One of my academic colleagues, Fumie Costen, recently asked members of The University of Manchester GPU Club the following question
‘I am looking for a journal with high impact factor where I can publish our work on GPU for FDTD computations. Can anyone help?’
So far, we haven’t had any replies so I’m throwing the question open to the world. Any suggestions?
I was recently working with someone who was running thousands of R jobs on our Condor pool and some of them were failing. As part of the diagnostic process, we needed to determine which machines in the pool were causing the problem. My solution was to add the line
hostname
To the Bash script that eventually called R and the program he was running. This gives output that looks like this
badmachine.ourdomain.ac.uk
His solution was to do exactly the same thing in R. To do this, he added the following line to the beginning of his R script
(Sys.info()["nodename"])
Which produces output that looks like
nodename "badmachine.ourdomain.ac.uk"
Either way, the job was done.
Welcome to my monthly round-up of news from the world of mathematical software. As always, thanks to all contributors. If you have any mathematical software news (releases, blog articles and son on), feel free to contact me. For details on how to follow Walking Randomly now that Google Reader has died, see this post.
Numerics for .NET
Faster, faster, FASTER!
Sparsification
Data and Statistics
Molten Mathematics
Some Freebies
Safe and reliable numerical computing
Pythonic Mathematics
I was recently chatting to a research group who were considering moving to Python from MATLAB for some of their research software output. One team member was very worried about Python’s use of indentation to denote blocks of code and wondered if braces would ever find their way into the language? Another team member pointed out that this was extremely unlikely and invited us to attempt to import braces from the __future__ module.
>>> from __future__ import braces File "", line 1 SyntaxError: not a chance
Welcome to the latest Month of Math Software where I look back over May 2013 and pick out items of interest in the world of mathematical software. As always, thanks to everyone who contributed news items this month– I couldn’t do these posts without you. Feel free to contact me if you have any news you’d like to share in future editions.
Connecting MATLAB with Mathematica
Octave news
GNU Octave is an open source system for numerical computing that is broadly compatible with MATLAB.
Numerical Algorithms Group
Maple’s 25th Birthday
Linear Algebra
Free Stuff
Optimization
Finite Elements and PDEs
GPU Accelerated Mathematics
Statistics
From the blogs
Earlier this year I was awarded a fellowship from the software sustainability institute, an organization that works to improve all aspects of research software. During their recent collaborations workshop in Oxford, it occurred to me that I was aware of only a relatively tiny number of software projects at my own institution, The University of Manchester. I decided to change that and started contacting our researchers to see what software they had released freely to the world as part of their research activities.
Research software comes in many forms; from small but useful MATLAB, Python or R scripts with just a handful of users and one developer right through to fully-fledged applications used by large communities of researchers and supported by teams of specialist developers. I’m interested in knowing about all of it. After all, we live in a time when even a mistake in an Excel spreadsheet can change the world.
The list below is what’s been sent to me so far and is a mirror of an internal list that’s been doing the rounds at Manchester. I’ll update it as more information becomes available. If you are at Manchester and know of a project that I’ve missed, feel free to contact me.
Last updated: 26th Jan 2015
Centre for Imaging Sciences
Faculty of Life Sciences
Health Informatics
IT Services
Manchester Institute of Biotechnology
School of Chemical Engineering & Analytical Science
School of Chemistry
School of Earth, Atmospheric and Environmental Sciences
School of Electrical and Electronic Engineering
School of Mathematics
School of Mechanical, Aerospace and Civil Engineering (MACE)
School of Physics and Astronomy
I recently picked up a few control theory books from the University library to support a project I am involved with right now and was interested in the seemingly total dominance of MATLAB in this subject area. Since I’m not an expert in control systems, I’m not sure if this is because MATLAB is genuinely the best tool for the job or if it’s simply because it’s been around for a very long time and so has become entrenched. Comments from anyone who works in relevant fields would be most welcome.
On its own, MATLAB is insufficient to teach introductory control systems courses — you also need the control systems toolbox as a bare minimum but most books and courses also seem to require Simulink and the symbolic math toolbox. All of these are included in the student edition of MATLAB which is very reasonably priced.
If you are not a registered student, however, and don’t work for someone who can provide you with MATLAB it’s going to be very expensive! As far as I can tell, your only option would be to purchase commercial licenses which are very expensive (as in thousands of dollars/pounds for MATLAB and a few toolboxes).
What else is out there?
I have a strong interest in mathematical software and so I know that there are several products that have support for control theory. Here are some that I know of and have access to myself
I haven’t mentioned Simulink alternatives in this post since I’ve discussed them before.
Questions
Some questions that arise are
I find that the final point is very divisive among people I discuss it with. On the one hand you have those who say ‘It’s the concepts that matter, the language you choose to implement them in is much less important’ and on the other hand you have those who say ‘It’s gotta be MATLAB, my father used MATLAB and his grandfather before him. Industry uses MATLAB, I only know MATLAB, we must teach MATLAB.’
As I type this, the sun is shining (finally!) and the skies are blue. You’d think that it would be difficult to concentrate on writing this Month’s mathematical software round-up but it has been such an interesting month that it turned out to be a breeze. Thanks to everyone who submitted news items for this month’s review, your feedback and generosity is greatly appreciated–I would have given up long ago without it.
If you have any news items for next month’s issue, please let me know via the usual channels. Click here for the Month of Math Software Archives.
Things that are a bit like MATLAB
Things written for MATLAB
Spreadsheets
Graphs and Plotting
Numerical libraries
Python
It’s been a big month for mathematical and scientific software in Python with several releases of note.
Misc
From the blogs
I was recently working on some MATLAB code with Manchester University’s David McCormick. Buried deep within this code was a function that was called many,many times…taking up a significant amount of overall run time. We managed to speed up an important part of this function by almost a factor of two (on his machine) simply by inserting two brackets….a new personal record in overall application performance improvement per number of keystrokes.
The code in question is hugely complex, but the trick we used is really very simple. Consider the following MATLAB code
>> a=rand(4000); >> c=12.3; >> tic;res1=c*a*a';toc Elapsed time is 1.472930 seconds.
With the insertion of just two brackets, this runs quite a bit faster on my Ivy Bridge quad-core desktop.
>> tic;res2=c*(a*a');toc Elapsed time is 0.907086 seconds.
So, what’s going on? Well, we think that in the first version of the code, MATLAB first calculates c*a to form a temporary matrix (let’s call it temp here) and then goes on to find temp*a’. However, in the second version, we think that MATLAB calculates a*a’ first and in doing so it takes advantage of the fact that the result of multiplying a matrix by its transpose will be symmetric which is where we get the speedup.
Another demonstration of this phenomena can be seen as follows
>> a=rand(4000); >> b=rand(4000); >> tic;a*a';toc Elapsed time is 0.887524 seconds. >> tic;a*b;toc Elapsed time is 1.473208 seconds. >> tic;b*b';toc Elapsed time is 0.966085 seconds.
Note that the symmetric matrix-matrix multiplications are faster than the general, non-symmetric one.
A friend of mine recently got hold of a Microsoft Surface Pro tablet and he let me have a play on it for a couple of hours. So, I installed Mathematica 9 and ran the benchmark. A screenshot of the result is below with the Surface’s result in blue. Not bad for a tablet!
Touch controlled Manipulates were a lot of fun too. If only I could run such things on my iPad as appeared to be promised in http://blog.wolfram.com/2012/02/17/a-preview-of-cdf-on-ipad/
My only other comment is that the Touch Cover is truly awful, reminding me of ye-olde ZX81, but I’ve been told that the Type Cover is much better
