We continue the investigation from the previous post, trying to measure how the memory subsystem affects software performance. We write small programs (kernels) to quantify the effects of cache line, memory latency, TLB cache, cache conflicts, vectorization and branch prediction.
In this post we investigate the memory subsystem of a desktop, server and embedded system from the software viewpoint. We use small kernels to illustrate various aspects of the memory subsystem and how it effects performance and runtime.
We talk about instruction level parallelism: what instruction-level parallelism is, why is it important for your code’s performance and how you can add instruction-level parallelism to improve the performance of your memory-bound program.
We investigate how memory consumption, dataset size and software performance correlate…
For all the engineers who like to tinker with software performance, vectorization is the holy grail: if it vectorizes, this means that it runs faster. Unfortunately, many times this is not the case, and the results of forcing vectorization by any means can mean lower performance. This happens when vectorization hits the memory wall: although…
A post explaining how a few small changes in the right places can have a drastic effects on performance of an image processing algorithm named Canny.
