It has been a while, too long for sure, since my last update on this scikit. I’m pleased to announce that some algorithms are finally fixed as well as some tests.


  • Fixed Polytope/Simplex/Nelder-Mead
  • Fixed the Quadratic Hessian helper class

Additional tutorials will be available in the next weeks.

Old announces:

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Due to the end of the free lunch, manufacturers started to provide differents processing units and developers started to go parallel. It’s kind of back to the future, as accelerators existed before today (the x87 FPU started as a coprocessor, for instance). If those accelerators were integrated into the CPU, their instruction set were also.

Today’s accelerators are not there yet. The tools are not ready yet (code translators) and usual programming practices may not be adequate. All the ecosystem will evolve, accelerators will change (GPUs are the main trend, but they will be different in a few years), so what you will do today needs to be shaped with these changes in mind. How is it possible to do so? Is it even possible?

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This is the question I asked myself recently. If you write a scientific code in Fortran, you can expect a huge performance boost compared to the same program in C or C++. Well, unless you use some compiler extensions, in which case you get the same performance, or better.
Let’s try this on a 3D propagation sample, with a 8-points stencil.

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I came across the issue of how to teach a trainee how to write a parallel finite-difference time-domain (FDTD) method. There are a lot of books on the FDTD, but only a few on parallel ones. So I’ve decided to go for this book, knowing that some chapters won’t apply to our job (wave equations). My goal was to seek a book that would explain the basics of my issues.

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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.

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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.

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Today ships my first book on Python for the scientists. Although IT people can learn a lot of Python with it (mainly if they are working in labs are research centers), scientists will be more interested as it presents a viable alternative to Matlab : fast, efficient, a real language with a large standard library.

After an introduction, the Python language is exposed as well as some main modules. The three central chapters are dedicated to Numpy, Scipy and Matplotlib. Each library tackles a specific problem, storing data, using it or display it. Finally, the last chapter exposes ways of speeding up Python with the use of C or C++.

The link to my publisher : here