Efficiency of various Ewald-type schemes for the summation of electrostatic interactions
I also unsuccessfully tried to look for such a paper in the past but below are some useful resources I've found. The only definitive answer I can give you is that traditional Ewald summation will be slower than a particle-mesh technique for any relevant system sizes (this is backed up in several of the references below). 

Chapter 4.9 of the GROMACS user manual has some very nice discussion on their various implemenations of long-range electrostatics and appropriate references.

The LAMMPS documentation also has quite a few references and some bits of helpful discussion.

You can also try digging through the AMBER manual which uses a PME implementation but I haven't read any of this myself.

Also here you can find implementation details for the PPPM method in HOOMD (helpful if you're interested in GPGPU implementations).

I think the most relevant bit of discussion is probably this excerpt in the GROMACS manual:


  The Particle-Particle Particle-Mesh methods of Hockney &amp; Eastwood can
  also be applied in GROMACS for the treatment of long range
  electrostatic interactions [99]. Although the P3M method was the ﬁrst
  efﬁcient long-range electrostatics method for molecular simulation,
  the smooth PME (SPME) method has largely replaced P3M as the method of
  choice in atomistic simulations. One performance disadvantage of the
  original P3M method was that it required 3 3D-FFT back transforms to
  obtain the forces on the particles. But this is not required for P3M
  and the forces can be derived through analytical differentiation of
  the potential, as done in PME. The resulting method is termed P3M-AD.
  The only remaining difference between P3M-AD and PME is the
  optimization of the lattice Green inﬂuence function for error
  minimization that P3M uses. However, in 2012 it has been shown that
  the SPME inﬂuence function can be modiﬁed to obtain P3M [100]. This
  means that the advantage of error minimization in P3M-AD can be used
  at the same computational cost and with the same code as PME, just by
  adding a few lines to modify the inﬂuence function. However, at
  optimal parameter setting the effect of error minimization in P3M-AD
  is less than 10%. P3M-AD does show large accuracy gains with
  interlaced (also known as staggered) grids, but that is not supported
  in GROMACS (yet).


If you're really looking to dive in, you can look at the source code for these packages (I believe you need a license for AMBER) to see the actual implementations.