August 06, 2015 Re: D for project in computational chemistry | ||||
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Posted in reply to Gerald Jansen | On Thursday, 6 August 2015 at 08:11:49 UTC, Gerald Jansen wrote:
> Is the Dscience github project an adequate platform? How can other people get involved? Is a dedicated discussion group needed? Can we develop a plan of some sort rather than just a scatter of individual efforts?
If a group do go ahead with this it would be good to add a discussion group on this forum for it to maximize visibility and involvement.
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August 06, 2015 Re: D for project in computational chemistry | ||||
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Posted in reply to Gerald Jansen | On Thursday, 6 August 2015 at 08:11:49 UTC, Gerald Jansen wrote: > On Wednesday, 5 August 2015 at 18:49:21 UTC, bachmeier wrote: > >> Yes. The question is whether we can put together a group of developers to build the infrastructure, which is a lot more than just code. That means, in particular, good documentation and using it for our own projects. > > Right on! I would be willing to help with documentation if there were a concerted effort in this direction. There have been a number of failed individual efforts over the years. So how can a group effort be promoted? > > Is the Dscience github project an adequate platform? How can other people get involved? Is a dedicated discussion group needed? Can we develop a plan of some sort rather than just a scatter of individual efforts? Yes, come join https://github.com/DlangScience. Ilya and I have a plan of sorts, but it needs formally writing down. For now we have been using a private Gitter room for discussion, which has been OK for now but likely won't scale. Anyone serious about getting involved, drop a message here: https://gitter.im/DlangScience/public and we can start to build a picture of what expertise we have and what we're missing. A proper public forum could be great, but I don't personally have time to set up something like that at the moment. |
August 16, 2015 Re: D for project in computational chemistry | ||||
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Posted in reply to Yura | Good afternoon, gentlemen, just want to describe my very limited experience. I have re-written about half of my Python code into D. I got it faster by 6 times. This is a good news. However, I was amazed by performance of D vs Python for following simple nested loops (see below). D was faster by 2 order of magnitude! Bearing in mind that Python is really used in computational chemistry/bioinformatics, I am sure D can be a good option in this field. In the modern strategy for the computational software python is used as a glue language and the number crunching parts are usually written in Fortran or C/C++. Apparently, with D one language can be used to write the entire code. Please, also look at this article: http://www.worldcomp-proceedings.com/proc/p2012/PDP3426.pdf Also, I wander about the results of this internship: http://forum.dlang.org/post/laha9j$pc$1@digitalmars.com With kind regards, Yury Python: #!/usr/bin/python import sys, string, os, glob, random from math import * a = 0 l = 1000 for i in range(l): for j in range(l): for m in range(l): a = a +i*i*0.7+j*j*0.8+m*m*0.9 print a D: import std.stdio; // command line argument import std.getopt; import std.string; import std.array; import std.conv; import std.math; // main program starts here void main(string[] args) { int l = 1000; double a = 0; for (auto i=0;i<l;i++){ for (auto j=0;j<l;j++) { for (auto m=0;m<l;m++) { a = a + i*i*0.7+j*j*0.8+m*m*0.9; } } } writeln(a); } |
August 16, 2015 Re: D for project in computational chemistry | ||||
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Posted in reply to Yura | On 17/08/2015 1:11 a.m., Yura wrote:
> Good afternoon, gentlemen,
>
> just want to describe my very limited experience. I have re-written
> about half of my Python code into D. I got it faster by 6 times. This is
> a good news.
>
> However, I was amazed by performance of D vs Python for following simple
> nested loops (see below). D was faster by 2 order of magnitude!
>
> Bearing in mind that Python is really used in computational
> chemistry/bioinformatics, I am sure D can be a good option in this
> field. In the modern strategy for the computational software python is
> used as a glue language and the number crunching parts are usually
> written in Fortran or C/C++. Apparently, with D one language can be used
> to write the entire code. Please, also look at this article:
>
> http://www.worldcomp-proceedings.com/proc/p2012/PDP3426.pdf
>
> Also, I wander about the results of this internship:
>
> http://forum.dlang.org/post/laha9j$pc$1@digitalmars.com
>
> With kind regards,
> Yury
>
>
> Python:
>
> #!/usr/bin/python
> import sys, string, os, glob, random
> from math import *
>
> a = 0
>
> l = 1000
>
> for i in range(l):
> for j in range(l):
> for m in range(l):
> a = a +i*i*0.7+j*j*0.8+m*m*0.9
>
> print a
>
> D:
>
> import std.stdio;
> // command line argument
> import std.getopt;
> import std.string;
> import std.array;
> import std.conv;
> import std.math;
>
> // main program starts here
> void main(string[] args) {
>
>
> int l = 1000;
> double a = 0;
> for (auto i=0;i<l;i++){
> for (auto j=0;j<l;j++) {
> for (auto m=0;m<l;m++) {
> a = a + i*i*0.7+j*j*0.8+m*m*0.9;
> }
>
> }
> }
> writeln(a);
> }
Any chance for when you get the time/content, to create a research paper using your use case?
It would be amazing publicity and even more so to get it published!
Otherwise, we could always do with another user story :)
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August 16, 2015 Re: D for project in computational chemistry | ||||
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Posted in reply to Yura | On Sunday, 16 August 2015 at 13:11:12 UTC, Yura wrote:
> Good afternoon, gentlemen,
>
> just want to describe my very limited experience. I have re-written about half of my Python code into D. I got it faster by 6 times. This is a good news.
>
> However, I was amazed by performance of D vs Python for following simple nested loops (see below). D was faster by 2 order of magnitude!
>
> Bearing in mind that Python is really used in computational chemistry/bioinformatics, I am sure D can be a good option in this field. In the modern strategy for the computational software python is used as a glue language and the number crunching parts are usually written in Fortran or C/C++. Apparently, with D one language can be used to write the entire code. Please, also look at this article:
>
> http://www.worldcomp-proceedings.com/proc/p2012/PDP3426.pdf
>
> Also, I wander about the results of this internship:
>
> http://forum.dlang.org/post/laha9j$pc$1@digitalmars.com
>
> With kind regards,
> Yury
>
>
> Python:
>
> #!/usr/bin/python
> import sys, string, os, glob, random
> from math import *
>
> a = 0
>
> l = 1000
>
> for i in range(l):
> for j in range(l):
> for m in range(l):
> a = a +i*i*0.7+j*j*0.8+m*m*0.9
>
> print a
>
> D:
>
> import std.stdio;
> // command line argument
> import std.getopt;
> import std.string;
> import std.array;
> import std.conv;
> import std.math;
>
> // main program starts here
> void main(string[] args) {
>
>
> int l = 1000;
> double a = 0;
> for (auto i=0;i<l;i++){
> for (auto j=0;j<l;j++) {
> for (auto m=0;m<l;m++) {
> a = a + i*i*0.7+j*j*0.8+m*m*0.9;
> }
>
> }
> }
> writeln(a);
> }
Initially I thought the Python version is so slow because it uses `range` instead of `xrange`, but I tried them both and they both take about the same, so I guess the Python JIT(or even interpreter!) can optimize these allocations away.
BTW - if you want to iterate over a range of numbers in D, you can use a foreach loop:
foreach (i; 0 .. l) {
foreach (j; 0 .. l) {
foreach (m; 0 .. l) {
a = a + i * i * 0.7 + j * j * 0.8 + m * m * 0.9;
}
}
}
Or, to make it look more like the Python version, you can iterate over a range-returning function:
import std.range : iota;
foreach (i; iota(l)) {
foreach (j; iota(l)) {
foreach (m; iota(l)) {
a = a + i * i * 0.7 + j * j * 0.8 + m * m * 0.9;
}
}
}
There are also functions for building ranges from other ranges:
import std.algorithm : cartesianProduct;
import std.range : iota;
foreach (i, j, m; cartesianProduct(iota(l), iota(l), iota(l))) {
a = a + i * i * 0.7 + j * j * 0.8 + m * m * 0.9;
}
Keep in mind though that using these functions, while making the code more readable(to those with some experience in D, at least), is bad for performance - for my first version I got about 5 seconds when building with DMD in debug mode, while for the last version I get 13 seconds when building with LDC in release mode.
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August 17, 2015 Re: D for project in computational chemistry | ||||
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Posted in reply to Yura | On Sunday, 16 August 2015 at 13:11:12 UTC, Yura wrote:
>
>
> Python:
>
> #!/usr/bin/python
> import sys, string, os, glob, random
> from math import *
>
> a = 0
>
> l = 1000
>
> for i in range(l):
> for j in range(l):
> for m in range(l):
> a = a +i*i*0.7+j*j*0.8+m*m*0.9
>
> print a
>
While starting over with D might make a better framework for going forward, it might be less work than you think to speed-up your existing Python code base. Loops in Python are notoriously slow. The code you're using seems like a classic example of something that could be sped up. You could write the slow parts in C with Cython. Alternately, you could play with Numba's @jit.
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August 17, 2015 Re: D for project in computational chemistry | ||||
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Posted in reply to Idan Arye | On Sunday, 16 August 2015 at 13:59:33 UTC, Idan Arye wrote:
> Initially I thought the Python version is so slow because it uses `range` instead of `xrange`, but I tried them both and they both take about the same, so I guess the Python JIT(or even interpreter!) can optimize these allocations away.
>
> BTW - if you want to iterate over a range of numbers in D, you can use a foreach loop:
>
> foreach (i; 0 .. l) {
> foreach (j; 0 .. l) {
> foreach (m; 0 .. l) {
> a = a + i * i * 0.7 + j * j * 0.8 + m * m * 0.9;
> }
>
> }
> }
>
> Or, to make it look more like the Python version, you can iterate over a range-returning function:
>
> import std.range : iota;
> foreach (i; iota(l)) {
> foreach (j; iota(l)) {
> foreach (m; iota(l)) {
> a = a + i * i * 0.7 + j * j * 0.8 + m * m * 0.9;
> }
>
> }
> }
>
> There are also functions for building ranges from other ranges:
>
> import std.algorithm : cartesianProduct;
> import std.range : iota;
> foreach (i, j, m; cartesianProduct(iota(l), iota(l), iota(l))) {
> a = a + i * i * 0.7 + j * j * 0.8 + m * m * 0.9;
> }
>
> Keep in mind though that using these functions, while making the code more readable(to those with some experience in D, at least), is bad for performance - for my first version I got about 5 seconds when building with DMD in debug mode, while for the last version I get 13 seconds when building with LDC in release mode.
There is a new implementation of cartesianProduct that makes the performance difference disappear for me with ldc and dmd. It's not in ldc's phobos yet so I had to copy it manually, but hopefully it will be in the next release.
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