On Wed, 09 Oct 2019 12:37:51 +0900, Irfan Ullah said: > Thanks in advance. I am a PhD candidate, and currently I have started > working on kernel development. My professor told me to implement this paper > <https://arxiv.org/abs/1803.02329>. In this paper authors have used machine > learning to predict the next missed addresses. >From the abstract: On a suite of challenging benchmark datasets, we find that neural networks consistently demonstrate superior performance in terms of precision and recall Delving further into the paper, we discover that the researchers have learned that if you run Spec CPU2006 enough times, a neural network can learn what memory access patterns Spec CPU2006 exhibits. But they don't demonstrate that the patterns learned transfer to any other programs. And nobody sane runs the exact same program with the exact same inputs repeatedly unless they're doing benchmarking. Ah, academia - where novelty of an idea is sufficient to get published, and considerations of whether it's a *useful* idea are totally disregarded. > 1) How can I directly store the missed addresses, and instruction addresses > from kernel handle_mm_fault() to a file? Don't do that. Pass the data to userspace via netlink or debugfs or shared memory or other means, and have userspace handle it. > 2) How can I use machine learning classifier in the kernel for predicting addresses?� �� Well... in general, you won't be able to do much actually *useful*, because of time scales. If your predictor says "Oh, program XYZ will need page 1AB83D 20 milliseconds from now", but it takes 10 milliseconds to bring a page in, your predictor has only 10 milliseconds to make the prediction in order to be useful. And in fact, you probably have even less, because your predictor has to be fast enough and use little enough memory that it doesn't significantly affect CPU, cache, or RAM usage. > 3) Is there any way to do the machine learning in the user space in python, and > then transfer� the classifier in bytes forms to the kernel space for address > predictions ? Sure, there's plenty of ways, from using shared memory to creating an ioctl(). But all of them are going to have the same "you need to do it in less time than it takes for the program you're predicting to reach the point for the prediction". Good luck, you will need it.
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