Tag Archives: Scientific computing

Audio Toolkit: ATK SD1 Implementation

I just released my SD1 simulation, and now it is time to explain why is inside this plugin. Not everything in the original pedal was simulated, and different schemes were used to tackle the different stages.

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Announcement: ATK SD1 1.0.0

I’m happy to announce the release of my third audio plugin, the first based on the Audio Toolkit. It is available on Windows and OS X in different formats.

The UI was designed by Florent Keller, many thanks to him!

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Announcement: Audio TK 0.2.0

It’s time for a new release of the toolkit. Much has been done in terms of basic filters, but also to simplify usage (static and shared libraries are compiled, no need to reset the pipeline before calling process…).

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Audio Toolkit: Optimization of the overdrive simulation

I’ve explained in earlier posts how to simulate a simple overdrive circuit. I’ve also explained how I implemented this in QtVST (and yes, I should have added labels on those images!), which was more or less the predecessor of Audio TK.

The main problem with simulating non linear circuits is that it costs a lot. Let’s see if I can improve the timings a little bit.

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Announcement: Audio TK 0.1.0

A long time ago, I started implementing audio filters with a Qt GUI. I also started other pet projects in the same area, but I didn’t have a proper audio support library in C++ for that. Also Qt plugins are no longer an option (for me), I still hope to implement new filters in the future.

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On the importance of not reinventing the wheel in distributed applications

Sometimes, it’s so easy to rewrite some existing code because it doesn’t fit exactly your bill.

I just so the example with an All To All communication that was written by hand. The goal was to share how many elements would be sent from one MPI process to another, and these elements were stored on one process in different structure instances, one for each MPI process. So in the end, you had n structures on each of the n MPI processes.

The MPI_Alltoall cannot map directly to this scattered structure, so it sounds fair to assume that using MPI_Isend and MPI_Irecv would be simpler to implement. The issue is that this pattern uses buffers on each process for each other process it will send values to or receive values from. A lot of MPI library allocate their buffer when needed, but will never let go of the memory until the end. So you end up with a memory consumption that doesn’t scale. In my case, when using more than 1000 cores, the MPI library uses more than 1GB per MPI process when it hits these calls, just for these additional hidden buffers. This is just no manageable.

Now, if you use MPI_Alltoall, two things happen:

  • there are no additional buffer allocated, so this scales nicely when you increase the number of cores
  • it is actually faster than your custom implementation

Now with MPI 3 standard having non-blocking collective operations, there is absolutely no reason to try to outsmart the library when you need a collective operation. It has heuristics when it knows that it is doing a collective call, so let them work. You won’t be smarter if you try, but you will if you use them.

In my case, the code to retrieve all values and store them in an intermediate buffer was smaller that the one with the Isend/Irecv.

Book review: Learning scikit-learn – Machine Learning in Python

It seems that Packt Publishing is on a publishing spree on Machine Learning in Python. After Building Machine Learning Systems In Python for which I was technical reviewer, Packt published Learning Scikit-Learn In Python last November.

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Annoucement: scikits.optimization 0.3

I’m please to announce a new version for scikits.optimization. The main focus of this iteration was to finish usual unconstrained optimization algorithms.


  • Fixes on the Simplex state implementation
  • Added several Quasi-Newton steps (BFGS, rank 1 update…)

The scikit can be installed with pip/easy_install or downloaded from PyPI

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