Loop not a SIMD opportunity because of the strided memory accesses in the loop body
Memory access pattern is very important for good software performance. The loop contains strided memory accesses. This type of memory access pattern is inefficient from the memory subsystem perspective, and these loops are typically not good SIMD opportunities.
Loops with strided memory accesses can be vectorized if the strided memory access is removed. This is typically the case when the strided memory access is inside a loop nest. In that case, techniques like loop interchange can help convert the inefficient strided memory access pattern into other, more efficient access patterns. Techniques like loop tiling can also make the dataset smaller and make vectorization beneficial even in the presence of strides.
Repacking the data to avoid the stride is also one of the ways to remove the stride. For example, instead of loop which writes to elements of the array
a[3 * i],
a[3 * i + 1] and
a[3 * i + 2], we introduce three separate arrays and write to them:
a3[i]. This kind of transformation however may require rewriting a large part of the code.
If the loop is part of the loop nest, consider applying loop interchange or loop tiling.