Take a look at http://code.google.com/p/gpuocelot/ for more details

To Build Ocelot:
	./build.py --install

To Link Against Ocelot:
	g++ program.cu.cpp `OcelotConfig -l`

To Run Ocelot:
	Run your CUDA program normally with a 'configure.ocelot' file 
		in the same directory (see the website for samples)

Ocelot Version 2.0.969 Features:
    * PTX 2.2 and Fermi device support: Floating point results should be within
    	the ULP limits in the PTX ISA manual. Over 500 unit tests verify that
    	the behaviour matches NVIDIA devices.
    * Four target device types: A functional PTX emulator. A PTX to LLVM to
    	x86/ARM JIT. A PTX to CAL JIT for AMD devices (beta). A PTX to PTX JIT
    	for NVIDIA devices.
    * A full-featured PTX 2.2 IR: An analysis/optimization pass interface over
    	PTX (Control flow graph, dataflow graph, dominator/postdominator trees,
    	structured control tree). Optimizations can be plugged in as modules.
    * Correctness checking tools: A memory checker (detects unaligned and out
    	of bounds accesses). A race detector. An interactive debugger (allows
    	stepping through PTX instructions).
    * An instruction trace analyzer interface: Allows user-defined modules to
    	receive callbacks when PTX instructions are executed. Can be used to
    		compute metrics over applications or perform correctness checks.
    * A CUDA API frontend: Existing CUDA programs can be directly linked against
    	Ocelot. Device pointers can be shared across host threads. Multiple
    	devices can be controlled from the same host thread (cudaSetDevice can
    	be called multiple times).