/explore
|
You are here 
|
blog.lenot.re | ||
| | | | |
blog.nelhage.com
|
|
| | | | | tl;dr "Transparent Hugepages" is a Linux kernel feature intended to improve performance by making more efficient use of your processor's memory-mapping hardware. It is enabled ("enabled=always") by default in most Linux distributions. Transparent Hugepages gives some applications a small performance improvement (~ 10% at best, 0-3% more typically), but can cause significant performance problems, or even apparent memory leaks at worst. To avoid these problems, you should set enabled=madvise on your server... | |
| | | | |
manybutfinite.com
|
|
| | | | | After examining the virtual address layout of a process, we turn to the kernel and its mechanisms for managing user memory. Here is gonzo again: Linux processes are implemented in the kernel as insta | |
| | | | |
www.lukas-barth.net
|
|
| | | | | If you build an application that uses large, contiguous amounts of memory, it can increase your performance if you allocate this memory in so-called huge pages. Linux offers you two ways of doing that - a legacy way and a modern way. This article describes the modern way of using huge pages, so called transparent huge pages (THP) and applies the techniques from a previous article to verify that we actually got huge pages. The article starts by giving a super-short recap on how paging works and why huge p... | |
| | | | |
tomaugspurger.net
|
|
| | | This work is supported by Anaconda, Inc. and the Data Driven Discovery Initiative from the Moore Foundation. This is part three of my series on scalable machine learning. Small Fit, Big Predict Scikit-Learn Partial Fit Parallel Machine Learning You can download a notebook of this post [here][notebook]. In part one, I talked about the type of constraints that push us to parallelize or distribute a machine learning workload. Today, we'll be talking about the second constraint, "I'm constrained by time, and would like to fit more models at once, by using all the cores of my laptop, or all the machines in my cluster". | ||
