January 21, 2021
Last year, from June to September, I worked on the kernel development tool Coccinelle under Collabora. I implemented a performance boosting algorithm for one of Coccinelle's use cases. Here's a look at this work.
Coccinelle is a tool used to refactor C source code. It's used for development in the Linux Kernel. You write an abstract patch (called a Semantic patch in Coccinelle terms), basically to remove a few lines of code and add some, to make a tree-wide change.
Coccinelle uses the semantic patch language for this purpose. Following is a basic example of a semantic patch:
@@ expression E; constant c; type T; @@ -kzalloc(c * sizeof(T), E) +kcalloc(c, sizeof(T), E)
When applied to the tree, the above semantic patch replaces every instance of kzalloc with kcalloc.
For more information, check out this page.
On the inside, Coccinelle has a semantic patch parser and a C parser. When fed a semantic patch and a C file, Coccinelle parses the semantic patch to create an AST, and parses the C file to create an AST as well.
Following this, it compares the semantic patch AST with the C AST. If matches are found, the changes detailed in the semantic patch are made to the C file.
During my work on Coccinelle, I implemented a performance boosting algorithm to speed up recursive parsing of header files in the Linux Kernel.
Coccinelle has an option to parse included header files recursively to figure out types of certain C constructs such as struct fields and typedefs. This is necessary in some cases, as Coccinelle can only look at one C file at a time.
Initially, this recursive parsing would take close to 7 hours for the entire Linux Kernel. Since the target userbase of Coccinelle is kernel developers, 7 hours wasn't a very good benchmark.
Implementation of the performance boosting algorithm resulted in that time coming down to 45 minutes. For the curious, following is the algorithm:
The algorithm isn't perfect, as it still takes 45 minutes to get everything done. There's a lot more that could be done, like leveraging multiprocessing (a whole other can of worms), or conditionally parsing the files based on the semantic patch's matches. However, it works relatively fine on a moderately fast PC.
Thank you Collabora for financially supporting this project!
Earlier this year, I joined Collabora as an intern to work on improving testing in libcamera and automating it through KernelCI. Having…
With the LLVM toolchain seeing increasing development and adoption alongside the older, more established GNU toolchain, projects needing…
This summer, Christoph Haag and I had the pleasure of taking part in Google Summer of Code as mentors for xrdesktop, the Open Source project…
Earlier this year, from January to April 2021, I worked on adding support for stateless decoders for GStreamer as part of a multimedia internship…
In our previous post, we presented a project backed by INVEST-AI which introduces a multi-stage neural network-based solution. Now let's…
Initiated as a joint effort by the Google Chrome OS team and Collabora, the recent KernelCI hackfest brought the addition of new tests including…