My $300 Home Cloud Server: A Story of Blood, Sweat, and eBay

“I really wish I had a dedicated Linux computer to run computer vision algorithms on,” said my fiancée a couple of weeks ago. If you were there you would have been blinded by the metaphorical light bulb that lit over my head. You see, just the week before, my friend and co-worker had ordered an old, decommissioned (complete with “non-classified” stickers!) Apple Xserve off of eBay for merely $40. Like my fiancée, he wanted to have a machine for a special purpose: test compilations of open source software on a big-endian architecture. I was quite envious that he was able to hack on such cool hardware for such a cheap price. But, I wasn’t yet ready to bring out my wallet. I couldn’t justify indulging a new hobby without good reason—I was stuck waiting for just the right impetus. I didn’t wait long. My fiancée’s wish became my command!
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Parallel Programming through Dependence Analysis – Part I

 “As soon as an Analytical Engine exists, it will necessarily guide the future course of the science. Whenever any result is sought by its aid, the question will then arise — by what course of calculation can these results be arrived at by the machine in the shortest time?”

Charles Babbage (1864)

Points to Ponder

Would it not be wonderful, if we could write all our simulations as serial programs, and parallelized code (highly optimized for any given supercomputer) would be generated automatically by the compiler? Why is this not the case today? How come supercomputing centers require teams of highly trained developers to write simulations?


Scientists around the world develop mathematical models and write simulations to understand systems in nature. In many cases, simulation performance becomes an issue either as datasets (problem size) get larger, and/or when higher accuracy is required. In order to resolve the performance issues, parallel processing resources can be utilized. Since a large number of these simulations are developed using high level tools such as Matlab, Mathematica, Octave, etc., the obvious choice for the scientist is to use the parallel processing functions provided within the tool. A case in point is the parfor function in Matlab, which executes iterations of a for-loop in parallel. However, when an automation tool fails to parallelize a for-loop, it can be hard to understand why parallelization failed, and how one might change the code to help the tool with parallelization. Continue reading

Class participation incentive using e-tokens

For the past two years I was the lab assistant for the “Information Systems Design and Implementation — Programming in Java” course, taught by my PhD supervisor Prof. Diomidis Spinellis at the Athens University of Economics and Business. To make the lesson more interesting and give an extra motivation to the students, me, Vassilios Karakoidas and Diomidis decided to distribute e-tokens to the students that actively participated. In return, the students were offered the possibility to better their grades by the end of the semester. In this post I will describe how we did this and I will provide some initial results based on the students feedback.

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