the same directory (if they can write to it)
which is not desired in my situation. There should be some system-wide
packages available and then a user should be still able to install packages
individually.
Jan
Dňa štvrtok, 8. januára 2015 16:13:40 UTC+1 Tim Holy napísal(-a):
There's a second way to install system-wide (at least, on Unix): set the
environment variable JULIA_PKGDIR for all users. If you do it that way,
tab
completion works.
Still, it would be nice if tab completion also worked with LOAD_PATH. Care
to
open an issue about that?
--Tim
On Thursday, January 08, 2015 07:08:32 AM Ján Dolinský wrote:
I noticed that if packages are installed system-wide than
auto-completion
for a regular user does not work e.g. typing
julia using Arr
will not auto-complete Arr (after pressing the Tab key) to
ArrayViews.
This is not the case if packages are installed in user's home directory.
Is
there any fix for this ?
Thanks,
Jan
Dňa streda, 10. decembra 2014 16:28:15 UTC+1 Stefan Karpinski
napísal(-a):
There's a Julia variable called LOAD_PATH that is arranged to point at
two
system directories under your julia installation. E.g.:
julia LOAD_PATH
2-element Array{Union(ASCIIString,UTF8String),1}:
/opt/julia-0.3.3/usr/local/share/julia/site/v0.3
/opt/julia-0.3.3/usr/share/julia/site/v0.3
If you install packages under either of those directories, then
everyone
using that Julia will see them. One way to do this is to run julia as
a
user who can write to those directories after doing `export
JULIA_PKGDIR=/opt/julia-0.3.3/usr/share/julia/site` in the shell. That
way
Julia will use that as it's package directory and normal package
commands
will allow you to install packages for everyone. Or you can just copy
your
installed packages and change the ownership and permissions so that
everyone can access the files.
On Wed, Dec 10, 2014 at 8:16 AM, Ján Dolinský jan.do...@2bridgz.com
javascript: wrote:
Hello,
I'd like to ask how to install Julia packages system-wide so that
users
do not have to install packages individually on their own but rather
just
once by an admin.
Thanks,
Jan
Re: [julia-users] Why is memory allocated to run loop?
BTW, all that is (briefly) described here:
http://docs.julialang.org/en/latest/manual/profile/#memory-allocation-analysis
Petr, if you nevertheless found my more detailed explanation useful, please
weave it into the documentation and submit a pull request (click-and-edit
instructions here:
https://github.com/JuliaLang/julia/blob/master/CONTRIBUTING.md#improving-documentation)
--Tim
On Thursday, January 08, 2015 10:38:03 PM Andreas Noack wrote:
Im not sure, but I don't think this allocation is of the same kind as the
global variable issue from last mail. Sometimes allocation in connection
with the arguments or the function it self is written in front of the first
code line in the function whatever that is. You could try to insert a dummy
line, e.g. tmp = 1 before the loop and see if the allocation moves to that
line.
2015-01-08 22:34 GMT-05:00 Petr Krysl krysl.p...@gmail.com:
Sorry: I forgot that this function got called thousands of times. I
guess in that case calling the size() function might result in some
allocation which would then get magnified by the number of calls. (Am I
on
the right track?)
On Thursday, January 8, 2015 at 7:19:52 PM UTC-8, Petr Krysl wrote:
Andreas,,
Good point about the global scope. What I was really trying to
understand was this observation:
- function getconn!{T:FESet}(self::T,conn::JFIntMat,j::JFInt)
5959808 for i=1:size(self.conn,2)
0 conn[i]=self.conn[j,i];
- end
0 return self
- end
Isn't the size here in the local context, and also of a definite
type?
Petr
On Thursday, January 8, 2015 at 7:09:35 PM UTC-8, Andreas Noack wrote:
The reason is the usual global variable problem. Because n is global the
type is not inferred well and the loop is therefore not well optimized.
This doesn't happen in local scope e.g.
julia let
m = 100
@allocated for i = 1:m end
end
0
and it can also be avoided if you specify the type in the loop range
julia @allocated for i = 1:n::Int
end
0
2015-01-08 22:02 GMT-05:00 Petr Krysl krysl...@gmail.com:
julia a=rand(100);
julia n=length(a);
julia @allocated for j=1:n
end
63983688
64 MB to run this loop? Is that expected?
Thanks for any insight.
Petr
[julia-users] Re: MPI.jl issue
How about trying out some other MPI library? Mac support may vary across
multiple open source implementations.
-viral
On Thursday, January 8, 2015 at 9:28:21 PM UTC+5:30, Amit Murthy wrote:
Hello,
On a Ubuntu 14.10 system, I am having trouble getting MPI.jl up and
running.
The deps build goes through fine, but fails on running tests with the
following error:
amitm@amitm-macbookpro:~/.julia/v0.4/MPI/test$ mpirun -np 4 julia
test_bcast.jl
WARNING: deprecated syntax {a=b, ...} at /home/amitm/.julia/v0.4/MPI/
src/mpi-base.jl:19.
Use Dict{Any,Any}(a=b, ...) instead.
.
Inconsistency detected by ld.so: dl-minimal.c: 136: realloc: Assertion `ptr
== alloc_last_block' failed!
systeminfo:
| | |_| | | | (_| | | Version 0.4.0-dev+2558 (2015-01-08 07:21 UTC)
_/ |\__'_|_|_|\__'_| | Commit 2bb647a (0 days old master)
|__/ | x86_64-linux-gnu
It is actually a MacbookPro running Ubuntu.
FWIW, it works fine on a different Ubuntu 14.04 Core i5 system.
Amit
Re: [julia-users] MPI.jl issue
Oops - realized that you are running in fact in linux. Sorry - ignore my
comment then. Could it be a julia issue, or some MPI call that is not being
used correctly and is corrupting memory?
-viral
On 09-Jan-2015, at 3:18 pm, Viral Shah vi...@mayin.org wrote:
How about trying out some other MPI library? Mac support may vary across
multiple open source implementations.
-viral
On Thursday, January 8, 2015 at 9:28:21 PM UTC+5:30, Amit Murthy wrote:
Hello,
On a Ubuntu 14.10 system, I am having trouble getting MPI.jl up and running.
The deps build goes through fine, but fails on running tests with the
following error:
amitm@amitm-macbookpro:~/.julia/v0.4/MPI/test$ mpirun -np 4 julia
test_bcast.jl
WARNING: deprecated syntax {a=b, ...} at
/home/amitm/.julia/v0.4/MPI/src/mpi-base.jl:19.
Use Dict{Any,Any}(a=b, ...) instead.
.
Inconsistency detected by ld.so: dl-minimal.c: 136: realloc: Assertion `ptr
== alloc_last_block' failed!
systeminfo:
| | |_| | | | (_| | | Version 0.4.0-dev+2558 (2015-01-08 07:21 UTC)
_/ |\__'_|_|_|\__'_| | Commit 2bb647a (0 days old master)
|__/ | x86_64-linux-gnu
It is actually a MacbookPro running Ubuntu.
FWIW, it works fine on a different Ubuntu 14.04 Core i5 system.
Amit
Re: [julia-users] How to define help text for a module function so that it displays in REPL
arg, the hazards of replying to a big batch of emails without checking what's come in since the initial check... --Tim On Friday, January 09, 2015 01:26:55 AM ele...@gmail.com wrote: On Friday, January 9, 2015 at 7:08:03 PM UTC+10, Ivar Nesje wrote: Compat.jl doesn't have doc, but there is a package that is reported to do this for 0.3. I think it was Doctile.jl, but I might remember wrong. See https://github.com/JuliaLang/julia/pull/9447 for first pass at documentation. Cheers Lex
[julia-users] Re: Adding Compose circles/lines/polygons to Gadfly plots?
That's a nice feature to know about, but it seems to suffer from the problem that it doesn't scale with the rest of the graph if one uses the zoom functionality (i.e. the circles don't stay in position relative to the graph if you change the display of the graph).
Re: [julia-users] Installing packages system-wide
Hi,
Thanks for the tip. How do I open an issue about this problem ?
Jan
Dňa štvrtok, 8. januára 2015 16:13:40 UTC+1 Tim Holy napísal(-a):
There's a second way to install system-wide (at least, on Unix): set the
environment variable JULIA_PKGDIR for all users. If you do it that way,
tab
completion works.
Still, it would be nice if tab completion also worked with LOAD_PATH. Care
to
open an issue about that?
--Tim
On Thursday, January 08, 2015 07:08:32 AM Ján Dolinský wrote:
I noticed that if packages are installed system-wide than
auto-completion
for a regular user does not work e.g. typing
julia using Arr
will not auto-complete Arr (after pressing the Tab key) to
ArrayViews.
This is not the case if packages are installed in user's home directory.
Is
there any fix for this ?
Thanks,
Jan
Dňa streda, 10. decembra 2014 16:28:15 UTC+1 Stefan Karpinski
napísal(-a):
There's a Julia variable called LOAD_PATH that is arranged to point at
two
system directories under your julia installation. E.g.:
julia LOAD_PATH
2-element Array{Union(ASCIIString,UTF8String),1}:
/opt/julia-0.3.3/usr/local/share/julia/site/v0.3
/opt/julia-0.3.3/usr/share/julia/site/v0.3
If you install packages under either of those directories, then
everyone
using that Julia will see them. One way to do this is to run julia as
a
user who can write to those directories after doing `export
JULIA_PKGDIR=/opt/julia-0.3.3/usr/share/julia/site` in the shell. That
way
Julia will use that as it's package directory and normal package
commands
will allow you to install packages for everyone. Or you can just copy
your
installed packages and change the ownership and permissions so that
everyone can access the files.
On Wed, Dec 10, 2014 at 8:16 AM, Ján Dolinský jan.do...@2bridgz.com
javascript: wrote:
Hello,
I'd like to ask how to install Julia packages system-wide so that
users
do not have to install packages individually on their own but rather
just
once by an admin.
Thanks,
Jan
Re: [julia-users] How to define help text for a module function so that it displays in REPL
A help/doc system is in place in 0.4 (unstable) which allows to do that (although no documentation about it yet...). It has not been backported to 0.3 (yet?). But I guess you could use Compat.jl and start documenting and have it all in place once 0.4 is released. On Fri, 2015-01-09 at 09:41, Ján Dolinský jan.dolin...@2bridgz.com wrote: Hi, I would like to know how to write a help (comment) text e.g. for a module function so that once module is loaded it is displayed if in REPL help mode. E.g. for sum function help?sum displays brief help text for sum Thanks, Jan
Re: [julia-users] How to define help text for a module function so that it displays in REPL
Compat.jl doesn't have doc, but there is a package that is reported to do this for 0.3. I think it was Doctile.jl, but I might remember wrong.
Re: [julia-users] Re: Where is documentation on { } notation for creating an Array(Any,1) ??
See https://github.com/JuliaLang/julia/commit/be0f0dafa798c26279f4ece2f70afa9fc078772c
Re: [julia-users] Why is memory allocated to run loop?
It's just an artifact of calling the function from the REPL. If that function
is called from another function, you won't see that.
It might help if you understand how `--track-allocation` works: each time you
get to a new line of code, you ask julia's memory allocator how much memory
has been allocated since that last time you checked. With `--track-
allocation=user`, you only check when you are inside user code. So when you
go back and forth between the REPL and your functions, your functions are
getting blamed for anything that happens in the code that runs the REPL. If
you place the call to your function inside another function (one that perhaps
also does the looping to call your function many times), then you shift the
REPL-blame higher up the call chain, and you'll see that `getconn!` is
clean.
When you see confusing things like this, another approach is to insert some
trivial statement, e.g., `dummy = 0`, as the first line of a function.
--Tim
On Thursday, January 08, 2015 10:38:03 PM Andreas Noack wrote:
Im not sure, but I don't think this allocation is of the same kind as the
global variable issue from last mail. Sometimes allocation in connection
with the arguments or the function it self is written in front of the first
code line in the function whatever that is. You could try to insert a dummy
line, e.g. tmp = 1 before the loop and see if the allocation moves to that
line.
2015-01-08 22:34 GMT-05:00 Petr Krysl krysl.p...@gmail.com:
Sorry: I forgot that this function got called thousands of times. I
guess in that case calling the size() function might result in some
allocation which would then get magnified by the number of calls. (Am I
on
the right track?)
On Thursday, January 8, 2015 at 7:19:52 PM UTC-8, Petr Krysl wrote:
Andreas,,
Good point about the global scope. What I was really trying to
understand was this observation:
- function getconn!{T:FESet}(self::T,conn::JFIntMat,j::JFInt)
5959808 for i=1:size(self.conn,2)
0 conn[i]=self.conn[j,i];
- end
0 return self
- end
Isn't the size here in the local context, and also of a definite
type?
Petr
On Thursday, January 8, 2015 at 7:09:35 PM UTC-8, Andreas Noack wrote:
The reason is the usual global variable problem. Because n is global the
type is not inferred well and the loop is therefore not well optimized.
This doesn't happen in local scope e.g.
julia let
m = 100
@allocated for i = 1:m end
end
0
and it can also be avoided if you specify the type in the loop range
julia @allocated for i = 1:n::Int
end
0
2015-01-08 22:02 GMT-05:00 Petr Krysl krysl...@gmail.com:
julia a=rand(100);
julia n=length(a);
julia @allocated for j=1:n
end
63983688
64 MB to run this loop? Is that expected?
Thanks for any insight.
Petr
Re: [julia-users] Installing packages system-wide
On Friday, January 09, 2015 12:34:12 AM Ján Dolinský wrote:
Hi,
Thanks for the tip. How do I open an issue about this problem ?
https://github.com/JuliaLang/julia, then click on the Issues link near the
top-right. It's a kindness to busy developers to first search to see if your
issue has been reported previously (I haven't done so for this problem, so
please do).
--Tim
Jan
Dňa štvrtok, 8. januára 2015 16:13:40 UTC+1 Tim Holy napísal(-a):
There's a second way to install system-wide (at least, on Unix): set the
environment variable JULIA_PKGDIR for all users. If you do it that way,
tab
completion works.
Still, it would be nice if tab completion also worked with LOAD_PATH. Care
to
open an issue about that?
--Tim
On Thursday, January 08, 2015 07:08:32 AM Ján Dolinský wrote:
I noticed that if packages are installed system-wide than
auto-completion
for a regular user does not work e.g. typing
julia using Arr
will not auto-complete Arr (after pressing the Tab key) to
ArrayViews.
This is not the case if packages are installed in user's home directory.
Is
there any fix for this ?
Thanks,
Jan
Dňa streda, 10. decembra 2014 16:28:15 UTC+1 Stefan Karpinski
napísal(-a):
There's a Julia variable called LOAD_PATH that is arranged to point at
two
system directories under your julia installation. E.g.:
julia LOAD_PATH
2-element Array{Union(ASCIIString,UTF8String),1}:
/opt/julia-0.3.3/usr/local/share/julia/site/v0.3
/opt/julia-0.3.3/usr/share/julia/site/v0.3
If you install packages under either of those directories, then
everyone
using that Julia will see them. One way to do this is to run julia as
a
user who can write to those directories after doing `export
JULIA_PKGDIR=/opt/julia-0.3.3/usr/share/julia/site` in the shell. That
way
Julia will use that as it's package directory and normal package
commands
will allow you to install packages for everyone. Or you can just copy
your
installed packages and change the ownership and permissions so that
everyone can access the files.
On Wed, Dec 10, 2014 at 8:16 AM, Ján Dolinský jan.do...@2bridgz.com
javascript: wrote:
Hello,
I'd like to ask how to install Julia packages system-wide so that
users
do not have to install packages individually on their own but rather
just
once by an admin.
Thanks,
Jan
[julia-users] Sort performance depends on Array type in a strange way
The performance of the sort algorithm varies largely with the element type
of an Array, in an unexpected (at least for me) way. Sorting time is
ordered like this: Vector{(Any,Any)} Vector{Any}
Vector{(Float64,Int64}). Is this a bug or is this behavior intended? Here's
the code:
---
julia function sort_floatint_tuple()
temp = rand(500_000)
temp2 = (Float64,Int64)[(temp[i],i) for i=1:length(temp)]
sort!(temp2)
end
sort_floatint_tuple (generic function with 1 method)
julia gc(); @time sort_floatint_tuple();
elapsed time: 5.371570706 seconds (2187115768 bytes allocated, 44.77% gc
time)
julia gc(); @time sort_floatint_tuple();
elapsed time: 7.522759215 seconds (2184593304 bytes allocated, 57.48% gc
time)
julia function sort_any_tuple()
temp = rand(500_000)
temp2 = (Any,Any)[(temp[i],i) for i=1:length(temp)]
sort!(temp2)
end
sort_any_tuple (generic function with 1 method)
julia gc(); @time sort_any_tuple();
elapsed time: 2.705974449 seconds (526135400 bytes allocated, 23.32% gc
time)
julia gc(); @time sort_any_tuple();
elapsed time: 3.215082563 seconds (523241560 bytes allocated, 37.72% gc
time)
julia function sort_any()
temp = rand(500_000)
temp2 = Any[(temp[i],i) for i=1:length(temp)]
sort!(temp2)
end
sort_any (generic function with 1 method)
julia gc(); @time sort_any();
elapsed time: 3.591829528 seconds (231327824 bytes allocated, 6.01% gc time)
julia gc(); @time sort_any();
elapsed time: 3.932805966 seconds (231203560 bytes allocated, 12.54% gc
time)
---
Python is much faster here:
---
%timeit temp=np.random.rand(50); temp2=zip(temp,range(len(temp)));
temp2.sort()
1 loops, best of 3: 1.33 s per loop
---
PS: Type inference gives Vector{(Float64,Int64)} if I write the for
comprehension in a function and Vector{(Any,Any)} if I run it outside of a
function.
PPS: After looking at this in detail, I found out that sortperm would
have been a better option for this. But it is still surprising to me that
losing type information ((Any,Any) instead of (Float64,Int64)) speeds up
the code.
[julia-users] How to define help text for a module function so that it displays in REPL
Hi, I would like to know how to write a help (comment) text e.g. for a module function so that once module is loaded it is displayed if in REPL help mode. E.g. for sum function help?sum displays brief help text for sum Thanks, Jan
Re: [julia-users] Re: Where is documentation on { } notation for creating an Array(Any,1) ??
On Friday, January 9, 2015 at 6:02:21 PM UTC+10, Ivar Nesje wrote: Yes, but he is looking at the 0.4 documentation, so naturally he won't find it there. Try the 0.3 documentation. Its in http://docs.julialang.org/en/latest/stdlib/punctuation/#punctuation in both 0.3 and 0.4 docs, so 0.4 indeed needs to remove it. Cheers Lex
Re: [julia-users] How to define help text for a module function so that it displays in REPL
If you're running julia 0.3, see the Docile package. If you're running julia 0.4, there's some in-progress documentation here: https://github.com/JuliaLang/julia/pull/9447 --Tim On Friday, January 09, 2015 12:41:33 AM Ján Dolinský wrote: Hi, I would like to know how to write a help (comment) text e.g. for a module function so that once module is loaded it is displayed if in REPL help mode. E.g. for sum function help?sum displays brief help text for sum Thanks, Jan
Re: [julia-users] numpy vs julia benchmarking for random matrix-vector multiplication
There's already a certain amount of automatic bounds-checking removal, but it does that only when it can prove that it's safe. It's not hard to cook up situations where the compiler can't do that. See also the `--check-bounds` command line option for starting julia. --Tim On Thursday, January 08, 2015 07:19:36 PM Joshua Adelman wrote: Hi Steven, I added your version (vander3) to my benchmark and updated the IJulia notebook: http://nbviewer.ipython.org/gist/synapticarbors/26910166ab775c04c47b As you mentioned it's a lot faster than the other version I wrote and evens out the underperformance vs numpy for the larger arrays on my machine. The @inbounds macro makes a small difference, but not dramatic. One of the things that I wonder is if there would be interest in having a way of globally turning off bounds checking either at the function level or module/file level, similar to cython's decorators and file-level compiler directives. Josh On Thursday, January 8, 2015 at 4:36:52 PM UTC-5, Steven G. Johnson wrote: For comparison, the NumPy vander function https://github.com/numpy/numpy/blob/f4be1039d6fe3e4fdc157a22e8c071ac106519 97/numpy/lib/twodim_base.py#L490-L577 does all its work in multiply.accumulate. Here is the outer loop of multiply.accumulate (written in C): https://github.com/numpy/numpy/blob/3b22d87050ab63db0dcd2d763644d924a69c52 54/numpy/core/src/umath/ufunc_object.c#L2936-L3264 and the inner loops (I think) are generated from this source file for various numeric types: https://github.com/numpy/numpy/blob/3b22d87050ab63db0dcd2d763644d924a69c52 54/numpy/core/src/umath/loops.c.src A quick glance at these will tell you the price in code complexity that NumPy is paying for the performance they manage to get.
[julia-users] startswith not defined
When I try to use the startswith http://julia.readthedocs.org/en/latest/stdlib/strings/ function Julia cannot locate it. endswith seems to work fine. Am I doing something wrong? Thanks! _ _ _(_)_ | A fresh approach to technical computing (_) | (_) (_)| Documentation: http://docs.julialang.org _ _ _| |_ __ _ | Type help() for help. | | | | | | |/ _` | | | | |_| | | | (_| | | Version 0.3.4 (2014-10-21 20:18 UTC) _/ |\__'_|_|_|\__'_| | Official http://julialang.org release |__/ | x86_64-linux-gnu julia startswith(asdfas, asd) ERROR: startswith not defined julia startswith ERROR: startswith not defined julia endswith(blah, ah) true
[julia-users] Re: Vector Fields in Julia
PyPlot can do this, see the documentation for Matplotlib. On Friday, January 9, 2015 at 1:38:24 PM UTC-5, Pileas wrote: Hi all, I was wondering how one can make vector fields in Julia similar to those in Matlab. To put it simple, is there any intrinsic routine that does the job or should we program it from scratch? Matlab example here: http://www.mathworks.com/help/symbolic/mupad_ref/plot-vectorfield2d.html Thank you
[julia-users] Re: Adding Compose circles/lines/polygons to Gadfly plots?
Fixed in master now. Thanks for pointing that out. On Friday, January 9, 2015 at 2:22:46 AM UTC-8, Nils Gudat wrote: That's a nice feature to know about, but it seems to suffer from the problem that it doesn't scale with the rest of the graph if one uses the zoom functionality (i.e. the circles don't stay in position relative to the graph if you change the display of the graph).
Re: [julia-users] Installing packages system-wide
I have made a pull-request on it. See https://github.com/JuliaLang/julia/pull/9699
[julia-users] Julia v0.3.5
Hello all! The latest bugfix release of the 0.3.x Julia line has been released. Binaries are available from the usual place http://julialang.org/downloads/, and as is typical with such things, please report all issues to either the issue tracker https://github.com/JuliaLang/julia/issues, or email this list. As this is a bugfix release, there are not too many new big-item features to announce, but if you are interested in the bugs fixed since 0.3.4, this commit log https://github.com/JuliaLang/julia/compare/v0.3.4...v0.3.5 should give you an idea of the effort put in by our team of backporters. This is a recommended upgrade for anyone using any of the previous 0.3.x releases, and should act as a drop-in replacement for any of the 0.3.x line. We would like to get feedback if someone has a correctly working program that doesn't work after this upgrade. Happy Hacking, -E
Re: [julia-users] Why is memory allocated to run loop?
I wonder if your `size` function isn't returning an Int, or that the compiler can't infer that. Did you try this on julia 0.4 with @code_warntype? --Tim On Friday, January 09, 2015 08:06:43 AM Petr Krysl wrote: Actually, I realized that the example shows the same problem, but for a different function. This time the allocation occurs in the function CALLING getconn!. P On Friday, January 9, 2015 at 8:03:00 AM UTC-8, Petr Krysl wrote: Tim, Your explanation is very helpful. There is a complication though: The function is actually called from another function (from a chain of functions). Here is the measurement from a simplified situation: - using JFFoundationModule - using FESetModule - - n=100; - function test(n) 0 fes=FESetH8(conn=rand(JFInt,n, 8)) 112 conn1::JFIntMat=zeros(JFInt,nfense(fes),1); 6383704 for j=1:size(fes.conn,1) 0 getconn!(fes,conn1,j); - end 0 return true - end - - test(n) - clear_malloc_data() - n=10; - test(n) As you can see the loop over the rows of the array fes.conn allocates substantial amount of memory. Here is the type: https://gist.github.com/PetrKryslUCSD/794f521a8e5b057e5e4e Petr
[julia-users] Vector Fields in Julia
Hi all, I was wondering how one can make vector fields in Julia similar to those in Matlab. To put it simple, is there any intrinsic routine that does the job or should we program it from scratch? Matlab example here: http://www.mathworks.com/help/symbolic/mupad_ref/plot-vectorfield2d.html Thank you
[julia-users] Re: startswith not defined
You probably want beginswith, which was recently renamed to startswith but only in Julia 0.4. (Make sure you look at the appropriate docs http://julia.readthedocs.org/en/release-0.3/stdlib/strings/ for Julia 0.3; there's a version selector in the bottom right.) You can also use the Compat package, which makes startswith available on Julia 0.3. On Friday, January 9, 2015 at 1:44:29 PM UTC-5, Tim Wheeler wrote: When I try to use the startswith http://julia.readthedocs.org/en/latest/stdlib/strings/ function Julia cannot locate it. endswith seems to work fine. Am I doing something wrong? Thanks! _ _ _(_)_ | A fresh approach to technical computing (_) | (_) (_)| Documentation: http://docs.julialang.org _ _ _| |_ __ _ | Type help() for help. | | | | | | |/ _` | | | | |_| | | | (_| | | Version 0.3.4 (2014-10-21 20:18 UTC) _/ |\__'_|_|_|\__'_| | Official http://julialang.org release |__/ | x86_64-linux-gnu julia startswith(asdfas, asd) ERROR: startswith not defined julia startswith ERROR: startswith not defined julia endswith(blah, ah) true
[julia-users] Re: Julia v0.3.5
Looking forward for the Linux version! Τη Παρασκευή, 9 Ιανουαρίου 2015 - 2:11:13 μ.μ. UTC-5, ο χρήστης Elliot Saba έγραψε: Hello all! The latest bugfix release of the 0.3.x Julia line has been released. Binaries are available from the usual place http://julialang.org/downloads/, and as is typical with such things, please report all issues to either the issue tracker https://github.com/JuliaLang/julia/issues, or email this list. As this is a bugfix release, there are not too many new big-item features to announce, but if you are interested in the bugs fixed since 0.3.4, this commit log https://github.com/JuliaLang/julia/compare/v0.3.4...v0.3.5 should give you an idea of the effort put in by our team of backporters. This is a recommended upgrade for anyone using any of the previous 0.3.x releases, and should act as a drop-in replacement for any of the 0.3.x line. We would like to get feedback if someone has a correctly working program that doesn't work after this upgrade. Happy Hacking, -E
Re: [julia-users] MPI.jl issue
The other closest explanation is here -
https://code.google.com/p/chromium/issues/detail?id=31809#c31 . Though, I
still don't know what to fix to get MPI.jl up and running on my system
On Fri, Jan 9, 2015 at 5:53 PM, Amit Murthy amit.mur...@gmail.com wrote:
A google search for that error all showed up issues related to either a)
cross-compiling or b) memory allocated by one library being
freed/realloc'ed by another. Issue -
https://github.com/JuliaParallel/MPI.jl/issues/25
On Fri, Jan 9, 2015 at 3:20 PM, Viral Shah vi...@mayin.org wrote:
Oops - realized that you are running in fact in linux. Sorry - ignore my
comment then. Could it be a julia issue, or some MPI call that is not being
used correctly and is corrupting memory?
-viral
On 09-Jan-2015, at 3:18 pm, Viral Shah vi...@mayin.org wrote:
How about trying out some other MPI library? Mac support may vary
across multiple open source implementations.
-viral
On Thursday, January 8, 2015 at 9:28:21 PM UTC+5:30, Amit Murthy wrote:
Hello,
On a Ubuntu 14.10 system, I am having trouble getting MPI.jl up and
running.
The deps build goes through fine, but fails on running tests with the
following error:
amitm@amitm-macbookpro:~/.julia/v0.4/MPI/test$ mpirun -np 4 julia
test_bcast.jl
WARNING: deprecated syntax {a=b, ...} at
/home/amitm/.julia/v0.4/MPI/src/mpi-base.jl:19.
Use Dict{Any,Any}(a=b, ...) instead.
.
Inconsistency detected by ld.so: dl-minimal.c: 136: realloc: Assertion
`ptr == alloc_last_block' failed!
systeminfo:
| | |_| | | | (_| | | Version 0.4.0-dev+2558 (2015-01-08 07:21 UTC)
_/ |\__'_|_|_|\__'_| | Commit 2bb647a (0 days old master)
|__/ | x86_64-linux-gnu
It is actually a MacbookPro running Ubuntu.
FWIW, it works fine on a different Ubuntu 14.04 Core i5 system.
Amit
[julia-users] Problems understanding the usage of SharedArrays
Sorry for asking the umptiest question on parallelization, but I can't seem
to get even the most basic calculations to run, so here's hoping someone
can enlighten me.
I'd like to make an interpolant created with Dierckx available on all my
processors. I figured this would be possible using SharedArrays in the
following way:
addprocs(3)
@everywhere using Dierckx
xgrid = linspace(0.1, 100.0, 200)
f_val = [xgrid[i]^(-0.25) for i in length(xgrid)]
convert(SharedArray, xgrid)
convert(SharedArray, f_val)
@everywhere interpolant = Spline1D(xgrid, f_val)
However, this attempt fails with two errors (or six, two for each core):
Spline1D has not method matching Spline1D(::SharedArray{Float64, 1},
::SharedArray{Float64, 1})
and
xgrid_irr not defined
I have a question on each:
1. How can I use SharedArrays in functions that don't have a method for
them?
2. Why would xgrid not be defined on the other cores? My understanding from
reading the SharedArray part
http://julia.readthedocs.org/en/latest/manual/parallel-computing/#shared-arrays-experimental
of
the documentation was that their whole purpose is to make an array
available to all cores?
Any hints or tips would be greatly appreciated!
Re: [julia-users] Problems understanding the usage of SharedArrays
1. You (or someone) needs to write those methods. Since the Spline1D function
is defined in the Dierckx package, that's where the SharedArray version should
go, too (if you don't just maintain it on your own).
2. After
convert(SharedArray, xgrid)
xgrid will still be an ordinary array. You've not captured the output of the
convert function.
--Tim
On Friday, January 09, 2015 08:33:20 AM Nils Gudat wrote:
Sorry for asking the umptiest question on parallelization, but I can't seem
to get even the most basic calculations to run, so here's hoping someone
can enlighten me.
I'd like to make an interpolant created with Dierckx available on all my
processors. I figured this would be possible using SharedArrays in the
following way:
addprocs(3)
@everywhere using Dierckx
xgrid = linspace(0.1, 100.0, 200)
f_val = [xgrid[i]^(-0.25) for i in length(xgrid)]
convert(SharedArray, xgrid)
convert(SharedArray, f_val)
@everywhere interpolant = Spline1D(xgrid, f_val)
However, this attempt fails with two errors (or six, two for each core):
Spline1D has not method matching Spline1D(::SharedArray{Float64, 1},
::SharedArray{Float64, 1})
and
xgrid_irr not defined
I have a question on each:
1. How can I use SharedArrays in functions that don't have a method for
them?
2. Why would xgrid not be defined on the other cores? My understanding from
reading the SharedArray part
http://julia.readthedocs.org/en/latest/manual/parallel-computing/#shared-ar
rays-experimental of the documentation was that their whole purpose is to
make an array available to all cores?
Any hints or tips would be greatly appreciated!
[julia-users] Re: string literals split to multilines?
Note that in Python if there is whitespace after the `\` it will be an error: In [1]: s = 'one\ File ipython-input-1-270c132ab6d9, line 1 s = 'one\ ^ SyntaxError: EOL while scanning string literal I think it's safer to just use the implicit string literal concatenation and implicit line continuation: In [2]: s = ( ...: 'one' ...: 'two' ...: 'tree' ...: ) In [3]: s Out[3]: 'onetwotree' Which lets you order the strings with the desired indentation level, without forcing you to write them flush to the left, as in your example. El martes, 6 de enero de 2015, 4:15:13 (UTC-6), Andreas Lobinger escribió: Hello colleagues, is there a counterpart for the string literal split to multiple lines like in python? d = '09ab\ eff1\ a2a4' Wishing a happy day, Andreas
[julia-users] working with dense parallel matrices
Hello, I am very new to Julia and trying to learn how to do some very basic linear algebra in parallel. As a simple example I wanted to be able to define a dense matrix in parallel and then multiply it to a vector. I have seen how this works in mpi4py at the following tutorial http://mpi4py.scipy.org/docs/usrman/tutorial.html and am wondering how this can be accomplished in julia. I did find a number of posts that discussed how to do this with sparse matrices, which seems more complicated than it should be. I did read about distributed arrays and that seems like it should be able to do something along these lines but I didn't seen any examples and am unsure of the syntax. Is this the best direction to go or is there something else I should be looking into? I am sorry for asking such a simple question but would appreciate any assistance. Cheers, Francis
Re: [julia-users] How can I achieve C-like ## in Julia
https://github.com/JuliaLang/julia/pull/9700 On Thursday, January 8, 2015 at 10:29:20 AM UTC-5, Josh Langsfeld wrote: Thanks, I might do that sometime in the next few days if I can make it look a bit nicer. On Wednesday, January 7, 2015 4:24:32 PM UTC-5, Ivar Nesje wrote: The problem is where we should stop adding methods to allow symbols to work as strings. * might be fine though, so please submit your code as a pull request, so we can get more opinions on the matter. (Be sure to link to this discussion and to post a link to the PR here)
[julia-users] Strange random NaN's
I'm using julia-0.3.3 installed via yum on Fedora 21 (I saw similar
behavior on Fedora 20 as well) and I'm encountering behavior I can't
explain. I haven't been able to come up with a simple reproducible case
because this weirdness with NaN's appears to be non-deterministic and
restarting my julia kernel fixes the problem more often than not but I'm
hoping that someone can shed some light on what's going on.
Here's the relevant part of my notebook session (I apologize for the
strange formatting):
anynan{T}(x::Array{T}) = any(isnan, x)
anynan (generic function with 3 methods)
typeof(rkvar2.w.f1), typeof(rkvar2.sol), typeof(rkvar2.w.rk2)
(Array{Float64,2},Array{Float64,2},Array{Float64,2})
anynan(rkvar2.w.f1), minimum(rkvar2.w.f1), maximum(rkvar2.w.f1)
(false,-1.1317244376193704e-5,5.1067129405671126e-5)
anynan(rkvar2.sol), minimum(rkvar2.sol), maximum(rkvar2.sol)
(false,-2.789562122325811e-7,4.2592881099528343e-7)
rkvar2.w.rk2[:,:] = rkvar2.sol + 0.5t.dt*rkvar.w.f1
241x241 Array{Float64,2}:
3.38286e-48 1.52057e-48 -9.05314e-48 … 1.52057e-48 3.38286e-48
1.52057e-48 -8.61345e-48 -3.74648e-47 -8.61345e-48 1.52057e-48
-9.05314e-48 -3.74648e-47 -9.55565e-47 -3.74648e-47 -9.05314e-48
-3.83165e-47 -9.43884e-47 -1.73654e-46 -9.43884e-47 -3.83165e-47
-9.57104e-47 -1.7154e-46 -2.02324e-46 -1.7154e-46 -9.57104e-47
-1.73204e-46 -2.02326e-46 2.34406e-47 … -2.02326e-46 -1.73204e-46
-2.05281e-46 7.70356e-48 9.17989e-46 7.70356e-48 -2.05281e-46
-1.58991e-48 8.52933e-46 3.06846e-45 8.52933e-46 -1.58991e-48
8.2321e-462.89897e-45 6.82077e-45 2.89897e-45 8.2321e-46
2.8249e-456.51202e-45 1.10477e-44 6.51202e-45 2.8249e-45
6.38329e-45 1.07416e-44 1.06454e-44 … 1.07416e-44 6.38329e-45
1.06528e-44 1.10276e-44 -7.09009e-45 1.10276e-44 1.06528e-44
1.13985e-44 -4.18965e-45 -6.50745e-44 -4.18965e-45 1.13985e-44
⋮⋱ ⋮
1.06528e-44 1.10276e-44 -7.09009e-45 1.10276e-44 1.06528e-44
6.38329e-45 1.07416e-44 1.06454e-44 … 1.07416e-44 6.38329e-45
2.8249e-456.51202e-45 1.10477e-44 6.51202e-45 2.8249e-45
8.2321e-462.89897e-45 6.82077e-45 2.89897e-45 8.2321e-46
-1.58991e-48 8.52933e-46 3.06846e-45 8.52933e-46 -1.58991e-48
-2.05281e-46 7.70356e-48 9.17989e-46 7.70356e-48 -2.05281e-46
-1.73204e-46 -2.02326e-46 2.34406e-47 … -2.02326e-46 -1.73204e-46
-9.57104e-47 -1.7154e-46 -2.02324e-46 -1.7154e-46 -9.57104e-47
-3.83165e-47 -9.43884e-47 -1.73654e-46 -9.43884e-47 -3.83165e-47
-9.05314e-48 -3.74648e-47 -9.55565e-47 -3.74648e-47 -9.05314e-48
1.52057e-48 -8.61345e-48 -3.74648e-47 -8.61345e-48 1.52057e-48
3.38286e-48 1.52057e-48 -9.05314e-48 … 1.52057e-48 3.38286e-48
anynan(rkvar2.w.rk2)
true
anynan(rkvar2.sol + 0.5t.dt*rkvar.w.f1)
true
0.5t.dt
0.005001
anynan(rkvar2.sol + rkvar.w.f1)
true
idx2d(find(isnan(rkvar2.w.rk2)), rkvar2.w.rk2)
([(122,86),(125,87)],[NaN,NaN])
i, j = 122, 86
rkvar2.w.rk2[i,j]
NaN
rkvar2.w.rk2[i,j] = rkvar2.sol[i,j] + 0.5t.dt*rkvar2.w.f1[i,j]
0.0
for jj=1:size(rkvar2.sol, 2), ii=1:size(rkvar2.sol,1)
rkvar2.w.rk2[ii,jj] = rkvar2.sol[ii,jj] + 0.5t.dt*rkvar2.w.f1[ii,jj]
end
rkvar2.w.rk2[i,j]
0.0
Where are these NaN's coming from? In the actual code that's failing the
updates are written using @nloops and @nref but I'm still getting NaN's.
Any ideas on what I can do?
thanks,
Peter.
[julia-users] Re: I cannot reach GitHub because of a firewall at work, how do I install Gadfly?
See the manual on how to use git from behind a firewall: http://docs.julialang.org/en/latest/manual/packages/ In particular, you can run git config --global url.https://.insteadOf git:// to tell git to use https instead of git protocol, in order to work with your firewall.
Re: [julia-users] Re: I cannot reach GitHub because of a firewall at work, how do I install Gadfly?
I've seen both the git manual and the https/git workaround before. Neither seem to work. The method for configuring git is also discussed here https://groups.google.com/forum/#!msg/julia-users/W-H6ZxjXYSI/RWnc6bk6e-EJ and tried running C:\Julia-0.3.4\Git\cmdgit config --global http.proxy http://[autoproxy thingy from IE] C:\Julia-0.3.4\Git\cmdgit config --global http.proxy http://@[autoproxy thiny from IE] C:\Julia-0.3.4\Git\cmdgit config --global http.proxy http://[userid]@[autoproxy thingy from IE] and nothing even remotely works. I spent some time on the phone with tech support and might as well have poked myself in the eye. On Fri, Jan 9, 2015 at 4:47 PM, Steven G. Johnson stevenj@gmail.com wrote: See the manual on how to use git from behind a firewall: http://docs.julialang.org/en/latest/manual/packages/ In particular, you can run git config --global url.https://.insteadOf git:// to tell git to use https instead of git protocol, in order to work with your firewall.
Re: [julia-users] Julia v0.3.5
Man, that is an impressive amount of backporting in just a few weeks. You guys are amazing. --Tim On Friday, January 09, 2015 11:10:30 AM Elliot Saba wrote: Hello all! The latest bugfix release of the 0.3.x Julia line has been released. Binaries are available from the usual place http://julialang.org/downloads/, and as is typical with such things, please report all issues to either the issue tracker https://github.com/JuliaLang/julia/issues, or email this list. As this is a bugfix release, there are not too many new big-item features to announce, but if you are interested in the bugs fixed since 0.3.4, this commit log https://github.com/JuliaLang/julia/compare/v0.3.4...v0.3.5 should give you an idea of the effort put in by our team of backporters. This is a recommended upgrade for anyone using any of the previous 0.3.x releases, and should act as a drop-in replacement for any of the 0.3.x line. We would like to get feedback if someone has a correctly working program that doesn't work after this upgrade. Happy Hacking, -E
[julia-users] Re: startswith not defined
Yes, that was it. Thank you On Friday, January 9, 2015 at 11:04:17 AM UTC-8, Simon Kornblith wrote: You probably want beginswith, which was recently renamed to startswith but only in Julia 0.4. (Make sure you look at the appropriate docs http://julia.readthedocs.org/en/release-0.3/stdlib/strings/ for Julia 0.3; there's a version selector in the bottom right.) You can also use the Compat package, which makes startswith available on Julia 0.3. On Friday, January 9, 2015 at 1:44:29 PM UTC-5, Tim Wheeler wrote: When I try to use the startswith http://julia.readthedocs.org/en/latest/stdlib/strings/ function Julia cannot locate it. endswith seems to work fine. Am I doing something wrong? Thanks! _ _ _(_)_ | A fresh approach to technical computing (_) | (_) (_)| Documentation: http://docs.julialang.org _ _ _| |_ __ _ | Type help() for help. | | | | | | |/ _` | | | | |_| | | | (_| | | Version 0.3.4 (2014-10-21 20:18 UTC) _/ |\__'_|_|_|\__'_| | Official http://julialang.org release |__/ | x86_64-linux-gnu julia startswith(asdfas, asd) ERROR: startswith not defined julia startswith ERROR: startswith not defined julia endswith(blah, ah) true
Re: [julia-users] Why is memory allocated to run loop?
On Friday, January 09, 2015 11:51:15 AM Petr Krysl wrote: The size() function is from the base. fes.conn is a 2D array of ints. No it's not, it's a JFIntMat :-). But seriously, _you_ might know that corresponds to a 2D array of ints, but the key question is if you've given enough information in your declaration of JFIntMat for the _compiler_ to know that. Bottom line is that the behavior you're observing isn't normal. It's also extremely unlikely to be a bug in julia, because we do this kind of stuff all over julia without the kind of allocations you're observing. So it's almost certainly something in your code. @code_warntype is your best way of finding out. --Tim P On Friday, January 9, 2015 at 11:25:34 AM UTC-8, Tim Holy wrote: I wonder if your `size` function isn't returning an Int, or that the compiler can't infer that. Did you try this on julia 0.4 with @code_warntype? --Tim On Friday, January 09, 2015 08:06:43 AM Petr Krysl wrote: Actually, I realized that the example shows the same problem, but for a different function. This time the allocation occurs in the function CALLING getconn!. P On Friday, January 9, 2015 at 8:03:00 AM UTC-8, Petr Krysl wrote: Tim, Your explanation is very helpful. There is a complication though: The function is actually called from another function (from a chain of functions). Here is the measurement from a simplified situation: - using JFFoundationModule - using FESetModule - - n=100; - function test(n) 0 fes=FESetH8(conn=rand(JFInt,n, 8)) 112 conn1::JFIntMat=zeros(JFInt,nfense(fes),1); 6383704 for j=1:size(fes.conn,1) 0 getconn!(fes,conn1,j); - end 0 return true - end - - test(n) - clear_malloc_data() - n=10; - test(n) As you can see the loop over the rows of the array fes.conn allocates substantial amount of memory. Here is the type: https://gist.github.com/PetrKryslUCSD/794f521a8e5b057e5e4e Petr
Re: [julia-users] Why is memory allocated to run loop?
Sometimes Julia can have problems inferring the type of fields of types which is what conn is. Using @code_warntype as suggested by Tim could give an indication if it is the case. 2015-01-09 14:51 GMT-05:00 Petr Krysl krysl.p...@gmail.com: The size() function is from the base. fes.conn is a 2D array of ints. P On Friday, January 9, 2015 at 11:25:34 AM UTC-8, Tim Holy wrote: I wonder if your `size` function isn't returning an Int, or that the compiler can't infer that. Did you try this on julia 0.4 with @code_warntype? --Tim On Friday, January 09, 2015 08:06:43 AM Petr Krysl wrote: Actually, I realized that the example shows the same problem, but for a different function. This time the allocation occurs in the function CALLING getconn!. P On Friday, January 9, 2015 at 8:03:00 AM UTC-8, Petr Krysl wrote: Tim, Your explanation is very helpful. There is a complication though: The function is actually called from another function (from a chain of functions). Here is the measurement from a simplified situation: - using JFFoundationModule - using FESetModule - - n=100; - function test(n) 0 fes=FESetH8(conn=rand(JFInt,n, 8)) 112 conn1::JFIntMat=zeros(JFInt,nfense(fes),1); 6383704 for j=1:size(fes.conn,1) 0 getconn!(fes,conn1,j); - end 0 return true - end - - test(n) - clear_malloc_data() - n=10; - test(n) As you can see the loop over the rows of the array fes.conn allocates substantial amount of memory. Here is the type: https://gist.github.com/PetrKryslUCSD/794f521a8e5b057e5e4e Petr
Re: [julia-users] Problems understanding the usage of SharedArrays
Hi Nils,
We could add a SharedArray method to the Dierckx methods as Tim suggests,
perhaps by changing the signatures from Vector{Float64} to
Union(Vector{Float64},
SharedArray{Float64, 1}).
I’ve never really used SharedArrays myself, but an easier option might be
to use the sdata function. For example, the following works for me:
xgrid = convert(SharedArray, linspace(0.1, 100., 200))
fval = convert(SharedArray, Float64[xgrid[i]^(-0.25) for i in 1:length(xgrid)])
spl = Spline1D(sdata(xgrid), sdata(fval))
Best,
Kyle
On Fri, Jan 9, 2015 at 11:47 AM, Tim Holy tim.h...@gmail.com wrote:
1. You (or someone) needs to write those methods. Since the Spline1D
function
is defined in the Dierckx package, that's where the SharedArray version
should
go, too (if you don't just maintain it on your own).
2. After
convert(SharedArray, xgrid)
xgrid will still be an ordinary array. You've not captured the output of
the
convert function.
--Tim
On Friday, January 09, 2015 08:33:20 AM Nils Gudat wrote:
Sorry for asking the umptiest question on parallelization, but I can't
seem
to get even the most basic calculations to run, so here's hoping someone
can enlighten me.
I'd like to make an interpolant created with Dierckx available on all my
processors. I figured this would be possible using SharedArrays in the
following way:
addprocs(3)
@everywhere using Dierckx
xgrid = linspace(0.1, 100.0, 200)
f_val = [xgrid[i]^(-0.25) for i in length(xgrid)]
convert(SharedArray, xgrid)
convert(SharedArray, f_val)
@everywhere interpolant = Spline1D(xgrid, f_val)
However, this attempt fails with two errors (or six, two for each core):
Spline1D has not method matching Spline1D(::SharedArray{Float64, 1},
::SharedArray{Float64, 1})
and
xgrid_irr not defined
I have a question on each:
1. How can I use SharedArrays in functions that don't have a method for
them?
2. Why would xgrid not be defined on the other cores? My understanding
from
reading the SharedArray part
http://julia.readthedocs.org/en/latest/manual/parallel-computing/#shared-ar
rays-experimental of the documentation was that their whole purpose is
to
make an array available to all cores?
Any hints or tips would be greatly appreciated!
[julia-users] REQUIRES and a specific branch?
I find myself frequently using features in other packages that haven't been tagged yet. Is there a way to specify in REQUIRES that, say, the master branch of the package should be used instead of the latest tagged version? This way others, including Travis and Coveralls, can get the correct functionality until the maintainers create a new tag for the required package.
Re: [julia-users] Re: I cannot reach GitHub because of a firewall at work, how do I install Gadfly?
On Friday, January 9, 2015 at 4:56:29 PM UTC-5, John Hall wrote: I've seen both the git manual and the https/git workaround before. Neither seem to work. If you do git clone manually from the command line with an https or http URL, does it work? It would be good to diagnose the specific problem.
[julia-users] Re: Julia v0.3.5
yeah ... that parenthetical has been static since at least version 0.3.3 curious ... On Friday, January 9, 2015 at 3:06:29 PM UTC-8, ele...@gmail.com wrote: Thank you all the people who bring these updates. One question, the versioninfo() for theJulia release on the Ubuntu PPA says: Version 0.3.5 Commit 21d5433* (2014-10-21 20:18 UTC) The commit and date/time hasn't changed. Cheers Lex On Saturday, January 10, 2015 at 5:11:13 AM UTC+10, Elliot Saba wrote: Hello all! The latest bugfix release of the 0.3.x Julia line has been released. Binaries are available from the usual place http://julialang.org/downloads/, and as is typical with such things, please report all issues to either the issue tracker https://github.com/JuliaLang/julia/issues, or email this list. As this is a bugfix release, there are not too many new big-item features to announce, but if you are interested in the bugs fixed since 0.3.4, this commit log https://github.com/JuliaLang/julia/compare/v0.3.4...v0.3.5 should give you an idea of the effort put in by our team of backporters. This is a recommended upgrade for anyone using any of the previous 0.3.x releases, and should act as a drop-in replacement for any of the 0.3.x line. We would like to get feedback if someone has a correctly working program that doesn't work after this upgrade. Happy Hacking, -E
Re: [julia-users] Re: I cannot reach GitHub because of a firewall at work, how do I install Gadfly?
Tried before. Doesn't work. On Fri, Jan 9, 2015 at 5:23 PM, Steven G. Johnson stevenj@gmail.com wrote: On Friday, January 9, 2015 at 4:56:29 PM UTC-5, John Hall wrote: I've seen both the git manual and the https/git workaround before. Neither seem to work. If you do git clone manually from the command line with an https or http URL, does it work? It would be good to diagnose the specific problem.
Re: [julia-users] Re: Julia v0.3.5
We noticed that as well https://github.com/JuliaLang/julia/commit/a05f87b79ad62beb033817fdfdefa270c9557aaf. An `apt-get update apt-get upgrade` should fix it. -E On Fri, Jan 9, 2015 at 3:15 PM, cdm cdmclean@gmail.com wrote: yeah ... that parenthetical has been static since at least version 0.3.3 curious ... On Friday, January 9, 2015 at 3:06:29 PM UTC-8, ele...@gmail.com wrote: Thank you all the people who bring these updates. One question, the versioninfo() for theJulia release on the Ubuntu PPA says: Version 0.3.5 Commit 21d5433* (2014-10-21 20:18 UTC) The commit and date/time hasn't changed. Cheers Lex On Saturday, January 10, 2015 at 5:11:13 AM UTC+10, Elliot Saba wrote: Hello all! The latest bugfix release of the 0.3.x Julia line has been released. Binaries are available from the usual place http://julialang.org/downloads/, and as is typical with such things, please report all issues to either the issue tracker https://github.com/JuliaLang/julia/issues, or email this list. As this is a bugfix release, there are not too many new big-item features to announce, but if you are interested in the bugs fixed since 0.3.4, this commit log https://github.com/JuliaLang/julia/compare/v0.3.4...v0.3.5 should give you an idea of the effort put in by our team of backporters. This is a recommended upgrade for anyone using any of the previous 0.3.x releases, and should act as a drop-in replacement for any of the 0.3.x line. We would like to get feedback if someone has a correctly working program that doesn't work after this upgrade. Happy Hacking, -E
[julia-users] Re: I cannot reach GitHub because of a firewall at work, how do I install Gadfly?
My issue is that same as the OPs, but I'm trying to install NLopt.jl rather than Gadfly. I perennially have this issue with Python files, but it seems that I've had better luck getting stuff to work manually with Python than Julia. Pkg.init() doesn't work for me. I first looked in the require file for NLopt. It has BinDeps and MathProgBase 0.3.0 0.4.0-. BinDeps requires URIParser and SHA. So my .julia folder contains each of those .zip files and the unzipped folders that include the directories. However, when I try to start with the ones without dependencies, I run require(MathProgBase) then I get an error about it not being found. I tried a bunch of variations on this (and following the first answer on http://stackoverflow.com/questions/14092316/simplest-ways-to-make-a-julia-package-available-to-others) without much luck. On Tuesday, November 26, 2013 at 1:07:30 PM UTC-5, Connor Johnson wrote: Aha! I am on the trail. I called Pkg.init() from the Julia command line interpreter, is that what you meant? At any rate, it gave me an error about a failed process, which I think is due to the firewall. I went ahead and made a .julia directroy in the directory output by `homedir()` as you suggested, and I got a dependency error instead of a process error, so that's good news. Thanks for your help! On Tuesday, November 26, 2013 11:28:58 AM UTC-6, Connor Johnson wrote: I saw on stackoverflow http://www.google.com/url?q=http%3A%2F%2Fstackoverflow.com%2Fquestions%2F14092316%2Fsimplest-ways-to-make-a-julia-package-available-to-otherssa=Dsntz=1usg=AFQjCNGf-64GBqjIckQilbXbAUq-Ns7dBA that I could `require` a package that was placed in `.julia/`, so I downloaded the Gadfly zip file from GitHub manually, and then started putting it different places in my C:\julia directory, and tried to `require` it from the command line, but to no avail. Is there a way to install packages manually like you do in Python with `python setup.py install`? I am using Windows 7 (because I'm at work), and Pkg.add(Gadfly) won't work because of a firewall or something. (I can use Invoke-WebRequest via Windows PowerShell, but I can't use pip, the Python installer for some reason.) Thanks!
[julia-users] Re: QZ Decomposition Reordering
See this pull request https://github.com/JuliaLang/julia/pull/9701, it will hopefully provide this functionality On Friday, October 10, 2014 at 5:16:30 PM UTC-4, Ricardo Mayer wrote: Can the schurfact(A,B) function reorder its output such that generalized eigenvalues appear in descending (or ascending) magnitude order? I'm thinking on something like MATLAB's ordqz or R's ordqz (in QZ package). I'm really new to Julia (installed it just a few minutes ago, actually), but as far as I can tell there is no select option for this function. best, Ricardo
Re: [julia-users] How to define help text for a module function so that it displays in REPL
On Friday, January 9, 2015 at 7:08:03 PM UTC+10, Ivar Nesje wrote: Compat.jl doesn't have doc, but there is a package that is reported to do this for 0.3. I think it was Doctile.jl, but I might remember wrong. See https://github.com/JuliaLang/julia/pull/9447 for first pass at documentation. Cheers Lex
[julia-users] Re: How to create a composite type of function pointers
No, unfortunately changing to immutable does no good either.
The problem originally is that I get a 'no method' error in this code
@show typeof(cd_canvas)
@show methods(cdCanvasActivate)
cdCanvasActivate(cd_canvas);
which prints
typeof(cd_canvas) = Ptr{cdCanvas}
methods(cdCanvasActivate) = # 1 method for generic function
cdCanvasActivate:
cdCanvasActivate(canvas::Ptr{cdCanvas}) at
C:\programs\Gits\IUP.jl\src\libcd.jl:278
ERROR: `cdCanvasActivate` has no method matching
cdCanvasActivate(::Ptr{cdCanvas})
I have once crossed a similar situation that turned out to be due to the
type being defined in two files, but multiple-checked and that's not the
case here.
Actually the error is not restricted to this cdCanvasActivate() function
but happens with any function that has the same signature. Hence my
suspicious with cdCanvas Type.
quinta-feira, 8 de Janeiro de 2015 às 23:40:47 UTC, J Luis escreveu:
Hi,
I need to convert a structure of function pointers into a composite type.
The struct looks like this
struct cdCanvas {
void (*cxPixel)(cdCtxCanvas* ctxcanvas, int x, int y, long color);
void (*cxLine)(cdCtxCanvas* ctxcanvas, int x1, int y1, int x2, int y2);
void (*cxPoly)(cdCtxCanvas* ctxcanvas, int mode, cdPoint* points, int
n);
...
used Clang.jl but it converted it to
type cdCanvas
cxPixel::Ptr{Void}
cxLine::Ptr{Void}
cxPoly::Ptr{Void}
...
which is not correct (ccall crash when using this type), but I don't know
either how to write it manually.
Any advise?
Thanks
[julia-users] Re: How to create a composite type of function pointers
No shit (well, nice) I fall in the same trap again. It turns up that the
file where cdCanvas is declared was included twice and so I apparently had
two declarations of cdCanvas. Once I fixed this situation the program runs
fine but this is a hell of a slippery ground.
quinta-feira, 8 de Janeiro de 2015 às 23:40:47 UTC, J Luis escreveu:
Hi,
I need to convert a structure of function pointers into a composite type.
The struct looks like this
struct cdCanvas {
void (*cxPixel)(cdCtxCanvas* ctxcanvas, int x, int y, long color);
void (*cxLine)(cdCtxCanvas* ctxcanvas, int x1, int y1, int x2, int y2);
void (*cxPoly)(cdCtxCanvas* ctxcanvas, int mode, cdPoint* points, int
n);
...
used Clang.jl but it converted it to
type cdCanvas
cxPixel::Ptr{Void}
cxLine::Ptr{Void}
cxPoly::Ptr{Void}
...
which is not correct (ccall crash when using this type), but I don't know
either how to write it manually.
Any advise?
Thanks
Re: [julia-users] MPI.jl issue
A google search for that error all showed up issues related to either a)
cross-compiling or b) memory allocated by one library being
freed/realloc'ed by another. Issue -
https://github.com/JuliaParallel/MPI.jl/issues/25
On Fri, Jan 9, 2015 at 3:20 PM, Viral Shah vi...@mayin.org wrote:
Oops - realized that you are running in fact in linux. Sorry - ignore my
comment then. Could it be a julia issue, or some MPI call that is not being
used correctly and is corrupting memory?
-viral
On 09-Jan-2015, at 3:18 pm, Viral Shah vi...@mayin.org wrote:
How about trying out some other MPI library? Mac support may vary across
multiple open source implementations.
-viral
On Thursday, January 8, 2015 at 9:28:21 PM UTC+5:30, Amit Murthy wrote:
Hello,
On a Ubuntu 14.10 system, I am having trouble getting MPI.jl up and
running.
The deps build goes through fine, but fails on running tests with the
following error:
amitm@amitm-macbookpro:~/.julia/v0.4/MPI/test$ mpirun -np 4 julia
test_bcast.jl
WARNING: deprecated syntax {a=b, ...} at
/home/amitm/.julia/v0.4/MPI/src/mpi-base.jl:19.
Use Dict{Any,Any}(a=b, ...) instead.
.
Inconsistency detected by ld.so: dl-minimal.c: 136: realloc: Assertion
`ptr == alloc_last_block' failed!
systeminfo:
| | |_| | | | (_| | | Version 0.4.0-dev+2558 (2015-01-08 07:21 UTC)
_/ |\__'_|_|_|\__'_| | Commit 2bb647a (0 days old master)
|__/ | x86_64-linux-gnu
It is actually a MacbookPro running Ubuntu.
FWIW, it works fine on a different Ubuntu 14.04 Core i5 system.
Amit
Re: [julia-users] How to define help text for a module function so that it displays in REPL
Thanks a lot. I'll check it out. Jan Dňa piatok, 9. januára 2015 10:53:10 UTC+1 Tim Holy napísal(-a): If you're running julia 0.3, see the Docile package. If you're running julia 0.4, there's some in-progress documentation here: https://github.com/JuliaLang/julia/pull/9447 --Tim On Friday, January 09, 2015 12:41:33 AM Ján Dolinský wrote: Hi, I would like to know how to write a help (comment) text e.g. for a module function so that once module is loaded it is displayed if in REPL help mode. E.g. for sum function help?sum displays brief help text for sum Thanks, Jan
Re: [julia-users] Installing packages system-wide
All right.
Dňa piatok, 9. januára 2015 10:46:54 UTC+1 Tim Holy napísal(-a):
On Friday, January 09, 2015 12:34:12 AM Ján Dolinský wrote:
Hi,
Thanks for the tip. How do I open an issue about this problem ?
https://github.com/JuliaLang/julia, then click on the Issues link near
the
top-right. It's a kindness to busy developers to first search to see if
your
issue has been reported previously (I haven't done so for this problem, so
please do).
--Tim
Jan
Dňa štvrtok, 8. januára 2015 16:13:40 UTC+1 Tim Holy napísal(-a):
There's a second way to install system-wide (at least, on Unix): set
the
environment variable JULIA_PKGDIR for all users. If you do it that
way,
tab
completion works.
Still, it would be nice if tab completion also worked with LOAD_PATH.
Care
to
open an issue about that?
--Tim
On Thursday, January 08, 2015 07:08:32 AM Ján Dolinský wrote:
I noticed that if packages are installed system-wide than
auto-completion
for a regular user does not work e.g. typing
julia using Arr
will not auto-complete Arr (after pressing the Tab key) to
ArrayViews.
This is not the case if packages are installed in user's home
directory.
Is
there any fix for this ?
Thanks,
Jan
Dňa streda, 10. decembra 2014 16:28:15 UTC+1 Stefan Karpinski
napísal(-a):
There's a Julia variable called LOAD_PATH that is arranged to
point at
two
system directories under your julia installation. E.g.:
julia LOAD_PATH
2-element Array{Union(ASCIIString,UTF8String),1}:
/opt/julia-0.3.3/usr/local/share/julia/site/v0.3
/opt/julia-0.3.3/usr/share/julia/site/v0.3
If you install packages under either of those directories, then
everyone
using that Julia will see them. One way to do this is to run julia
as
a
user who can write to those directories after doing `export
JULIA_PKGDIR=/opt/julia-0.3.3/usr/share/julia/site` in the shell.
That
way
Julia will use that as it's package directory and normal package
commands
will allow you to install packages for everyone. Or you can just
copy
your
installed packages and change the ownership and permissions so
that
everyone can access the files.
On Wed, Dec 10, 2014 at 8:16 AM, Ján Dolinský
jan.do...@2bridgz.com
javascript: wrote:
Hello,
I'd like to ask how to install Julia packages system-wide so that
users
do not have to install packages individually on their own but
rather
just
once by an admin.
Thanks,
Jan
