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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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.

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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.

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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.

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