One of the fastest general[1] Life algorithms I know of is vlife, used by Adam P. Goucher in apgmera. It supports arbitrary (outer-totalistic) rules; the generic bits are written in C++ (and live in includes/vlife.h), while the rule-specific parts are in assembly, using SSE2/AVX1/AVX2 (whatever is available), generated by a Python script. It's a very clever algorithm using very clever data structures that probably achieves close to the maximum of what you can eke out of a modern CPU, speed-wise, at least without resorting to even newer instruction sets.

It includes a benchmark in which a well-known methuselah is run for 30k generations (about as long as it takes to stabilize). make vlifetest and run the resulting vlifetest binary to run it -- it would be interesting to compare this to your C++ and Perl implementations, as well as CPAN's offerings. On my machine (which is a couple of years old and only supports AVX1), the average time across the benchmark's 50 iterations is 135.72 ms:

$ ./vlifetest
VersaTile size: 336
Instruction set AVX1 supported.
Population count: 1623
Tiles processed: 596194
[...]
Lidka + 30k in 135.72 ms.
$ 

EDIT: on the same machine, tbench1.cpp takes about a minute to run Lidka to completion:

$ ./tbench1 lidka_106.lif 30000
cell count at start = 13
run benchmark for 30000 ticks
cell count at end = 1623
time taken 60 secs
$

The benchmark of the Perl implementation is still running.

EDIT 2: Perl ended up being faster than I thought, but at more than 40 minutes, it's still pretty slow compared to the C++ version, much less vlife:

$ perl tbench1.pl lidka_106.lif 30000
cell count at start = 13
run benchmark for 30000 ticks
cell count at end = 1623
time taken: 2441 secs
$ 

EDIT 3: apgmera has since seen a new major release (4.x), and the algorithmic guts now live in a separate repo, lifelib. The author informs me that it's actually about 8% faster compared to the vlife code in 3.x, too.

Footnote:

1. HashLife is a bit of a special case, obviously.