Detecting Faces with Dlib from F#. IFSharp Notebook

The Choices I have experimented with OpenCV and Dlib face detection in my computer vision pipeline. Both work well, but the Dlib one worked better: it is more sensitive with (in my case) almost no false positives right out of the box! Dlib uses several HOG filters that account for profile as well as frontal … Continue reading Detecting Faces with Dlib from F#. IFSharp Notebook →

Zooming Through Euler Path: Supercharging with GPU

So, continuing where we left off: Walking the Euler Path: Intro Visualizing Graphs Walking the Euler Path: GPU for the Road Walking the Euler Path: PIN Cracking and DNA Sequencing For the Win And finally I ran the GPU-enabled algorithm for finding the Euler path. And the results: Generating euler graph: vertices = 10,485,760; avg … Continue reading Zooming Through Euler Path: Supercharging with GPU →

Visualizing Graphs

Previously Walking the Eule Path: Intro Generating and Visualizing Graphs I can hardly overemphasize the importance of visusalizations. Many a bug had been immediately spotted just by looking at a visual of a complex data structure. I therefore decided to add visuals to the project as soon as the DirectedGraph class was born. Code & … Continue reading Visualizing Graphs →

Walking the Euler Path: Intro

Source Code I'm thinking about a few posts in these series going very fast through the project. The source is on my GitHub, check out the tags since the master branch is still work in progress. Experimenting with Graph Algorithms with F# and GPU Graphs play their role in bioinformatics which is my favorite area … Continue reading Walking the Euler Path: Intro →

Look-and-say: [Alea.]CUDA

Continuing the Advent of Code theme from the previous post. Figured since this year is going to be my year of CUDA, this would be a good opportunity to take it for a ride. A good April 1st post, but why wait? So, how can we make this even faster than the already fast imperative … Continue reading Look-and-say: [Alea.]CUDA →

Non-linear Thinking with CUDA.

I love GPU programming for precisely this: it forces and enables you to think about a solution in a non-linear fashion in more than one sense of the word. The Problem Given a set $latex A = \{a_1, a_2 \ldots a_n\}$, output a set $latex S_A = \{0,\ \sum\limits_{k=1}^{n} a_k,\ \sum\limits_{k=i}^{i + j \mod n} … Continue reading Non-linear Thinking with CUDA. →

Modelling Stochastically Independent Processes with F# Computation Expressions: Part 1

The idea for doing this is not new. There is an excellent series of posts closely tracing an article on applications of functional programming to probability. A colleague of mine has recently called my attention to his own post of two years ago, where he describes a monad that models stochastically independent events in Clojure. … Continue reading Modelling Stochastically Independent Processes with F# Computation Expressions: Part 1 →

Generating Permutations: Clojure or F#: Part 2

Marching on from the last post. Lazy Sequences This is my favorite feature ever. If I want to generate just a few of 10! (nobody even knows how much that is) permutations, I could: provided, the function is defined (as described in the first post): Here I am not sure which language I like more. … Continue reading Generating Permutations: Clojure or F#: Part 2 →

Generating Permutations: Clojure or F#: Part 1

The Alogirthm Recently, I have entered a brave (new?) world of Clojure and was looking for a small project to take it for a ride. I stopped on a popular/interesting enough little problem, that subsumed a certain interview question which I was once unfortunate enough to stumble through with half my brain tied behind my … Continue reading Generating Permutations: Clojure or F#: Part 1 →

Computing Self-Organizing Maps in a Massively Parallel Way with CUDA. Part 2: Algorithms

In the previous post I spoke briefly about motivations for implementing self-organizing maps in F# using GPU with CUDA. I have finally been able to outperform a single threaded C++ implementation by a factor of about 1.5. This is quite modest, but on the other hand rather impressive since we started out by being 60 … Continue reading Computing Self-Organizing Maps in a Massively Parallel Way with CUDA. Part 2: Algorithms →