I've seen this over and over. One of the main issues pointed out by TFA is that there's too many small tasks allocated for parallel execution. Rayon is not going to magically distribute your work perfectly, though it very often does a decent job.
If your algorithm is the equivalent of a couple of nested iterations, you have essentially three options: parallelize outer, inner, or both. In the vast majority of the cases I've run into, you want thread/task level parallelism on the outer loop (only), and if required, data/simd parallelism on the inner loop(s).
It's a rule of thumb, but it biases towards batches of work assigned to CPUs for a decent amount of time, allowing cache locality and pipelining to kick in. That's even before SIMD.
If your algorithm is the equivalent of a couple of nested iterations, you have essentially three options: parallelize outer, inner, or both. In the vast majority of the cases I've run into, you want thread/task level parallelism on the outer loop (only), and if required, data/simd parallelism on the inner loop(s).
It's a rule of thumb, but it biases towards batches of work assigned to CPUs for a decent amount of time, allowing cache locality and pipelining to kick in. That's even before SIMD.