# Memory Bandwidth¶

Consider the following loop:

Example

do i = 1, n
do j = 1, m
c = c + a(i) * b(j)
end do
end do


CPUs perform arithmetic by reading an item from each of two registers, combining them in some way (eg by adding) and putting the result into a third register. There are only a few registers in the CPU so the above loop is implemented something like:

fetch c -> r1
(loop)
fetch a(i) -> r2
(loop)
fetch b(j) -> r3
mul r2, r3 -> r4
(repeat)
store r1 -> c


If a, b and c are double precision numbers - ie 8 bytes each - then we must fetch 8 bytes for each multiply, add pair: we have an operational intensity 2/8 = 1/4.

If we vectorize the loop, then with KNL AVX-512 we can do 8 loop iterations simultaneously. But we need to load 8 values for b, so our operational intensity is still 16/64 = 1/4. Overall we will read all of b, n times: for 2*m*n operations we will local 8*m*n bytes.

A KNL core can load 16 double precision values from it's L1 cache per cycle, but if b has more that 4096 elements then 8*m*n values will need to be fetched from the L2 cache or beyond. As the diagram below illustrates, fetching from each step further in the memory hierarchy requires traversing narrower, longer and more-shared links, so the performance is being limited not by the CPU but by the rate at which the memory system can deliver work to the CPU. The roofline model is a useful tool for identifying whether performance is being limited by the CPU, memory bandwidth or something else.

If you identify that memory bandwidth is a limiting factor then you can modify the code to reuse data in L1, for example with the following transform:

Example

do jblock = 1, m, block_size
do i = 1, n
do j = jblock, jblock+block_size
c = c + a(i) * b(j)
end do
end do
end do


Now, if we choose block_size to fit in L1 cache, each subsequent iteration of the i loop will again traverse the part of b that is held in L1. We will still move 8*m*n bytes over the core/L1 boundary but only 8*(m/block_size)*n bytes across the slower L1/L2 boundary.

On KNL, jobs that can fit within, or make good use of, the 16GB/node high-bandwidth MCDRAM will benefit from it's much higher bandwidth compared to DDR. (On Cori, KNL nodes are configured to use MCDRAM as a very large last-level cache). Process and thread affinity is important on KNL primarily because pairs of cores share a large L2 cache. The narrowest point in the memory hierarchy on Haswell nodes, illustrated below, is the QPI link between sockets, affinity is therefore important on Haswell nodes so processes use the most-local memory. 