Added the cuda.multigpu package along with the implementation of the
LoadBalancer. Code has not been tested yet!
Renamed LoadBalancer to TransScale (Transparet Scalability)
TransScale works perfectly for 2 cards (Yaaaaaaaaay!)
Visualizer is broken and currently the visualization ability is broken
:(
Before attempting to halve the evaluation
CudaTrainingInstance
	Removed the "output" filed (won't be necessary when using TransScale)
	
CudaInterop:
	Now uses all the available devices at the same time for fitness
evaluation and filter calculation

Next step: change VisualizerKernel to use TransScale
Apparently multi-gpu works but produces invalid results.

Added KernelArgSetter to ensure the anonymous handlers don't produce any
side effects.
Classifier:
	Modifying the claims in now synchronized

GLVisualizerKernel:
	Now appropriately frees all objects

ClassifierSet:
	Now has the "resetClaims" method. This is necessary for a dual card
setup
	The getPointerToAll function no longer resets the claims

GLVisualizer:
	Uses both cards to run all classifiers on all segments.
	[ISSUE: will not draw overlay -- this will hopefully be fixed in the
next commit]
The whole system now uses TransScale for GPU interop
 
Fixes the serious bug with single claimer classifiers. If one segment is
uniquely identified
by more than 1 classifier, the older one is deleted

** NOTE: Do thresholding is always performed! (still have no idea how to
map an OpenGL buffer
to more than 1 card)
 
CudaEvolutionState:
	Now correctly reports the classifiers every "n" generations
	When reporting classifiers, they are cloned

Classifier:
	Now supports cloning

Segment:
	Each segment now holds a list of all the classifiers that have claimed
it
Removed TransScale and added it as a dependency library
TransScale is available here: https://github.com/Maghoumi/TransScale
Changed class names here and there because of the change in the name in
TransScale