The problem it solves is temporal cache locality, and whether pull or push is better often only depends on what is it that you want to keep in cache for the nex

The problem it solves is temporal cache locality, and whether pull or push is better often only depends on what is it that you want to keep in cache for the next operation. For example, suppose you are doing a 3x3 convolution over a matrix, and you want to keep the result of the convolution in cache because the next operation consumes it. So your cache is pretty much filled with the output matrix, and you only have space in cache to pull in one row from global memory for reading. Now consider what happens if you process your input matrix row-by-row, first with the pull approach and then with the push approach. With the pull approach you need to load 3 rows from memory into cache before you can push. No matter how you do this, as you advance through the input, you are going to be loading some rows twice. For example, suppose that from the 3x3 window, we load the rows in this order: top, bottom, mid (and then push the convolution to the mid row of the output). In one iteration we load rows 0, 2 (evicts row 0) and 1 (evicts row 2). The next iteration loads 1 (hit) and 3 (evicts 1), and then 2 (reloads 2!). That's bad! With the push approach, you load one row, and push partial contributions to the 3 rows of the output matrix. Then you load the next row, and do the same. The output matrix is always in cache, so those writes always hit, and you never need to load a row from memory twice. So in a sense, pull optimizes for bringing the rows of the output into cache once, which is good if you want to continue working with the input afterwards, while push optimizes for keeping the output in cache, which is good if you want to continue working with the output afterwards. One isn't really better than the other. Which one to choose depends on what is it that you want to do. Maybe you have one input matrix, and multiple convolution windows, and want to compute multiple outputs that you don't need right away. In that case, you want to keep the input matrix in cache, and compute the rows of the output one by one, evicting them from the cache after each computation.