I’m a very curious guy, and I wanted to know who is looking at my blog, and for my wife, who is interested by what is viewed on her decoration site (in construction as she wants to make a living of decoration advice). With my hosting service, I have access to Awstats, but Google Analytics seems better suited for data analysis. And this is what this book explains.
I chose Eclipse as my new Linux IDE, instead of Konqueror + KWrite. The purpose was to be able to launch a SCons build from the IDE, get the errors in a panel and double-clicking on one of them would direct me to the location of the error.
So Eclipse seemed to fit my needs:
- Plug-ins to add the support of various languages
- Support of different construction tools
- Support from the main C/C++/Fortran compiler developers (GNU, Intel, IBM, …)
So I will know show you two ways of enabling SCons support for Eclipse.
When I started my new job three months ago, I didn’t know how to write a Fortran program. I had to modify an already existign Fortran 77 program to enhance and parallelize it. So I went to the library and I took this book aimed at people like me.
I had this discussion with one of my Ph.D. advisors some months ago when we talked about correctly using the computers we had then (dual cores), and I had almost the same one in my new job here: applied maths (finite differences, signal processing, …) graduate students are not taught how to use current computers, so how could they develop an HPC program correctly?
I think it goes even further than that, and it will be a part of this post. What I see is that trainees and newly-hired people (to some extent myself included) lack a lot of basic Computer Science knowledge, and even IT knowledge.
The book description was really appetizing: Machine Learning applied to the Internet, so it should be easy to understand, and Python as the mean to compute. Unfortunately, contrary to what I saw in different reviews, I was not pleased with the book, and here is why.
This is the first time I will review a book on something I’m not familiar with at all. I’ve started now for more than two months a new job related to geophysics, and I had to catch up with my colleagues.
I’ve stopped studying geology ten years ago, so this is a review from someone who is learning geophysics and who wants to have a quick and global look on the different fields of geophysics.
Sometimes, a C or C++ array structure must be used in Python, and it’s always better to be able to use the underlying array to do some Numpy computations. To that purpose, Numpy proposes the array interface.
I will now expose an efficient way to use SWIG to generate the array interface and exposing the __array_struct__ property.
I’m trying to use the MKL with some programs and libraries, but I encountered something really strange and I’m not alone.
First, what is i_free ? Accoding to Intel, it’s their way to handle memory allocation and deallocation. They are only pointers to the actual memory functions so as to let the user decide if he wants a custom memory handler. Since 10.0.3, Intel changed their model, and the trouble begins.
As I have to parellize some programs developed in my new lab, I monitor CPU usage during thier execution. I do not usually need MPI to optimize them (although sometimes it is needed), only OpenMP, which means I can track /proc/ to get CPU and instantaneously memory usages.
So I wrote a small script that can be used by anyone for this purpose. I’ll explain how it works now.
For four years I’ve been researching the intersting field of manifold learning. Indeed, I’ve defended my PhD thesis last friday and now I have a PhD in electronics, electrotechnics and automatism in the university Louis Pasteur in Strasbourg, France. Those years after my engineering school allowed me to read and to learn a lot, but I’m still glad those years are over.
During these years, I enjoyed looking for new solutions for my problems, and it helped me discover some technologies I didn’t really master (like Python but also C++, parallel computing, … now I can use some of them safely). So this is my advice to every new PhD student: read a lot, and not only articles or books in your field, but also in the other fields, like the tools you may use (Computer Science mainly) so that you can use the best of them (it takes some time, but you’ll be rewarded in the end).
And before I forget: read http://phdcomics.com/ (my favourite character being Mike, although since he’s doing a post-doc, we don’t see him much :().