Vectorized RNG
randomTensor uses the default rng from Nim stdlib which is xoroshiro128.

Through SIMD vectorization, it's possible to improve xoroshiro256 by 4X see https://github.com/JuliaLang/julia/issues/27614 which would be quite useful when initializing tensors of millions of elements.

See also http://prng.di.unimi.it/xoshiro256+-vect-speed.c and http://prng.di.unimi.it/

Note that the xoshiro family provides "jump sequence for parallel computation http://prng.di.unimi.it/xoshiro256plus.c