Added a new fast reading/writing engine to io.ascii

This PR adds a Cython/C parsing engine to the infrastructure of `io.ascii` so that new reading classes for simple formats (currently `FastBasic`, `FastTab`, `FastCsv`, `FastNoHeader`, `FastCommentedHeader`, and `FastRdb`) can opt to use this engine instead of the ordinary `BaseReader` inheritance model. The ordinary readers for these formats still remain to maintain flexibility and compatibility. The PR introduces a new parameter ``use_fast_reader`` in `ascii.read()` which can be used to enable or disable the fast engine (by default, `read()` will attempt to use the fast reader and fall back on the slow reader if parsing fails). The fast readers are also on the guess list directly in front of their slower counterparts.

There is also a Cython engine for writing with these formats; a similar parameter ``use_fast_writer`` can enable or disable this engine, and `ascii.write()` uses the engine by default when writing output in a compatible format.

Using randomly generated text files, it seems that the fast reading engine is anywhere from 3 to 7 times faster than the ordinary readers depending on the data type of table columns, while the fast writing engine is anywhere from ~2.5 to 12 times faster than the ordinary writers; both engines have less of a speed gain for floating-point data, which might be worth looking into at some point. [Here](http://amras1.github.io/astropy-benchmarks/#) are some before and after benchmarks, which cover a range of ASCII formats and compare Astropy's performance with that of numpy and Pandas. There is also a short design document [here](https://github.com/amras1/astropy-benchmarks/blob/master/design.md), which gives a quick overview of the parsing implementation in this PR.

Please feel free to make any suggestions or ask me anything.
My one concern about this class (and I haven't gone through it in detail) is that it seems like there are many areas where it doesn't take full advantage of being written in Cython as opposed to normal Python (other than being able to use the tokenizer easily without having to define a full Python interface to the tokenizer.

THAT SAID, even if that's the case, I don't think it should hold up merging.  I'm just pointing out that there are probably some rich opportunities to further optimization in here in a few places :)