Unrelated question, isn't it a bad idea to update the seed for every sample?
First, you are wasting cycles and with so little work to do before reseeding as in the code shown it probably matters quite a bit.
Second, some random number generators need some warm-up time producing lower quality random numbers at the beginning.
Third, if you are reseeding faster than your seed changes, you will repeatedly consume the same sequence of random numbers. I am not sure what the resolution of Now() is, but unless it is on the order of nanoseconds this will heavily affect the shown implementation.
If the resolution is one millisecond and it took 15 seconds to execute on a single thread, then the generated random values changed only every 74 iterations.
edit: Another issue is that I really dont like when people present speedup in %. How should 540% speedup be interpreted? It makes more sense as a ratio, so we find sequential/parallel = 10067483333/1583584841 ~= 6.36. So the parallel version achieves a speedup factor of 6.36.
So what distrinctiont are you trying to draw?
For Monte Carlo simulations, it's in fact very bad practice to continually reseed a computation, as it makes them unrepeatable.
Besides the author is only sampling the PRNG 1 million times, this is hardly enough to stumble upon any periodicity in a modern PRNG. I have absolutely no idea if the PRNG provided by Go is any good or what method it is based off of.