For the last few years (since 2009), I’ve been pitching the idea to my peers that language speed & concurrency/parallel capabilities will become more important as CPU clock speeds plateau and manufacturers add more CPU cores instead of advancing clock rates. My 2+ year old Macbook Pro has 4 cores and 8 hyperthreads.
I stumbled upon a programming challenge a company was using for recruitment purposes and thought I’d create a Haskell solution as a learning exercise. The first problem was to find the longest palindrome embedded in a text string.
I first wrote a program to solve the Cracker Barrel peg board puzzle (15 holes arranged in a triangle with 14 golf tees) many years ago as youth using the BASIC language. I wish I still had the source to that, because I’m pretty sure this Haskell version would kick its butt :)
As I explain in 2009 Programming Language Plan, I’ve been in the process of evaluating programming languages to determine their suitability for use in my work. I’ve been proceeding on two fronts – statically typed functional programming languages and the venerated Lisp family.
I first became interested in functional programming when I was exposed to Python, Ruby & JavaScript a number of years ago. Since then I’ve looked into Arc, Clojure, Common Lisp, Haskell, Logo, ML & Scheme. I haven’t yet determined whether I’ll be more productive in any of them than I am with Ruby for developing web applications, but I do find them quite interesting.
One of the two parallel tracks in my 2009 Programming Language Plan begins with the Standard ML programming language, so it’s time to get started.
Update 11/23/2020: The ultimate winner for my primary programming language was Racket.
The 2008 Programming Language Plan didn’t go as well as I hoped, so I’m regrouping for another go at it. I did make progress learning some Logo and teaching it to my daughters, and I worked through seven chapters of “Programming in Haskell” which was very enjoyable, but I also spent way too much time trying to decide which language(s) to learn without actually learning them.
I recently obtained a mobile broadband device that has a built in GPS receiver and can emit NMEA sentences. My old Garmin portable GPS can emit NMEA also, but it’s a pain to hookup to the laptop. Combining a GPS unit in a mobile broadband device is a great idea.
Ruby is a very flexible and expressive language. A recent question posted by a Ruby newbie got me looking through my IRC logs for a discussion about the performance of various dynamic method invocation approaches, so I thought I’d share some performance results.
Update 10/16/2015: Please see the Racket Version also.
Peter Norvig wrote a simple spelling corrector in 20 lines of Python 2.5, so I thought I’d see what it looks like in Ruby. Here are some areas I’m not pleased with: