  We present a comprehensive study of the applications of the pixel
color-magnitude diagram (pCMD) technique for measuring star formation histories
(SFHs) and other stellar population parameters of galaxies, and demonstrate
that the technique can also constrain distances. SFHs have previously been
measured through either the modeling of resolved-star CMDs or of
integrated-light SEDs, yet neither approach can easily be applied to galaxies
in the "semi-resolved regime". The pCMD technique has previously been shown to
have the potential to measure stellar populations and star formation histories
in semi-resolved galaxies. Here we present Pixel Color-Magnitude Diagrams with
Python (pcmdpy), a GPU-accelerated package that makes significant computational
improvements to the original code and including more realistic physical models.
These advances include the simultaneous fitting of distance, modeling a
Gaussian metallicity-distribution function, and an observationally-motivated
dust model. GPU-acceleration allows these more realistic models to be fit
roughly 7x faster than the simpler models in the original code. We present
results from a suite of mock tests, showing that with proper model assumptions,
the code can simultaneously recover SFH, [Fe/H], distance, and dust extinction.
Our results suggest the code, applied to observations with HST-like resolution,
should constrain these properties with high precision within 10 Mpc and can be
applied to systems out to as far as 100 Mpc. pCMDs open a new window to
studying the stellar populations of many galaxies that cannot be readily
studied through other means.
