Final project for a discrete structures and algorithms course. Fun to compare then and now, and it's pretty fast!
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DISCLAIMER: this code dates to 2008-12-20, right before/during term finals. It
predates my discovery of things like version control, style guides, and false

I love it anyway. It ran pretty damn well (many iterations of optimization got
it within ~1 order of magnitude of the best solver I found online) and I now
find the false sharing pitfall hilarious. I implemented the naive parallelization literally several hours before the deadline, and couldn't get a reliable benchmark on the shared machine.

Unedited, rambling, sleep deprived notes and terr^H^H^H^Hawesome code follows!


CPU information:
	cat proc/cpuinfo
Page size:
	getconf PAGESIZE


+parallel processing to make use of two CPUs
	UPDATE: implemented, code is sloppy but it's a robust algorithm
		that's hard to screw up. time utility shows user time is
		higher than real time - I take it that means it's working
		and work is being split across two CPUs. Can't judge
		increase in efficiency though, since people are running
		jobs non-stop which throws off the results.
	splitting top50000 in half and running two processes
		109.8s + 115.6s reported by each
		130s with one process: this is with stuff running in
		the background - what gives?
	I'd have to rearrange the sudata linked list into an array.
	mmap'd array, either to begin with (static array? convert 
		list to array after finishing the list?)
	then fork and have the child do odd problems while parent
		does even, or something like that
	alternatively: fork before reading mmap'd input file,
		parent only parses odd puzzles child only even ones
		problem: output would be out of order
	never tried using threads on Linux - thread pool is out of the
+thread priority setting (impossible needs root)
-'next bit' (rightmost 1 bit) lookup table
	will simplify a WIDTH-long for loop - worth it?
+refactor hidden singles function to use lookup tables
	(instead of three ugly simiarl-except-for one line blocks of code)


10-30x slower than many numbers mentioned by gsf on the forums :(
	29.9puzzles/s/GHz for gsftest.txt (335, 746, 1190 exist)
	175.3puzzles/s/GHz for top50000.txt (remember reading about 5k/s)
Reasons? Not enough lookup tables? Need proper block hidden single parsing?


	Wikipedia worst case for a simple backtracking algorithm, 9x9.
Starts off with unknown cells 9876543... leading to lots of backtracking.
I minimized the thread of puzzles like this one by rotating (well, flipping)
them as they're loaded from a file, such that the majority of their givens
are in the NW quadrant.
	The technique could be taken a lot further, with row/column rotations.
My search algorithm chooses cells to test nondeterministiaclly (linear) and
such transformations could have a similar effect to the deterministic column
choice in DLX.

	Supposed to mess with backtracking, singles/hidden singles only
solvers. Takes about a minute. 

	Supposedly more general - 2 minutes.

	Test specifics parts of program.

	Bad input. (For a given value of bad - total garbage WILL crash.)

  The program was originally written to solve mostly low-hint puzzles that
required a lot of guessing. For puzzles with many hints that are logic-solveable
a pre-processing step looks for simple hints.
  Algorithm choice was between simple but fast brute-force based on bitboards,
or DLX. Other, more obscure options were explored and rejected based on 
preliminary research.
  DLX is more general than a sudoku-specific brute force. It has lower
algorithmic complexity, but takes much longer per operation. For high values
of N it should prove superior - how high is hard to say. It's nifty in that
it handles basic sudoku logic reductions intrinsically.

  Speaking of fancier options: ssh-linux has a decent GPU and OpenGLs' 
extension scheme -probably- doesn't need root access to install shader support.
I seriously considered trying to do most of the processing on the GPU.  Solving
a bunch of sudokus is very parallel, and the since the solving algorithm 
basically traverses a search tree it's also easy to parallelize. Not to mention
subtasks like constraint checking being fairly independant of each other.
  However, closer examination proved disappoting. Floating point operations 
complicate things more than they help, bitboards being far superior. Hardware 
accelerated vector operations don't seem particularly useful. Branching is not 
suggested and is poorly supported on older video card models. Most interesting: 
operations are done on read only memory and outupt into write only memory. A
GPU algorithm would have to branch a puzzle into many possible copies, reduce
those copies to test validity, then recurse. Losing out on memory usage to gain
in parallelism and hopefully speed... An implementation of something like Knuth's
algorithm X might be doable. Instead of spending time covering/uncovering
like DLX does, just make specific copies.
  In short, definitely interesting, definitely has potential, probably not a good
way to solve Sudoku.
  Also, even the C-like shader language may be pushing the project's definition
of "in C or C++".