On Thu, 2019-10-10 at 11:10 +0900, Irfan Ullah (울라 이르판) wrote:
Thank you for the responses. It is not my assignment. Actually, we have initiated a project based on the idea "incorporating artificial intelligence in the linux kernel to increase its performance".
That is an interesting idea, but it is worth keeping in mind that most "kernel performance" is not about how well the kernel does something, but about how quickly the kernel lets userland programs do things. For example, I have done a lot of work on the scheduler code recently, and the most common thing I see (whenever I try something wrong) is for total CPU use in the system to go up. However, the increase in CPU time used is often in user space, and not in the kernel. For example, trying out a bad idea might result in 0.2% more CPU time spent in the kernel, but 2% more CPU time spent in user space, due to seeing a larger number of cache misses from user space programs. When things are done right, enabling a certain kernel feature might result in some overhead (say 0.3%), which is almost entirely caused by cache line misses, resulting in a total system overhead of twice that (0.6%), because every cache miss in my code results in something else getting evicted from the cache, and somebody else incurring a cache miss as well. In short, if you want to use machine learning to improve kernel performance, you need to figure out a way to do that without causing cache misses in any really hot path. That puts a serious constraint on what you can do. -- All Rights Reversed.