Save the Planet: Kill Flash!

The browser-embedded Flash player (or a derivative) is the preferred small (and low quality) video delivery mechanism on the web-based Internet. While I completely understand the usefulness of such a player (or at least the amusement it procures), I can’t fathom why the hell it’s so resource hungry. That damn thing sucks an inordinate amount of CPU to play tiny videos! What’s wrong with this thing?

Wallpaper: Douceur retrouvée

Douceur retrouvée (1920×1200)

The CFM-00

Parallel computing is the next paradigm shift, everybody knows this, but not everyone is taking the proper action to face it adequately. One thing to do is to read on the subject and force oneself to code using threads and various degrees of parallelism; and that’s pretty easy now that a quad core machine doesn’t cost all that much. But the next step, distributed computing, necessitates, well, more than one machine, and if you have different levels of memories and communication channels, all the better.

So out of a bunch of old x86 PCs, I’ve decided to build my own portable mini-cluster with 8 nodes. Nothing all that impressive, but still plenty of fun to build.

Is Python Slow? (Part II)

In a previous post I expressed my worries about Python being excruciatingly slow and I used a toy problem to compare the speed of Python to programs in other several languages, including C.

Of course, all kind of people complained that I couldn’t compare a dynamic, interpreted language with static, compiled languages. First, let met tell you that I sure can. First, the goal was to measure speed, and not the effects of type system of the language (although logically correlated) nor the programming paradigm: the amount of CPU used to solve a given problem was the primary (if not only) point in interest.

But to be fair to Python, I extended the tests to other interpreted, dynamic languages, such as Lua, Perl, PHP and JavaScript. I also added Pascal and Haskell in the compiled languages groups.

Suggested Reading: Mrs. Perkins’s Electric Quilt

Paul J. Nahin — Mrs. Perkins’s Electric Quilt: And Other Intriguing Stories of Mathematical Physics — Princeton University Press, 2009, 391 pp. ISBN 978-0-691-13540-3

(Buy at Amazon.com)

In this book, you will discover the topic of mathematical physics—or physics mathematics, depending on how you look at it—through a series of counter-intuitive results (counter-intuitive for the non-physicist, that is). The author shows that with logic and (quite) a bit of mathematics, you can obtain surprising but correct results. A good part of the book rotates around the topic of gravity (boom, tss.) but also presents other topics such as air drag, partitionning squares into squares optimally, infinite resistor networks, and random walks. The narrative style is clear and simple; and while the mathematics in the book may seems scary at first, you still get the point; someone with just a little background in mathematics will still get the essential; someone with a better background in mathematics will get the best of it.

Another worthwhile note is that the typography of mathematical equations is simply exquisite; it is very well typeset. Something that is getting rare these days for a grand public book.

Soap Geometry

Sometimes, we look for relation between objects of different dimensionality, search for proportionality rules and try to factor away constants, or, at least, figure out what they are made of. A cute example of which came to me in the shower…

Knowing your weight, can you know how much soap you use?

Suggested Reading: Les sciences de l’imprécis

Abraham A. Moles — Les sciences de l’imprécis — Seuil, 1990, 310 pp. ISBN 2-02-011620-0

(out of print?)

“Thinking about the vague is not vague thinking” would quite succinctly and accurately describe Moles’ thesis. The terminology used will be a bit disconcerting to the computer scientist as the vocabulary comes from the social sciences rather than the “hard” sciences. At times we feel that analogies drawn between the author’s ideas and information theory (and computer science) are almost stretched but we can quite forgive this since it nevertheless remains a very clear exposition of his thesis, that is, imprecision is not to be frown upon and is quite necessary to science and as such should be mastered rather than feared.

The text is almost grand public as it contains essential no maths.

Data Insecurity

It always amazes me to see how people put trust in their service providers. While in principle, there’s no real need to worry, careless implementation of services can really have dire consequences!

And it’s not like leaks and exploits are rare. Sometimes we hear about them, sometimes we don’t. Let’s consider these two (amongst those) I know about:

Suggested Reading: The Common Sense of Science

Jacob Bronowski — The Common Sense of Science — Harvard University Press, 1978, 154 pp. ISBN 0-674-14651-4

(Buy at Amazon.com)

I already knew Bronowski by the television series The Ascent of Man (broadcasted in french in the late 70s or maybe the very early 80s by Radio-Québec, now TéléQuébec). Even as a child, I was impressed by the depth of discourse of the series. Universal thinker, in The Common Sense of Science, Bronowski tells us how he conceives science and its methods as a fundamental human activity, and why it plays such an important (if misunderstood) rôle in our society. The narration follows more or less the evolution of science since the Enlightenment to our time and how it is tied to the industrial revolution.

A matter of interpretation

In calculus 101, amongst the first things we learn, is that the derivative a function is the slope of the tangent to the function, that is, the instantaneous slope at some point on the function. We have, for some function that the derivative is given by:

So the formulation looks like a slope, and it is taught that it is a slope as well; all the concepts surrounding differentiation are expressed in terms of slopes of tangents, and that’s OK, because that’s what they are.

But suddenly, in calculus 201, we learn how to find the anti-derivative of a function, also known as the integral. But the metaphor changes completely: we’re know talking about the area under the curve. Wait. What?