OpenMP or PPL do no such thing as being pessimistic. They just do as they are told, however there's some things you should take into consideration when you do try to paralellize loops.
Without seeing how you implemented these things, it's hard to say what the real cause may be.
Also if the operations in each iteration have some dependency on any other iterations in the same loop, then this will create contention, which will slow things down. You haven't shown what your some_operation
function actually does, so it's hard to tell if there is data dependencies.
A loop that can be truly parallelized has to be able to have each iteration run totally independent of all other iterations, with no shared memory being accessed in any of the iterations. So preferably, you'd write stuff to local variables and then copy at the end.
Not all loops can be parallelized, it is very dependent on the type of work being done.
For example, something that is good for parallelizing is work being done on each pixel of a screen buffer. Each pixel is totally independent from all other pixels, and therefore, a thread can take one iteration of a loop and do the work without needing to be held up waiting for shared memory or data dependencies within the loop between iterations.
Also, if you have a contiguous array, this array may be partly in a cache line, and if you are editing element 5 in thread A and then changing element 6 in thread B, you may get cache contention, which will also slow down things, as these would be residing in the same cache line. A phenomenon known as false sharing.
There is many aspects to think about when doing loop parallelization.