Timing data comparing CClasp to C++, SBCL and Python

Originally posted on drmeister:

Work on CClasp (Clasp using Robert Strandh’s Cleavir compiler) is moving forward, here is some timing data that I generated comparing CClasp performance to C++, SBCL and Python.

NOTE: this test is a specific test of an algorithm that uses FIXNUM arithmetic. I have inlined simple FIXNUM arithmetic (+, -, <, =, >, and fixnump) and so these operations are fast. Code that uses other functions will run a lot slower until inlining is implemented more broadly.

I’m calculating the 78th Fibonacci number 10,000,000 times in each case. For these integer arithmetic heavy functions, CClasp performs pretty well (~4x slower than C++). Once type inference is added as well as a few other optimizations CClasp should be generating performant code.

Note: There are compiler settings (loop unrolling) where the C code runs even faster than SBCL, it’s just for this specific test, with the compiler settings below that SBCL…

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Gabor Filters : A Practical Overview

Originally posted on Computer Vision Tutorials:

In this tutorial, we shall discuss Gabor filters, a classic technique, from a practical perspective.

Do not panic on seeing the equation that follows. It has been included here as a mere formality.


In the realms of image processing and computer vision, Gabor filters are generally used in texture analysis, edge detection, feature extraction, disparity estimation (in stereo vision), etc. Gabor filters are special classes of bandpass filters, i.e., they allow a certain ‘band’ of frequencies and reject the others.

In the course of this tutorial, we shall first discuss the essential results that we obtain when Gabor filters are applied on images. Then we move on to discuss the different parameters that control the output of the filter. This tutorial is aimed at delivering a practical overview of Gabor filters; hence, theoretical treatment is omitted (a tutorial that provides the essential theoretical rigor is currently in the pipeline).


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