Yes it is because every element can be processed independently and we are doing the same operation to every element.

But isn't this the same as (or very similar to) saxpy which did not achieve much speedup with additional processors?

@sareyan I think you're correct – this program will likely be bandwidth-bounded because our arithmetic intensity is so low. Increasing throughput will not help us compute any faster unless we have sufficient memory bandwidth!

What is the definition of a throughput-oriented parallel processor? Also what kind of modern processor would be a good option to run the above program?

I believe that although this program satisfies the 3 requirements on the previous slide (for a program to utilize modern parallel processors efficiently). But like other students have said, it could still subject to memory bandwidth limit (it needs frequent read / write to memory and have relatively simple arithmetic operations on the data loaded from memory) and therefore can't make utilize 100% of a modern parallel CPU/GPU without the support of high bandwidth memory components. More details and calculations are shown on slide 36.

Even though this would likely be bandwidth-bound on typical modern architectures as @superscalar says, I think it typically takes several CPU cores to saturate memory bandwidth with sequential access patterns these days. So I'd definitely expect this program to benefit from multi-core parallelism, let alone SIMD as well.

This program is memory bandwidth limited. Although multi-threading in the modern processor could hide stalls, I think there will still be significant stalls.

Why would this be bandwith bounded as a result of our arithmetic intensity?

@noelma, it's the ratio of arithmetic (operations running on data that is stored on volatile memory) to memory accesses. In this program, 3 out of the 4 instructions are memory-bound (load / store), which is why this program is bandwidth bounded.