Skip to main content

Creating a file-based SWAP on your Beagleboard

Related image


On the Beagleboard you should expect to require a swap file given the limitation of how little RAM is available (between 256 MB and 512 MB). Some system programs like apt-get will only run properly when some swap space is present (due to 256 MB not being enough RAM).
Some images (such as those from Linaro.org) do not come with a swap partition or any swap space allocated.
Under Linux, swap space can be either a dedicated partition or a swap file. Both can be mounted as swap which the OS can access.

Creating a Swapfile

The following commands will create a 512MB file, limit access only to root, format it as swap and then make it available to the OS:
sudo mkdir -p /var/cache/swap/   
sudo dd if=/dev/zero of=/var/cache/swap/swapfile bs=1M count=512
sudo chmod 0600 /var/cache/swap/swapfile 
sudo mkswap /var/cache/swap/swapfile 
sudo swapon /var/cache/swap/swapfile 
To tell the OS to load this swapfile on each start up, edit the /etc/fstab file to include the following additional line:
/var/cache/swap/swapfile    none    swap    sw    0   0
To verify that the swapfile is accessilble as swap to the OS, run "top" or "htop" at a console.

Comments

Popular posts from this blog

hreflang? what is it? where does it come from?

Hreflang tags are a technical solution for sites that have similar content in multiple languages. The owner of a multilingual site wants search engines to send people to the content in their own language. Say a user is Dutch and the page that ranks is English, but there’s also a Dutch version. You would want Google to show the Dutch page in the search results for that Dutch user. This is the kind of problem hreflang was designed to solve. And for the  Webmasters who serve several versions of their content in different languages or for users in different countries should use   hreflang annotations   to help Google show the right version in the search results for each user. In this article, " How to implement hreflang tags... ", the author has lay out the steps from begin to finish on the right ways of implementing hreflang tags for your multilingual and multinational web sites. In another article, " hreflang: the ultimate guide ", the author has outlined the

Library for performing speech recognition, online and offline

This github project has a very detail description on some of the most popular speech recognition engines with installation steps and how-tos. Speech recognition engine/API support: CMU Sphinx  (works offline) Google Speech Recognition Google Cloud Speech API Wit.ai Microsoft Bing Voice Recognition Houndify API IBM Speech to Text Snowboy Hotword Detection (works offline) https://github.com/Uberi/speech_recognition