add option to compile nvcc's fatbin

Useful when compiling CUDA shaders on build server without NVidia GPU
(but with NVidia CUDA Toolkit) and specifiying target GPU architecture
with `options` argument.

Fatbin embeds prebuild for set of specified real architectures cubin's
and PTX assemblies for set of specified virtual architectures, allowing
driver to load prebuild cubin if there is any for current real GPU, or
to assemble PTX from closest virtual GPU architecture.

This can be used for distributing CUDA-powered applications without need
to install NVidia CUDA Toolkit (nvcc) and development environment on
target platform.

.cu files can be precompiled to fatbin's on build server with

    fatbin = pycuda.compiler.compile(
        cu_file_text,
        options=[
            "-gencode", "arch=compute_20,code=compute_20",
            "-gencode", "arch=compute_20,code=sm_20",
            "-gencode", "arch=compute_30,code=compute_30",
            "-gencode", "arch=compute_30,code=sm_30",
	],
        target="fatbin")

fatbin's can be distributed on machines without nvcc and loaded using

    cuda_module = pycuda.driver.module_from_buffer(fatbin)

add option to compile device-independent PTX files with pycuda.compiler.compile()

Port examples to Python 3.

Add async memset methods to driver, uncomment peer async copy method

Add support for unknown dimension in reshape

BUG: fix to demo_struct.py for recent CUDA 4.0+

wrap int32 and intp arguments to memcpy_htod in numpy.getbuffer() to avoid AttributeError.  fix prepare/prepared_call for CUDA v4.0+

Use appdirs package to create cache in XDG-correct location

This fixes #54.