Activity summary During the month of August, 19 contributors have been paid to work on Debian LTS (links to individual contributor reports are located below). The team released 58 DLAs fixing 1885 CVEs. Debian 11 ( bullseye ), which has reached the end of its Long Term Support on 31 August 2026, will now get security support from Freexian under the Extended LTS offer. The team published several notable updates:

• chromium (DLA 4710-1), uploaded by Emilio, to fix 375 security flaws in bookworm, including issues that could lead to arbitrary code execution. Further DLA 4728-1, uploaded by Emilio as well, fixed additional 41 security issues in bookworm and DLA 4739-1 fixed 5 issues. Last but not least, DLA 4749-1 fixed another 15 issues and DLA 4758-1 fixed another 7 issues.
• ruby2.7 (DLA 4716-1), uploaded by Abhijith, to fix four security flaws in bookworm, including issues that could lead to arbitrary code execution.
• several DLAs (DLA 4717-1, DLA 4720-1, DLA 4723-1, DLA 4724-1, DLA 4745-1) have been uploaded by Ben and Emilio to fix myriad security flaws in different versions of the linux kernel (5.10.262-1, 6.1.180-1, 6.12.100-1~deb12u1, 6.12.101-1~deb12u1) in bullseye and bookworm.
• 7zip (DLA 4718-1) and p7zip (DLA 4719-1), uploaded by Sylvain, to fix multiple security flaws in bookworm and bullseye, including issues that could yield to remote code execution.
• ca-certificates (DLA 4726-1), uploaded by Bastien to add some new trusted certificates to bookworm and bullseye, but also remove some untrusted ones.
• thunderbird (DLA 4727-1 and DLA 4754-1), uploaded by Emilio to fix 35 and 31 issues in bookworm and bullseye.
• nss (DLA 4729-1), uploaded by Jochen to fix a vulnerability in bookworm and bullseye that could yield to arbitrary code execution.
• libgd2 (DLA 4731-1), uploaded by Guilhem to fix a vulnerability in bookworm and bullseye that could yield to arbitrary code execution if a malformed GIF file is processed.
• xorg-server (DLA 4737-1 and DLA 4738-1), uploaded by Arnaud to fix several vulnerabilities in bookworm and bullseye.
• postgresql-15 (DLA 4740-1), uploaded by Carlos Henrique Lima Melara to fix 26 vulnerabilities in bookworm.
• unzip (DLA 4741-1), uploaded by Andrej to fix a vulnerability in bookworm and bullseye that could yield to arbitrary code execution.
• firefox-esr (DLA 4750-1), uploaded by Emilio to fix 31 vulnerabilities in bookworm and bullseye that could potentially result in arbitrary code execution, privilege escalation or information disclosure.
• nvidia-graphics-drivers (DLA 4752-1 and DLA 4753-1), uploaded by Tobias to fix lots of vulnerabilities in bookworm and bullseye.
• roundcube (DLA 4760-1), uploaded by Guilhem to fix ten vulnerabilities in bookworm and bullseye that could potentially result in remote code execution or information disclosure.

Contributions from outside the LTS Team: We are greatly thankful for the contributions from people outside the LTS Team:

• Salvatore Bonaccorso released a libyaml-syck-perl security update (DLA 4730-1), to fix a denial of service and potentially arbitrary code execution.
• Thomas Goirand released a neutron security update (DLA 4735-1).
• Thomas Goirand released an ironic security update (DLA 4743-1).
• Thomas Goirand released a designate security update (DLA 4751-1), to fix a denial of service or DNS hijacking.

The LTS Team has also contributed with updates to the latest Debian releases:

• xrdp (DSA 6469-1), prepared by Abhijith to address privilege escalation and arbitrary code execution related flaws.

Other contributions: Besides the work on security updates, different documentation and tooling changes were needed. This work was mainly done by Sylvain.

Individual Debian LTS contributor reports

• Abhijith PA
• Andrej Shadura
• Arnaud Rebillout
• Bastien Roucari s
• Ben Hutchings
• Carlos Henrique Lima Melara
• Chris Lamb
• Daniel Leidert
• Emmanuel Arias
• Emilio Pozuelo Monfort
• Guilhem Moulin
• Jochen Sprickerhof
• Lee Garrett
• Lucas Kanashiro
• Markus Koschany
• Santiago Ruano Rinc n
• Sylvain Beucler
• Thorsten Alteholz
• Tobias Frost

Thanks to our sponsors Sponsors that joined recently are in bold.

• Platinum sponsors:
  • Toshiba Corporation (for 131 months)
  • Civil Infrastructure Platform (CIP) (for 99 months)
  • VyOS Inc (for 64 months)
• Gold sponsors:
  • F. Hoffmann-La Roche AG (for 142 months)
  • CONET Deutschland GmbH (for 125 months)
  • University of Oxford (for 81 months)
  • EDF SA (for 53 months)
  • Dataport A R (for 28 months)
  • CERN (for 26 months)
• Silver sponsors:
  • Domeneshop AS (for 146 months)
  • Nantes M tropole (for 140 months)
  • Akamai - Linode (for 136 months)
  • Univention GmbH (for 132 months)
  • Universit Jean Monnet de St Etienne (for 132 months)
  • Ribbon Communications, Inc. (for 126 months)
  • Exonet B.V. (for 116 months)
  • Leibniz Rechenzentrum (for 110 months)
  • Minist re de l Europe et des Affaires trang res (for 94 months)
  • Dinahosting SL (for 81 months)
  • Upsun Formerly Platform.sh (for 76 months)
  • Moxa Inc. (for 69 months)
  • sipgate GmbH (for 67 months)
  • OVH US LLC (for 65 months)
  • Tilburg University (for 65 months)
  • GSI Helmholtzzentrum f r Schwerionenforschung GmbH (for 56 months)
  • THINline s.r.o. (for 29 months)
  • Copenhagen Airports A/S (for 23 months)
  • Conseil D partemental de l Is re (for 9 months)
  • CEOS GmbH
• Bronze sponsors:
  • Seznam.cz, a.s. (for 147 months)
  • Evolix (for 146 months)
  • Linuxhotel GmbH (for 144 months)
  • Intevation GmbH (for 143 months)
  • WAAYS (for 142 months)
  • Megaspace Internet Services GmbH (for 141 months)
  • Greenbone AG (for 140 months)
  • NUMLOG (for 140 months)
  • WinGo AG (for 139 months)
  • Entr ouvert (for 131 months)
  • Adfinis AG (for 128 months)
  • Plat Home (for 125 months)
  • Laboratoire LEGI - UMR 5519 / CNRS (for 123 months)
  • Tesorion (for 123 months)
  • Bearstech (for 114 months)
  • LiHAS (for 114 months)
  • Catalyst IT Ltd (for 109 months)
  • Demarcq SAS (for 103 months)
  • Universit Grenoble Alpes (for 89 months)
  • TouchWeb SAS (for 81 months)
  • SPiN AG (for 78 months)
  • CoreFiling (for 74 months)
  • Observatoire des Sciences de l Univers de Grenoble (for 65 months)
  • Tem Innovations GmbH (for 60 months)
  • WordFinder.pro (for 60 months)
  • CNRS DT INSU R sif (for 59 months)
  • Alter Way (for 52 months)
  • SOBIS Software GmbH (for 26 months)
  • Tuxera Inc. (for 18 months)
  • OPM-OP AS (for 9 months)
  • LU-CIX Management G.I.E.

Debian Contributions: 2026-08 Contributing to Debian is part of Freexian s mission. This article covers the latest achievements of Freexian and their collaborators. All of this is made possible by organizations subscribing to our Long Term Support contracts and consulting services.

Replacing lxc with Incus for Debian CI, by Antonio Terceiro At the beginning of this month, the default backend for tests was changed to incus-lxc, leaving only a few dozen packages explicitly configured to run as lxc. Those packages got bug reports filed and once those bugs are fixed they will be migrated over to incus-lxc. This marks an important milestone for Debian CI, as the low level of isolation between the worker host OS and the OS under test when using lxc was a significant source of instability in our platform. Common causes for test failures when comparing runs under plain lxc with runs with incus-lxc are documented in the wiki page linked from the bug reports.

security-tracker git performance, by Helmut Grohne Developers working with Debian s security-tracker have reported degrading performance for a while. The options for solving this are few with repository sizes now reaching 30GB for a repository whose working tree is a mere 60MB. While there have been a number of proposals for changing the storage layout, Helmut evaluated options not requiring such changes. Much of the problem hinges on the 50MB data/CVE/list file that is updated in most commits. Efficiency hinges on representing its content in git packs. Practically, git frequently fails to represent its content as a delta and stores a full copy that is typically compressed to 12MB. Add a few thousand 12MB full blobs and you quickly have a large repository. In particular, the copy at salsa.debian.org stores many such full blobs, so pulling from it consumes significant bandwidth. One might think that running git gc helps, but its utility is limited here. When git stores objects in packs, it stores the history in reverse. It starts with the current version and represents older objects as differences (deltas) to more recent objects. A delta effectively is a sequence of copying ranges from a base object and insertion of new content. Given that humans tend to add content over time, those additions are often represented as deletions. The crux here is finding that base object. Given that the data/CVE/list file is large, finding it involves comparing quite a few versions of it with each other. This requires both a significant amount of RAM and CPU time. How many objects git considers for this comparison is controlled with the --window option. It is beneficial if the base object does not come from a direct child commit but skips over commits. In doing so, long chains of deltas are avoided. The --depth option controls the maximum chain length. In this search, git combines all objects into a single window to facilitate file renames. As such, it tends to compare data/CVE/list with data/DSA/list, data/DLA/list and others. This is less than helpful and tends to evict all data/CVE/list versions from the comparison window such that a new full blob of it becomes required. Since the security-tracker repository rarely renames files, we can ask git to instead consider one window per filename via --path-walk. Once doing so, it becomes quite a bit better at finding suitable deltas. The technique is less applicable to older history (2025 and earlier), but given a lot of RAM and a bit of partitioning, git gc can shrink that as well. Combining these techniques, we can shrink the repository into 700MB and keep new growth somewhat under control. Temporarily, Helmut is providing a proof-of-concept mirror at git://git.subdivi.de/~helmut/security-tracker.git using these techniques. Avoid pulling from it directly as it does not provide a secure transport. While it does reduce the amount of data being downloaded, it does not resolve a number of related problems. After downloading, a git client will still expend considerable amounts of CPU on verifying received deltas and attempting to git blame data/CVE/list is not improved in any way. Still, there is work on integrating some of the improvements back into salsa.

OpenSSH GSS-API split, by Colin Watson In an option review Colin did in 2024, shortly after the xz-utils backdoor, he explained that having GSS-API authentication and key exchange support in the main OpenSSH packages is problematic. The key exchange patch is large and intrusive. Even linking to the necessary libraries isn t without risk: as the Ebury malware attack demonstrated way back in 2009, each extra library linked into security-critical daemons such as sshd (or nowadays into its privilege-separated helper programs) can modify the behaviour of the daemon even if you aren t doing anything that would involve calling into that library. Of course some of that risk remains, but as Damien Miller wrote, minimizing the number of libraries that end up in the address space of sshd and friends is still valuable. This split is now complete in testing. As of openssh 1:10.4p1-5, the OpenSSH client and server are built without GSS-API authentication and key exchange support. If you need those features, install openssh-client-gssapi or openssh-server-gssapi instead, as appropriate. Debian 13 (trixie) already has packages with those names that just depend on the regular openssh-client and openssh-server so that you can pre-emptively install them, as documented in the release notes. The new openssh-*-gssapi packages have relatively tight dependencies on openssh-common, in order for the testing migration system to ensure that we can t forget to keep them up to date. This will mean a bit more ongoing work on each new upstream version, but it should be manageable.

Catching up on DebConf video publication, by Stefano Rivera DebConf 25 videos suffered terribly from a bug in voctomix that stopped us from being able to publish the videos immediately after the conference. Ivo De Decker did some work on the videos earlier this year, fixing enough of the audio problems to make the videos at least intelligible. While at MiniDebConf Winterthur Stefano published all the videos from 2025 and 2026 to PeerTube and YouTube. DebConf 25 and DebConf 26 videos as well as all the videos from miniconfs that had got caught up in the backlog were published.

Miscellaneous contributions

• Carles used po-debconf-manager to review and submit Catalan translations for six packages.
• Carles prepared a prototype of building webwml and running tidy (linter) in Salsa.
• Carles created different webwml merge requests with different fixes: invalid links to different architectures, 404s to external sites, fixed templates with invalid URL to favicon.ico, making USE_SAMPLE_FILES=1 to avoid fetching external files, typos in English, etc.
• Carles updated the LibreOffice Wiki page.
• Emilio managed several transitions, such as glibc 2.43, perl 5.42, kdepim, nginx, gnome-desktop 51, python3.14-only, and many others.
• Emilio updated libxfont to 2.0.9, xorg-server to 21.1.24, xwayland to 24.1.13, and fixed a FTBFS bug in terminator.
• Antonio deployed the website for MiniDebConf Beijing 2026, and archived the Hamburg 2026 and Kanpur 2026 MiniDebConf websites.
• Antonio released auto-apt-proxy 18, adding new features and fixing bugs; that needed a followup 18.1 release to fix an issue identified in the testing migration tests.
• Antonio did several Salsa maintenance tasks, including his first platform security update and investigating an issue that was causing the background tasks to be severely delayed, imparting the site usability.
• Thorsten tried to fix some bugs in hplip and cups. Unfortunately this did not result in an upload this month, but stay tuned!
• Helmut continued to report undeclared file conflicts and correspond about them.
• Helmut reviewed and merged Johannes Schauer Marin Rodrigues work on adding hurd support to debvm.
• Helmut fixed a pile of minor issues in the support libraries underpinning unschroot.
• Helmut adapted rebootstrap to archive regressions and improved its handling of non-default gcc versions.
• Helmut fixed the rendering of decompressed logs at crossqa.debian.net.
• Helmut sent patches for two cross build failures.
• Colin deployed the fix for Invalid link rel= canonical on bugs.debian.org. In the process he found a few bugs in recent undeployed code and fixed them.
• Santiago continued helping Aryan Karamtoth with the GSoC project on introducing livepatching support for linux. Several challenges have been identified in the recent couple of months, and Santiago is trying to guide Aryan to address them and design the solution. The mentoring is done in collaboration with Emmanuel Arias.
• Lucas onboarded a co-maintainer for Valkey.
• Lucas has started to work on a draft for the Debian Outreach project for onboarding newcomers interested in Debian packaging via a video course.
• Stefano worked on the after-math of DebConf 26, handling the bursary expense reimbursement process and winding down the conference finances.
• Stefano uploaded Python 3.15 RC 1 to Debian unstable.
• Stefano did some budgeting and travel planning for the DebConf 27 site-visit in Asahikawa, Japan. The DebConf committee sends a couple of members to meet with the local team and view the venue together, in advance of the next DebConf.

Changes in version 0.0.7 (2026-09-13)

• Added fun_statistics and fun_profile function
• Expanded README.md with additional badges, and updated URLs
• Updated continuous integration multiple times
• Switched to Authors@R

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. If you like this or other open-source work I do, you can now sponsor me at GitHub.

Changes in version 0.1.2 (2026-09-12)

• Expanded README.md with additional badges, and updated URLs
• Updated continuous integration multiple times
• Switched to Authors@R
• Added usage, arguments and value sections to manual page

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. If you like this or other open-source work I do, you can now sponsor me at GitHub.

Two weeks ago, I released AppStream 1.2.0. This release contains a lot of great changes, but one of the most important ones concerns how media are being handled, and AppStream s default image export format.

AppStream is a Freedesktop metadata standard to describe software components. That can be anything from system services over fonts to console and graphical applications. AppStream metadata is supposed to give users enough information to decide whether they want to install a piece of software, to represent that piece of software, and to give the operating system enough information to decide whether a software component should be installed automatically and (to some extent) what capabilities and relations it has, to provide the user with sensible options.

Especially for the first two goals, and especially for GUI applications, AppStream supports icons and screenshots, which are used to showcase applications. Today, AppStream is used by all kinds of services, from Linux distributions over firmware updates to Flatpak and desktops directly. AppStream s original design however comes from the perspective of Linux distributions in 2011, where you may want to browse the software catalog offline, without delay, and without pinging an external server (which could be a privacy concern).

Therefore, a common way to deploy an AppStream-enabled software repository is to ship all icons of all applications in the repository to the user as part of the repository metadata download. AppStream does support remote icon downloads nowadays, and for a while I thought that this would become the default eventually. However, especially in today s world, having a bandwidth-saving, instantly responsive, privacy-protecting application browsing experience seems more important that ever.

PNG images are great!

The only format that AppStream supports for icons and screenshots (which are downloaded on-demand from your distributor s CDN) has always been exclusively PNG. PNG images are perfect for icons, because they compress well (especially for common icon shapes), are fast and simple to load, and can be loaded anywhere, by any toolkit or webbrowser. They also ensure we deliver faithful screenshot images, even though we may have scaled or re-rendered them. Still though, PNG images are less great for screenshots, as they are not very efficient, which puts strain on any CDN that has to deliver them, as well as on people s internet connections when browsing screenshots. Having smaller thumbnails alleviates that problem a little, but does not fully solve it.

But even for icons, PNG could be improved upon: In many cases, icons are re-downloaded with the repository metadata again and again, so having a large icon tarball adds up to the data transferred during metadata refreshes. AppStream also now supports large 128x128px icons, which nobody in 2012 expected we would need, adding even more data that will be re-downloaded. Saving some space here translates directly to lower bandwidth costs as well as faster downloads for users.

To improve PNG file sizes, the AppStream Compose library, which handles all image processing and metadata catalog composition, was running optipng on all generated PNG images. That does create smaller PNG images, but they were still relatively large compared to other image formats.

For a long time though, there was no alternative to PNG images for icons: There was no lossless image compression format that could give us the same quality as PNG images and that was also widely supported.

JPEG-XL vs PNG in AppStream

Since 2021 we have JPEG-XL (JXL), which offers a true lossless mode with often better compression than PNG. The issue was that JPEG-XL wasn t widely supported. Then, in 2025, the PDF Association selected JPEG-XL as the preferred image format for HDR images in PDFs, and now we are finally getting browser support and more ubiquitous availability of the format (you can try it right now in Firefox!).

For screenshots, using JXL s lossy mode, it has obvious and extreme size advantages over PNG, so supporting JXL or WebP for screenshot images was an obvious choice. If JXL would support the lossless case very well as well though, we could serve many use cases with the same exported image format, which is very attractive to me.

So, the obvious next question was whether it was worth the pain of switching the icon format, so I did some measurements on real icons. For that I used the AppStream component icon pool that Debian Unstable ships, which is almost 5000 application icons of various sizes, and converted them to PNG:

Icon size	Icons	PNG total	JXL total	Pool saved	PNG avg	JXL avg	Median saved	Mean saved	Worst	Best	Larger as JXL
48 48	1544	3.7 MiB	3.0 MiB	17.8%	2.4 KiB	2.0 KiB	17.9%	16.7%	-118.7%	60.0%	206
64 64	2018	7.0 MiB	5.8 MiB	17.8%	3.6 KiB	2.9 KiB	18.0%	15.8%	-112.7%	70.0%	279
128 128	1411	11.2 MiB	8.7 MiB	22.0%	8.1 KiB	6.3 KiB	20.1%	17.5%	-89.7%	61.0%	209
TOTAL	4973	21.9 MiB	17.5 MiB	19.9%	4.5 KiB	3.6 KiB	18.6%	16.6%	-118.7%	70.0%	694

PNG images saved with libpng at effort=4, compression=9, then optimized using optipng -o2, JXL images encoded using vips jxlsave lossless=1 effort=7 strip=1 via VIPS/libjxl.

As the table shows, using lossless JXL images over size-optimized PNG images (using optipng s default settings) provides a roughly 20% gain. This does not look like much, until you consider how often these files are downloaded: A 20% file size reduction may only save 1-2 MiB of disk space, but if they are downloaded over and over again by many clients, it will save a lot of bandwidth.

Interesting JXL encoding findings

As a sidequest, I was curious why some images were larger than their PNG counterparts when encoded with JXL, and what the ones that were significantly smaller were.

In short, the biggest size reductions for JXL existed on images that were already small as PNG, and contained large, flat color surfaces with hard edges and simple shapes. They were not very interesting, and much of JXL s wins come from accumulating smaller gains across all files, which compound the bigger icons get (especially at 128x128px, where JXL truly shines).

The events were JXL loses to PNG are more interesting: For example, it does quite poorly with pixel-art images that have a lot of repeating patterns. Those are encoded well by PNG, but less efficiently by JXL. Take for example Vonsh:

Icon of Vonsh, an SDL-based snake game, which PNG compresses better than JXL

My guess is that while PNG can exploit the repeating pixel patterns for compression, JXL s predicts surrounding pixels from its neighbours, which fails too often and makes it pay almost full entropy per pixel. In this single rare case, the PNG is at 5.4 KiB, while the JXL is almost 8 KiB in size.

Other cases I looked at were arguably buggy input data, where color channels were hidden under the alpha channel of the input image. PNG could probably again exploit repeats, while we were forcing JXL to encode pixels that were invisible in the final image. This is arguably a problem with the original input data. Currently, AppStream does not make any changes to icons at all, but in future we might add a filter that removes invisible colors from images to solve this pathological case (it was only two icons out of 5000 though, so it is not a high priority).

The third case I found where JXL loses to PNG were icons with checkerboard-like patterns:

Icon of x3270, an IBM 3270 Terminal Emulator

For those, PNG can likely again exploit the repeating patterns, while a checkerboard layout is pretty bad for left/top predictors like JXL s. However, in this case the size difference (and loss for JXL) is only 450 bytes, so even though JXL loses to PNG, it does so not by much.

JXL in AppStream

Given these findings, JPEG-XL is the default image format starting with AppStream 1.2.0. AppStream Compose will encode all images losslessly as JXL, while screenshots are encoded in lossy mode at Q=90 effort=7. Since the optipng step does not happen for JXL images, this comes at no speed penalty and is even a bit faster on modern x86_64 CPUs (where libjxl can use SIMD). PNG is still available, and Compose can be told to switch between the two formats.

Upsides of JXL in AppStream right now

If you use JXL in Compose or the recent release of appstream-generator, you will get much smaller images and, for screenshots, will benefit from other JPEG-XL features such as progressive decoding, providing a far nicer user experience. libAppStream has supported JXL icons since version 1.1.3, so your clients will need that version or a newer one, and all software centers will have to support loading JXL images (which all of them do, provided the right plugins are installed).

Downsides of switching to JXL too quickly

JXL is a very new format, so web browsers might not yet display it if you are serving webpages. Your clients may also have bugs in processing JXL images, as the format is still new . For example, switching on JXL in Debian sent KDE Discover into an infinite loop on startup while trying to load the icons (an issue which has been fixed, but clients will need that patch first before JXL is switched on).

This currently makes JXL enablement only possible when you know that your clients can support it. This is the case for me in Debian Unstable and Debian 14, which are using JXL images for a few weeks now, but not for any older releases. Platforms like Flatpak have it even harder, because they do know even less about their clients. So, even though it has big advantages, you may want to hold off on using JXL right away, and force PNG by setting the ImageFormat key to png in appstream-generator s configuration, or passing --image-format=png to appstreamcli compose.

It is also worth mentioning that JPEG-XL is much, much slower on systems that do not have SIMD instructions or for which the libjxl/jxl-rs library does not have them (such as apparently riscv64 right now). If this is a concern, you might not want to switch to JXL right away.

Media pipeline improvements

Besides the JXL default change, AppStream 1.2.0 also comes with a complete overhaul of its media processing pipeline. While libappstream, AppStream s main library, does not do any media processing and comes with very minimal dependencies to be embedded in client applications and used on servers, the same can not be said about libappstream-compose, AppStream s library to build metadata generating applications (the server-side part, usually).

The compose library has to render fonts into font specimen cards, inspect translation files, render SVG images, decode all kinds of raster images, inspect video files, etc. Especially the fonts, and the fact that fonts can appear in SVG images, has caused issues in the past, as libappstream-compose is a heavily threaded library and most font libraries can only work from a single thread. This forced the library to essentially go into single-thread mode anytime anything that could touch a font was being processed.

AppStream also originally was created for a safe world where applications were vetted by the distributors before their metadata was processed. This is increasingly not the case, so it made sense to put at least a few guardrails on the most complex part of the pipeline: The media processing. As part of the change, media processing was split out into a separate worker process. This solved two problems at once: Font handling was isolated in a single-threaded binary if we wanted to handle fonts in parallel, we could simply spawn more workers. And, being in a separate process, the media processing could now be sandboxed.

As part of the multiprocess changes, Compose also switched from using GdkPixbuf to VIPS for image processing. The latter allows for much more fine-grained control over the image output and encoding, and comes with a lot of well-maintained filters and operations, which made it possible to eliminate a fair chunk of AppStream s hand-rolled image processing operations. As part of this transition, we unfortunately lost the ability to read XPM images, which dropped about 20-30 applications from the pool at Debian. But in the name of security, this is a sensible choice, especially since most XPM icons were very small and low-resolution, and applications using them could benefit from adding a high-quality PNG icon anyway. With VIPS, we also now restrict the amount of image formats we can load to a sensible set, so extremely niche or unexpected formats will be outright rejected (this includes sane-but-unusual formats for screenshots and icons, such as TIFF images).

The Compose library, with all of these changes, will now just request high-level operations (e.g. render a font card for this font to a JXL image ) from the worker, and provide it with input data in sealed memfds and output locations as FDs as well. On Linux systems, the worker will use Landlock if available, to block all write access to the filesystem, deny device access and deny TCP and UDP as well. The sandbox can certainly be tightened a fair bit in future, but this was a good and safe start to gain some experience with it without having things break too easily, given the many places Compose is used in (also, Landlock s API is surprisingly nice to use, so it was easier than I thought to add in this early version).

With all of these changes, the libappstream-compose library is now also officially marked API-stable, so you should be able to rely on it in future to build new things (its API has barely changed in the past, and now with the new media API and defaults change in place, it was time to declare it stable).

I want to see / try this!

Currently, the easiest way to have a look at the new data is to check out Debian Unstable. If you have a JXL-enabled browser, you can also see the icons in AppStream Generator s HTML pages for Debian Sid. If you are using appstream-generator for your distribution, you will also get much more pleasant statistics and HTML pages, as well as fully deterministic media output and a whole bunch of security updates, so, update to its recent 1.0 release.

Please keep in mind that if you switch to JXL, the client tools receiving the image data have to support it. Support varies depending on the Linux distribution, so, test it first and switch the default back to PNG in case you encounter any issues.

What s next?

With so many features and changes landed, the next changes in AppStream will focus on improving what already exists and fixing any issues (there will be more blogposts about the other features 1.2.x delivers!). Testing with the entire Debian archive as data source makes me fairly confident though that there will not be many problems. In the longer term, tightening the media processing sandbox will also be something we might want to do, e.g. by hiding parts of the filesystem tree or filtering syscalls.

For JPEG-XL, one obvious question is Will you add support for it to the Freedesktop icon-theme specification as supported format alongside PNG, SVG(Z), and XPM? . For on-disk icon repositories, JXL s space-savings are less compelling, and it being HDR-capable is also not necessarily a killer feature (PNG can go a long way!). However, JPEG-XL s ability to immediately decode larger images at reduced resolution without resampling could legitimately be very powerful here, as applications could ship a single large image and quickly decode it at 1/2, 1/4 or 1/8 the size for different purposes in their UI. JPEG-XL also supports spot-color extra channels, which applications could use as masks to recolor raster icons at render time. This could be incredibly nice to color symbolic icons on-the-fly without any SVG and CSS. JXL also provides richer metadata, which might be neat for (license/author) documentation. So, the answer here is: Maybe it makes sense to allow another format, but this will have to be discussed first, as it would force JXL into every toolkit and desktop, which is a much bigger ask than supporting it only in AppStream.

As always, let me know what you think and please report any issues or bugs directly against AppStream or AppStream Generator if you encounter problems that are with the tools, and not with a project s metadata.

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. If you like this or other open-source work I do, you can now sponsor me at GitHub.

Changes in version 0.0.7 (2026-09-09)

• Corrected a docstring for the module
• Updated continuous integration setup several times
• Simplified setup by removing no-longer-needed Makevars
• Added badges to README.md

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. If you like this or other open-source work I do, you can now sponsor me at GitHub.

• [DLA 4713-1] sslh security update to fix one CVE in Bookworm related to a so-called link following vulnerability.
• [DLA 4714-1] libmodbus security update to fix one CVEs in Bookworm related to a stack-based Buffer Overflow vulnerability.
• [DLA 4715-1] kissfft security update to fix two CVEs in Bookworm related to integer overflows.

• einsteinpy to unstable, fixing a dependency bug.
• supernovas to unstable, sponsored upload of a new upstream version.

• libosmocore to unstable.
• osmocom-analog to unstable.
• libosmo-netif to unstable.
• osmo-trx to unstable.
• libosmo-abis to unstable.
• libosmo-sigtran to experimental.
• osmo-mgw to unstable.
• osmo-bsc to experimental.
• osmo-sgsn to experimental.
• osmo-pcu to unstable.
• osmo-hlr to unstable.
• osmo-tetra to unstable.

• tango to unstable to fix bugs and do a soname transition.
• pytango to unstable to fix bugs and do a soname transition.

Changes in RcppArmadillo version 15.6.0-1 (2026-09-07)

• Upgraded to Armadillo release 15.6.0 (Medium Roast Cortado)
  • Expanded solve() with solve_opts::scale_thresh option to widen detection of poorly conditioned systems
  • Expanded trans() and .t() to handle cubes
  • Added permute() to rearrange dimensions of cubes (generalised transpose)
  • Added cubemul() for batched matrix multiplication of cube slices

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. If you like this or other open-source work I do, you can sponsor me at GitHub.

Encoder Let s begin with the Encoder, since it s slightly less involved. I ve tried to make this a bit more accessible by drawing a diagram out before describing the order of operations, so that it s possible to follow along visually. While the process here can look like a lot, it s really not that bad. We begin by taking the incoming bytes, converting the bytes into bits, and chunk those bits into parts which are sized to fit completely within an LDPC message. We will then encode incoming data into LDPC messages, using our configured LDPC Matrix (the table we appropriated from 802.3an). Next, we apply whitning over all the bits in our encoded (and packed) LDPC messages, using our configured whitening constant. In the case of QPSK, pairs of bits will then be modulated into a QAM subcarrier, where each QAM point represents a range of bits in the message. We ll go through each of those modulated IQ subcarriers, and set each corresponding data subcarrier in order, for each OFDM symbol contained in the pigeon Burst. The preamble configuration is then used to generate (or, more likely, can be used at startup to precompute) the Schmidl-Cox preamble, which is written to the first IQ samples in our output IQ buffer. Finally, we will do a series of inverse FFT operations to convert each OFDM symbol to the time domain, including their cyclic prefix. Let s take a look at doing that, but in code this time now:

// (lightly edited for clarity)
impl Encoder  
 ..

 /// Encode the provided bits into the output time-domain IQ samples.
 fn encode(
 &mut self,
 dst: &mut [IQ],
 src: &Vector,
 ) -> Result<Burst, Error>  
 let src =  
 let mut raw = Vector::new(self.fec.message_len());

 // Set  raw 's data bits, compute and set LDPC
 // checkbits.
 self.fec.add(&mut raw, src);

 // Apply whitening, and return
 raw.xor(&self.whitening)
  ;

 // copy in the precomputed schmidl-cox preamble to  dst 
 let preamble_len = self.preamble_iq.len();
 dst[..preamble_len].copy_from_slice(&self.preamble_iq);

 // allocate a new (frequency domain) 'Burst' container.
 let mut burst = Burst::new(
 &self.mode.ofdm.plan,
 self.mode.ofdm.symbols
 );

 // modulate bits from 'src' as iq, and set each
 // data subcarrier for each ofdm symbol in the
 // burst.
 self.burst_encoder.multiplex(&mut burst, &src);

 // convert from frequency-domain data into time
 // domain iq samples, writing out ofdm symbols
 // and cyclic prefixes to  dst .
 self.burst_encoder
 .transform(&mut dst[preamble_len..], &burst)?;

 // normalize all IQ samples; the maximum magnitude
 // in the IQ buffer may be very small, which weakens
 // our transmitted signal. Scale all IQ samples such
 // that the maximum IQ sample magnitude will be '1.0'.
 dst.norm();

 Ok(burst)
  
 

Using the Encoder should hopefully be fairly straightforward we ll give it a bag of bytes, and get back some IQ samples that we can ask our nearest SDR to transmit. As for what happens on the other end?

Decoder Next up is the mirror image of our Encoder the, imaginatively named, Decoder. The Decoder is slightly more involved (since it has to find the packet in the IQ stream, as well as correct for channel error(s)), so we ll do the same thing as above start with a diagram. My hope is going over the Encoder first helps us only really focus on the new stuff, otherwise it should feel like running the Encoder backwards. Here, we start with an incoming stream of IQ, where we will process scan detections as they come in from our Schmidl-Cox detector and burst Scanner. This will give us a snippit of IQ, sized to exactly our Burst. We ll begin to correct our IQ samples by first doing frequency estimation and correction in the time domain using our preamble and ofdm configuration. We ll then do a series of inverse FFTs to extract each OFDM symbol in our Burst, where we can then do channel estimation and correction. With the OFDM symbols (hopefully) good enough, we can now map each data subcarrier back to bits, and unapply whitning. The resulting bits are then chunked back up into LDPC messages, which are then checked, and concatanated data extracted. Finally the bits are turned back into bytes, which are written to our output buffer. However, before we get into the code to do this there s one last detail. We know bursts won t overlap (if they do, it s likely not possible to recover right now even though other PHYs can and do), so any time we see something we believe to be a burst, we can skip ahead by the burst s (constant) length within the IQ, and avoid trying to decode anything else in there. The nice side-effect here is this also gives us an interesting property for the Decoder namely, we know the maximum number of Burst detections we can get for a given block of incoming IQ data if they were packed end-to-end and we can pre-allocate the memory we need, avoiding allocations for every demodulation attempt (which may or may not even be a valid Burst). This pre-allocated block of memory to hold the burst s data is something that I ve called a frame buffer internally. Each frame buffer contains exactly sized buffers to hold decoded information from the burst an iq buffer that is exactly the same number of samples required to encode the preamble and data, exactly the number of bits needed to store pre and post FEC data, pre-allocated byte array, etc. Not shockingly, the code looks like this:

#[derive(Clone)]
pub struct FrameBuffer  
 /// Corrected IQ samples
 pub samples: Samples,

 /// post-correction OFDM burst
 pub burst: Burst,

 /// demodulated bits from the OFDM burst
 pub bits: Vector,

 /// demodulated bits from the OFDM burst,
 /// after FEC, and cleaned
 pub raw_bits: Vector,

 /// Layer 2 contents of the Frame
 pub contents: Vec<u8>,
 

Of course, that alone is handy but we need to use them. So let s go ahead and do what we promised above each Decoder uses a fixed number of pre-allocated FrameBuffers to store packets in-flight, packed into what is, creatively, called FrameBuffers within my code. As an aside, I likely should have called this a Memory Pool, since that s the common and accepted name for this design pattern but being stuck with unfortunate names is the burden of those of us who stumble into sensible ideas over time. The only nuance here is that I use the pools strictly sequentially we only save the FrameBuffer if the LDPC checksum is correct, allowing us to only keep track of how many successful packets we have and being able to get the valid FrameBuffers, rather than storing a handle to each FrameBuffer as we go a promise that most memory pools do not make, since blocks can usually be taken and returned in any order. Let s go ahead and do the whole Decoder dance now:

// (lightly edited for clarity)

impl Decoder  
 ..

 /// Process incoming IQ for Pigeon Bursts, and
 /// demodulate them.
 pub fn decode(
 &mut self,
 buf: &[IQ],
 ) -> Result<Vec<(Detection, &FrameBuffer)>, Error>  
 let mut ret = Vec::new();
 let mode = self.scanner.mode().clone();

 // reset the "valid frame buffer count" back to 0
 self.frames.reset();

 // call the scanner and get scan detections for
 // this block of iq ( buf )
 for detection in self.scanner.scan(buf)  
 // for each detection, we're (only) going to process
 // the iq snippit, but pass along the metadata
 // such as SNR.
 let ScanDetection  
 snippit,
 snr,
 range,
 m,
   = detection;

 // grab the next free frame buffer to work within.
 let frame_buffer = self.frames.next_mut();

 // Copy the snippit into the frame buffer (a mutable
 // location)
 frame_buffer.samples.copy_from_slice(snippit);

 // estimate the frequency offset based on the
 // Burst's Schmidl-Cox preamble.
 let preamble_fo = preamble::estimate_frequency_offset(
 &mode.preamble,
 mode.rate,
 &frame_buffer.samples[..mode.preamble.samples()],
 );

 // Shift the IQ stream by the estimated frequency
 // offset -- hopefully we're closer to 0Hz
 frame_buffer.samples.shift(mode.rate, preamble_fo);

 // estimate the frequency offset based on the
 // each burst's **cyclic prefix** -- exactly like
 // we did with the Burst Schmidl-Cox preamble,
 // but this time on each OFDM symbol.
 let ofdm_fo = ofdm::estimate_frequency_offset(
 &mode.ofdm,
 mode.rate,
 &frame_buffer.samples[mode.preamble.samples()..],
 );

 // Shift the IQ stream closer yet; hopefully this
 // is a very small nudge even closer still to 0Hz.
 frame_buffer.samples.shift(mode.rate, ofdm_fo);

 // Do a bunch of inverse fft operations for each
 // OFDM symbol, filling the frequency-domain Symbol
 // structs in  frame_buffer.burst  (Burst) struct.
 //
 // this will also do channel estimation and
 // correction before returning.
 self
 .decoder
 .transform(
 &mut frame_buffer.burst,
 &frame_buffer.samples[mode.preamble.samples()..],
 )?;

 // "demultiplex" each data subcarrier's frequency-domain
 // IQ constellation point, setting the correct bit range.
 self.decoder.demultiplex(
 &mut frame_buffer.raw_bits,
 &frame_buffer.burst
 );

 // unapply whitening by XOR-ing the buffer with
 // the well-known whitening vector.
 frame_buffer.raw_bits = frame_buffer.raw_bits.xor(
 &self.whitening);

 // verify that the LDPC message(s) are all correct,
 // and if so, concatanate the the data bits (no check
 // bits) to the  bits  vector.
 if self.fec.decode(
 &mut frame_buffer.bits,
 &frame_buffer.raw_bits
 ).is_err()  
 // this is where invalid packets fail. we gave it a good go.
 // next packet please.
 continue;
  

 // copy the raw bits out, as bytes, to the  contents  buffer.
 frame_buffer.bits.copy_as_bytes(&mut frame_buffer.contents);

 // store metadata/metrics on the demodulation.
 ret.push(Detection  
 m,
 snr,
 index: range.start,
  );

 // save the contents of this frame buffer (don't
 // reuse this buffer next go-around).
 self.frames.save();
  

 // We're going to take out the borrow on the frame at
 // the end since we don't want to deal with telling the
 // compiler via code gymnastics that the mut and non-mut
 // borrows are OK since they're non-overlapping.
 Ok(ret.into_iter().zip(self.frames.iter()).collect())
  
 

Phew. That was kinda a lot. In fact it s basically the whole thing. This function is as close to how do you read an OFDM packet as it gets, and perhaps the most important part of this whole series. Beyond that, though, this is a huge conceptual unlock. This means we now have an incredibly powerful primitive; the ability to take bytes and go to/from IQ samples over the air. Let s talk about pigeon modes

Rendering in Lynx, a text browser

Rendering below the paragraph on a narrow viewport

Rendering in the margin on a large viewport

<sup id="fnref:YYY" data-anchor="--lf-sn-YYY">
  <a href="#sidenote-YYY">1</a>
</sup>

<aside role="note" id="sidenote-YYY" data-anchor="--lf-sn-YYY">
  <sup>1</sup>
  <p>A first paragraph.</p>
  <p>A second paragraph.</p>
</aside>

aside[role="note"]  
  margin-block: 1rlh;
  color: #444;
 

@supports (anchor-name: attr(data-anchor type(<custom-ident>)))  
  @media (min-width: 72rem)  
    main  
      position: relative;
      sup[data-anchor]  
        anchor-name: attr(data-anchor type(<custom-ident>));
        /*   anchor-name: --lf-sn-YYY */
       
      aside[role="note"][data-anchor]  
        anchor-name: --lf-sidenote;
        position: absolute;
        position-anchor: attr(data-anchor type(<custom-ident>));
        /*   position-anchor: --lf-sn-YYY */
        top: max(anchor(top), anchor(--lf-sidenote bottom, -1rlh) + 1rlh);
        left: 100%;
        margin: 0 2rem;
        width: 18rem;
        color: inherit;
       
     
   
 

The three cases for the vertical position of a note

1. The first note s top edge aligns with the top edge of its reference mark: as there is no previous note, anchor(--lf-sidenote bottom, -1rlh) + 1rlh resolves to 0 and max() returns anchor(top).
2. When the reference mark of a later note sits above the bottom of the previous note, plus some vertical space, the note goes below the previous one to avoid a collision. max() returns anchor(--lf-sidenote bottom) + 1rlh.
3. Otherwise, the note s top edge aligns with the reference mark s top edge, as max() returns anchor(top).

About the project and me Your program crashes. You open gdb and get ?? instead of a stack trace. So you go find the right -dbgsym package, for the right version, for the right architecture, install it, and start again. Debuginfod removes that entire detour: gdb asks a server for symbols by the build-ID baked into the binary. Debusine already built packages, already produced -dbgsym files, and already hosted the archives; it just couldn t answer the question. This summer I made it answer. My project was to add debuginfod server functionality to Debusine so that it not only hosts -dbgsym packages, but also serves their debug symbols over the debuginfod(8) protocol. Debian developers can then debug binaries by setting a single URL that gdb uses to fetch the matching debug symbols. This project took me through design, backend work, an extraction pipeline on the worker, HTTP serving, documentation, and testing from the first blueprint all the way to a live demo on debusine.debian.net.

Initial planning and design changes A design first, in !3030. The proposal submitted for GSoC 2026 was just an overview of how things will work, but in reality there were a lot of design questions which needed to be answered before starting with contribution. Debusine keeps development blueprints in its docs tree, reviewed like code, it s basically a blueprint of what feature or new changes are we going to make. I was assigned the work item #957, which was basically about how the idea of implementing a debuginfod server functionality inside Debusine was initially proposed by a fellow member which later became a project idea under GSoC 2026. My developer blueprint pinned down the four decisions everything else depends on: extraction happens on the worker after the build, symbols are stored as artifacts keyed by build-ID, they re published into suites alongside their binaries, and they re served from the archive root rather than per-suite. Settling that up front meant the design discussions happened in a document instead of across three merged branches. One of those arguments became its own fix. My wording implied symbols were unpacked inside the isolated sbuild environment (the consequence was I was handed a bug to be solved in the first week of contribution period), when they re actually extracted afterwards on the worker, where the build output already sits, a distinction that matters, because doing work inside the unshare environment means extra tooling in the chroot and more ways to affect the build. !3119 corrected it before the wrong model spread into the code.

A new artifact type Artifacts are a major concept in Debusine overall, so as per the developer blueprint we introduced a new artifact which was debian:debug-symbols. It holds every .debug file from one -dbgsym package. Its data is a validated list of lowercase 40-character build-IDs, and each file is stored under its build-ID as the path, so answering what are the symbols for this ID? is a direct lookup, with no path translation in the request handler. One artifact per package rather than per file: a util-linux build would otherwise spray hundreds of artifacts, collection items and relations across the database for no benefit. For implementing debian:debug-symbols artifact, I changed the main models.py file, along with that since it s a norm to write unit tests, all mentioned under !3088.

Publishing workflow and solving a bug Extracting symbols is only useful if they reach the archive people actually install from, so !3180 taught package_publish to follow the relates-to relation: copying binaries into a suite now brings their debug symbols along automatically, with nothing extra for the publisher to configure. Each build-ID becomes its own collection item, for example debugsym:hello_2.10-5_amd64_fcc9064 each carrying the package name, version and architecture copied from the binary, so the item is meaningful on its own without dereferencing anything. Uniqueness is enforced at both the suite and archive level, because the serving URLs are archive-wide and two suites must never disagree about what a build-ID means: republishing an identical file is accepted quietly, while two different files claiming the same ID is an error worth failing on. A partial index on the build-ID keeps the eventual HTTP lookup fast. That looked finished until symbols started arriving in target suites disconnected from their binaries published, but unfindable, because copying items between collections silently dropped their artifact relations, and that relation is the only thing tying the two together. The fix sat one level above my feature, in the generic CopyCollectionItems task that does the copying, and since it was reusable infrastructure rather than anything debuginfod-specific, Colin implemented it himself in !3228. My project needed it to work at all; every other Debusine feature that copies items now gets it for free.

Endpoint and CI tests With symbols in the archive, !3212 added the part users actually touch: GET / scope / workspace /buildid/<build-id>/debuginfo looks the ID up across every suite in that workspace s archive, streams the file, and sets the X-DEBUGINFOD-FILE and X-DEBUGINFOD-SIZE headers the protocol expects. It also handles the two things gdb actually does: a HEAD probe before committing to a download, and ranged requests to pull individual ELF sections instead of the whole file. Scoping it to the archive rather than the suite is what lets one URL cover a whole workspace, so the developer never has to know which suite their binary came from. Every merge request above landed with unit tests, but those only tell you that the pieces behave correctly. What Colin and I wanted was a real gdb fetching real symbols from a real instance, so !3261 adds an autopkgtest that builds a package, publishes it, checks the HTTP headers, then sets DEBUGINFOD_URLS and makes gdb go and get the symbols, wired into the CI integration tests so it runs on every change. It took me a day to learn that skipping the signing worker doesn t simplify that test, it just hangs until the 30-minute timeout, because update_suites needs signing to produce a usable repository. The last piece, !3301 covers the new artifact, the suite and archive changes, the new archive URL, and a how-to for using it. My first how-to draft explained how everything worked and offered four ways to set DEBUGINFOD_URLS; the version that shipped gives one recommended setup and gets out of the way. The same pass trimmed the blueprint down to only what s still unimplemented, since a design document describing merged code is just an obstacle for the next reader.

What s left Only one item on my original plan didn t land: an archive-level build_debug_symbols switch, modelled on Launchpad s equivalent, letting an archive skip building -dbgsym packages entirely by passing DEB_BUILD_OPTIONS=noautodbgsym to sbuild. It was always the stretch goal rather than core scope, landing the extract-publish-serve path solidly mattered more than landing it broadly. The design is written up in the blueprint, and I intend to implement it myself. The other gaps were deliberately out of scope from the start, and the blueprint says so. DWZ supplement files aren t ingested, so packages using compressed debug info may render without the alternate strings table; debugging still works, it s just less complete. Source-file serving runs into the same Debian packaging limits that constrain debuginfod.debian.net today, making it a design question rather than a coding one. Executable serving, the metrics and metadata endpoints, and federation to upstream debuginfod servers were excluded for similar reasons, none of them are needed for Debusine s core use case, and each would have crowded out the parts that are. One open bug is left too. On the last day of the coding period, Stefano Rivera found that publishing ledger and linux was failing, because I had told the database that a build-ID identifies one exact debug file which isn t true in Debian, since dh_dwz runs once per binary package, so when one object ships in two binary packages their .debug files differ while describing identical code. How to fix it is still an open discussion #1582, though it may not land before the formal end of the project. None of that is a handoff. GSoC s timeline is ending, my involvement isn t, I m carrying on with Debusine until both the build_debug_symbols switch and DWZ supplement support are merged, and I expect to keep contributing beyond that. This project got me familiar with a codebase I enjoy working in, and the remaining pieces are mine to finish.

Thanks! The biggest thanks go to my mentor, Colin Watson, whose reviews consistently found the thing I hadn t thought about. He also gave me room to get things wrong first and understand why, which taught me more than being handed the answer would have. Thanks as well to Rapha l Hertzog, Enrico Zini, Stefano Rivera, Carles Pina i Estany and Helmut Grohne and everyone else around Debusine and Freexian for reviews, comments and patience with my questions. Special thanks to Freexian for developing Debusine in the open and for giving me access to test on debusine.debian.net. Finally, thanks to the wider Debian community, whose build-ID and -dbgsym conventions did most of the hard work before I arrived and to Google Summer of Code for providing a platform and the time to do this properly.

Changes in version 0.0.4 (2026-09-06)

• Minor updates to continuous integration, README.md and DESCRIPTION

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. If you like this or other open-source work I do, you can now sponsor me at GitHub.

• [1] https://www.comaps.app/
• [2] https://www.openstreetmap.org/

Background: Why does DCS need a fast Integer Codec? Debian Code Search (DCS) is a search engine that allows searching all the Open Source source code within Debian, with either literal search expressions or regular expression search queries. A search engine uses an inverted index: a map from term to documents containing the term. Each document is typically represented most efficiently by using an id, so the index consists of many lists of document ids. When searching, it is important to quickly decode these lists to answer the search query. However, there is a point of diminishing returns where the decoding speed, even though it can still be measurably improved quite a bit, no longer influences the overall query duration. From 2012 (its inception) to 2019, Debian Code Search used to use a small index format, and queries were fast because the index was kept entirely in RAM. In 2019, I implemented the new index format, which adds an on-disk positional index. For literal queries (78.2% of DCS queries), querying the positional index on disk is faster than querying the non-positional index in RAM. The efficient encoding of the TurboPFor format makes it possible to fit such an index on a mid-sized Hetzner server, which I rent with two 1 TB SSD disks. The optimized decoder of the C TurboPFor library is what made decoding fast at query time. If you want to dive deeper into the algorithm, see this blog post from February 2019:

TurboPFor: an analysis

Motivation I have recently been looking into speeding up Debian Code Search. As a quick reminder, search engines answer queries by consulting an inverted index: a map from term to documents containing that term (called a posting list ). See the Debian Code Search Bachelor Thesis (PDF) for a lot more details. Read more

If you want to learn more about the positional index, see this blog post from September 2019:

Debian Code Search: positional index, TurboPFor-compressed

Over the last few months, I have been developing a new index format for Debian Code Search. This required a lot of careful refactoring, re-implementation, debug tool creation and debugging. Read more

SIMD in Go For many years, you had the following options for using SIMD instructions in Go:

1. Hand-writing Go assembler code. This is only doable for small functions, for example bytes.IndexByte is implemented with hand-written Go assembly (including AVX2).
2. Generating Go assembler code with tools like Michael McLoughlin s Avo . This is how crypto/internal/fips140/sha256 uses AVX2. While Avo generator code definitely is higher-level than hand-written assembly, it is still too close to assembly for my taste.
3. Use a C library via cgo so gcc or clang compiles SIMD code. Debian Code Search used to use the powturbo/TurboPFor C library via cgo for the last 7 years.

The C TurboPFor library has served us well, but Debian Code Search was always intended to be a project using Go, so I would prefer it if I did not have any C code in the project. Go 1.26 (released in February 2026) introduced the simd/archsimd package:

Go 1.26 introduces a new experimental simd/archsimd package, which can be enabled by setting the environment variable GOEXPERIMENT=simd at build time. This package provides access to architecture-specific SIMD operations. It is currently available on the amd64 architecture and supports 128-bit, 256-bit, and 512-bit vector types, such as Int8x16 and Float64x8, with operations such as Int8x16.Add. The API is not yet considered stable. Go 1.26 Release Notes

For my 2019 TurboPFor analysis, I implemented goturbopfor, a native Go teaching decoder (without any SIMD), because I find Go code easier to follow than C code, especially optimized C code. My implementation was intentionally not optimized so that the code was easier to study. The TurboPFor format/algorithm has a vector-optimized part: bitpacking comes in a scalar variant (bitunpack32) and a vector variant (bitunpack256v32), where the vector variant is used for full blocks (256 values) and the scalar variant is used for remainder blocks (< 256 values). When Go 1.26 was released, I used Claude Code to explore whether my native Go decoder s bitunpack256v32 function (for the vertical vector layout) could be implemented using Go SIMD, and the answer was yes, it was possible and it was faster than without SIMD, but not quite at the level of C TurboPFor. If you let Claude Code try for long enough, it eventually finds enough optimizations (about 10) to match C performance. I don t want to vibe-code Debian Code Search, though, so I figured I would find some time to review the SIMD code at some point and see if I could implement something similar myself. Before I found enough time and motivation to complete said review, I discovered that to not regress real-life query performance by more than 10 to 100 milliseconds (which seems acceptable), I don t actually need to add SIMD code to my teaching decoder at all; it would be sufficient to reduce allocations in my teaching decoder and specialize it per bit width. Encouraged by the possibility of using the optimized native Go decoder in Debian Code Search, I explored whether I could also implement a native Go encoder so that I could get rid of the C TurboPFor dependency entirely. The answer is yes, it is doable in a few days, and it isn t even that much slower: Go is at 76% of C, see Debian/dcs commit e920dc7. The goal I set myself at that point was to see if I could learn enough SIMD to optimize the native Go encoder such that its performance would match how DCS uses C TurboPFor (via cgo). Beating C TurboPFor was possible in 2-3 commits (SIMD and bit width specialization). To my surprise, Claude Fable 5 pointed out that the encoder s block scanning could be done more efficiently using a technique called positional popcount, and that is another 2x speed-up! To be clear: I am not saying the Go compiler beats C here. Certainly, the C compiler can also produce fast AVX512 code and can be used to implement positional popcount. When comparing apples to apples, i.e. backporting the AVX512 kernels and positional popcount technique to C TurboPFor, Go benchmarks a little slower at 1.4x C. This spectacular result (much faster than what DCS had before) got me curious how far I could push the decoder with SIMD after all. I ended up matching/exceeding the cgo version here, too! The rest of this article explains a few classes of optimizations I encountered along the way.

Starting Point When I wrote my goturbopfor teaching decoder, I named its functions to match the upstream C TurboPFor library, but now I want to get away from names like p4ndec256v32 they make sense from the TurboPFor perspective, but for Debian Code Search, we can use cleaner names. Before writing any code, I audited how DCS uses integer compression / decompression.

API design: BlockEncoder, BlockDecoder and streaming In Debian Code Search, we have the following usage patterns:

• Partial Indexing: When a new package (or package version) enters Debian, all of its (text) files are indexed. If the hello-2.12.3-1 package (hypothetically) contained only hello.c with printf("hello!\n");, we would assign document ID 1 to hello.c and store in the partial index that trigrams pri, rin, int, ntf, etc. are all found in doc 1 (hello.c).
• Full Index Merging: The many thousands of partial index files (for each Debian package) are combined into a small handful of large index files: When searching, it would be expensive to consult thousands of indexes. To merge multiple partial index files into one larger index (which can then be efficiently queried), we need to re-encode the partial index files: what used to be document ID 1 in the partial index might be document ID 2531 in the full index.
• Querying (searching): When users enter search queries, these queries need to be answered as quickly as possible. The relevant entries in the full indexes are decoded (in parallel).

For reading the index, we do keep the decoded uint32s fully in memory, so we only need DecodeN(input []byte, output []uint32) (read int), a function that reads len(output) values (uint32) from input and returns how many bytes it consumed. For writing the index (both in partial indexing, and when merging), keeping the entire index in memory is prohibitively expensive, so we need a streaming API, for decoding and for encoding. Ultimately, I converged on the following API:

package pforenc

type BlockEncoder struct  
    // scratch buffers can go here
 

// EncodeBlock encodes len(vals)<=256 uint32s into dest (one TurboPFor block).
func (*BlockEncoder) EncodeBlock(dest []byte, vals []uint32) []byte  

// EncodeN calls EncodeBlock in a loop.
func (*BlockEncoder) EncodeN(dest []byte, vals []uint32) []byte  

type StreamEncoder struct  
  be   BlockEncoder
  vals [256]uint32
  // scratch buffers
 

// if full, you need to call [EncodeBlock]
func (*StreamEncoder) Add(val uint32) (full bool)

// EncodeBlock must be called after all data was [Add]ed.
//
// Write the returned buffer to file or send it over the network;
// it is only valid until the next [EncodeBlock] call.
func (*StreamEncoder) EncodeBlock() []byte  
  if se.n == 0   return nil   // turn an extra EncodeBlock into a no-op
  //  
 

This API (the decoder works similarly) allows us to process data in TurboPFor format without any memory allocations. The types are not safe for concurrent use by multiple goroutines. The zero value is ready to be used. For the streaming API, the result only stays valid until the next call.

Initial Implementation Before we can optimize anything, we need a working decoder and encoder. The decoder already exists: my goturbopfor teaching decoder. Next up, I needed an encoder. Writing a TurboPFor encoder has a delightfully simple starting point: You can encode all values at bit width 32, in little endian, at which point you only need to add a one-byte TurboPFor block header every 256 values and you re done:

func (be *BlockEncoder) EncodeN(dest []byte, vals []uint32) []byte  
  for len(vals) > 0  
    chunk := min(len(vals), 256)
    dest = be.EncodeBlock(dest, vals[:chunk])
    vals = vals[chunk:]
   
  return dest
 

func (be *BlockEncoder) EncodeBlock(dest []byte, vals []uint32) []byte  
  const bitWidth = 32
  dest = append(dest, bitWidth)
  for _, val := range vals  
    dest = binary.LittleEndian.AppendUint32(dest, val)
   
  return dest
 

Of course, this is a terribly inefficient compressor, so after the first commit, the real work starts: implement each block type until the compression matches the original C TurboPFor implementation (same output file size), or in other words: do the reverse of the decoder.

1. The TurboPFor bitpacking block type (bitpacking implementation commit) encodes a bit stream of variable bit width (where the bit width is in range 0 bitWidth 32) in little endian byte order. By scanning all values and choosing the smallest bit width that allows representing all values, this technique saves disk space (compresses).
2. The bitpacking with exceptions block type (bitpacking with exceptions implementation commit) determines two bit widths: one for values, the other bit width for encoding exceptions. This allows choosing a lower bit width (that does not cover all values) compared to the bitpacking block type. A bitmap encodes whether a value has an exception or not.
3. The bitpacking with VB exceptions block type (bitpacking with VB exceptions implementation commit) is a variant which does not use an exception bitmap and encodes exceptions using a variable byte integer encoding. This is more efficient when there are few exceptions (less than 20) or the exceptions are very different in bit width compared to the other values.
4. Lastly, the constant block type (constant implementation commit) stores just one value on disk. This is useful for all-zero or all-one blocks, for example.

I found it interesting to realize that the main work of the encoder is to scan the input values and choose the optimal block type, whereas the actual encoding itself is cheap in comparison. At this point, we can look at performance and see that the Go encoder is at 76% of the C encoder. In all honesty, I could have probably stopped here, but now that the milestone of a viable replacement was reached, I got curious to see how far it would be possible to push the encoder (how much work to reach C speeds?) and afterwards, the decoder, too.

Setup

The microarchitecture level: set GOAMD64 The microarchitecture of a CPU determines which instructions it provides, and that includes not just SIMD instruction sets (like AVX2), but also other useful instructions like LZCNT (Leading Zero Count), which can be used to implement math/bits.Len32 more efficiently, which the TurboPFor encoder needs to call on every input value to determine the ideal bit width. Let s walk through how to set the microarchitecture level when using Go on 64-bit x86 (x86-64). Go uses the GOARCH environment variable to configure the target compilation architecture, and I am using the value amd64 to select 64-bit x86 (AVX2 and AVX512 are instruction sets found on x86-64 CPUs). With GOARCH=amd64, the architecture-specific variable GOAMD64 configures the microarchitecture level for which to compile and Go 1.18 introduced these 4 different levels:

GOAMD64=v1 (default): The baseline.
Exclusively generates instructions that all 64-bit x86 processors can execute. GOAMD64=v2: all v1 instructions,
plus CMPXCHG16B, LAHF, SAHF, POPCNT, SSE3, SSE4.1, SSE4.2, SSSE3. GOAMD64=v3: all v2 instructions,
plus AVX, AVX2, BMI1, BMI2, F16C, FMA, LZCNT, MOVBE, OSXSAVE. GOAMD64=v4: all v3 instructions,
plus AVX512F, AVX512BW, AVX512CD, AVX512DQ, AVX512VL.

In 2026, I generally recommend compiling with GOAMD64=v3 so that functions like bits.OnesCount8 are compiled into intrinsics (POPCNT) instead of using a lookup table. For Intel CPUs, setting GOAMD64=v3 means your programs will only start on Haswell CPUs (2013) or newer; for AMD CPUs that means Zen 1 (2017) or newer. In this specific case (DCS), I am even compiling with GOAMD64=v4. The v4 microarchitecture level requires AVX512, which means AMD Zen 4, Zen 5 or newer (Intel s story is complicated). Luckily, both my main development PC (Zen 5) and the Debian Code Search server (Zen 4) are recent enough. Setting GOAMD64=v4 has little effect on Go 1.27 itself: the only change is that maps use one less instruction (VPBROADCASTB instead of PSHUFB). But compiling with GOAMD64=v4 allows us to move one more feature check from runtime to compile time, see SIMD build tags. It makes sense to set the microarchitecture level in your benchmark setup so that you don t measure the slow fallback implementations. I use export GOAMD64=v4 in my Makefile.

Benchmarking setup Go s built-in testing package contains support for benchmarks which are written in functions of the form func BenchmarkXxx(b *testing.B). The simplest way to run such benchmarks is go test -bench=., but I ended up configuring a few convenience make targets, which write results to bench.txt and compare against baseline.txt (the previous commit s results, usually), using the very useful benchstat tool.

GOTEST=go test

# -count=6 gives p 0.002 in benchstat:
# https://pkg.go.dev/golang.org/x/perf/cmd/benchstat
BENCHFLAGS=-run=^$$ -bench=. -benchtime=200000x -count=6

# use taskset -c1 to always pin to the same single core,
# avoiding accidental scheduling on different cores on
# mixed-core CPUs like the Ryzen 9 9950X3D.
TASKSET=taskset -c 1
BENCH=$(TASKSET) $(GOTEST) $(BENCHFLAGS)

.PHONY: all test bench bench-baseline bench-relative

all: test

bench: test
	$(BENCH)   tee bench.txt
# Compares compression ratio between C and Go implementation
	benchstat -col /impl -row '/n /vals' -filter '-/impl:go-stream .unit:(encoded-bytes)' bench.txt
# Compares performance between C (cgo) and Go implementation
	benchstat -col /impl -row '/n /vals' -filter '.unit:(Mval/s)' bench.txt

bench-baseline: test
	$(BENCH)   tee baseline.txt

bench-relative: test
	$(BENCH)   tee bench.txt
	benchstat -filter '-/impl:go-stream .unit:(encoded-bytes)' baseline.txt bench.txt
	benchstat -filter '/impl:go .unit:(Mval/s)' baseline.txt bench.txt

The encoded-bytes and Mval/s units are custom metrics I am reporting from the various sub-benchmarks, which are arranged such that I can filter / report them with benchstat. The main encoder (and decoder) benchmarks compare 3 different implementations (cgo, Go, Go with the StreamEncoder API) with a number of benchmark cases that are designed to cover the different block types and contain a similar mix of values as what we see in Debian Code Search:

// reportMetrics adds Mval/s and encoded-bytes metrics to all benchmarks.
func reportMetrics(b *testing.B, n int, nencoded int)  
   b.ReportMetric(float64(nencoded), "encoded-bytes")
   b.ReportMetric(float64(b.N*n)/1e6/b.Elapsed().Seconds(), "Mval/s")
 

// BenchmarkEncode/n=<N>/vals=<testcase>/impl=<c go go-stream>
//
// e.g. BenchmarkEncode/n=2048/vals=one-constant/impl=go-stream
func BenchmarkEncode(b *testing.B)  
   for _, tc := range allBenchCases()  
     n := len(tc.vals)
     b.Run(fmt.Sprintf("n=%d/vals=%s", n, tc.name), func(b *testing.B)  
       b.Run("impl=c", func(b *testing.B)  
         b.ReportAllocs()
         var encoded []byte
         buf := make([]byte, turbopfor.EncodingSize(n))
         for b.Loop()  
           encoded = turbopfor.P4nenc256v32Buf(buf, tc.vals)
          
         reportMetrics(b, n, len(encoded))
        )
       b.Run("impl=go", func(b *testing.B)  
         b.ReportAllocs()
         var be BlockEncoder
         var encoded []byte
         buf := make([]byte, 0, turbopfor.EncodingSize(n))
         for b.Loop()  
           encoded = be.EncodeN(buf, tc.vals)
          
         reportMetrics(b, n, len(encoded))
        )
       b.Run("impl=go-stream", func(b *testing.B)  
         b.ReportAllocs()
         var se StreamEncoder
         var encoded int
         for b.Loop()  
           encoded = 0
           for _, val := range tc.vals  
             if se.Add(val)  
               encoded += len(se.EncodeBlock())
              
            
           encoded += len(se.EncodeBlock())
          
         reportMetrics(b, n, encoded)
        )
      )
    
 

CPU counters: perf Go has included excellent performance tooling for many years, see the Profiling Go Programs blog post (2011) for an example of how to use pprof, a sampling profiler. This profiler can help track down which part of a program runs slow, or where memory allocations happen. Once you identified the slow part of a program, how do you know why it s slow? To learn more about the specific bottlenecks your program encounters, you can consult your CPU s hardware performance counters. For example, you could check the branch predictor counters to see if your program is slow due to a high number of branch mispredicts. On Linux, the perf tool is the best way to access the CPU hardware performance counters. A good starting point for working with perf is the documentation on Top-down analysis with the perf tool , which describes the optimization method that Intel established. In my Makefile, I set up two perf targets:

# GOTEST and TASKSET like shown in the earlier benchmarking setup section:
GOTEST=go test -pgo=encode.cpuprof
TASKSET=taskset -c 1
PERFBENCHFLAGS=-test.bench='Encode/n=2048/vals=debian-mix/impl=go$$' -test.benchtime=200000x

# Use perf(1) to capture AMD IBS (the equivalent to Intel PEBS)
# PipelineL1 is roughly equivalent to Intel TopdownL1
perf:
	$(GOTEST) -c
	$(TASKSET) perf stat -M PipelineL1 ./pforenc.test -test.run=^$$ $(PERFBENCHFLAGS)
	sudo perf record -F 4999 -e ibs_op// --call-graph fp ./pforenc.test -test.run=^$$ $(PERFBENCHFLAGS)
	sudo chmod 644 perf.data

# 488281 iterations   2048 values = 1.000e9 values, so counter/1e9 = per value.
perf-per-value:
	$(GOTEST) -c
	$(TASKSET) perf stat -x, -e cycles:u,instructions:u,branches:u,branch-misses:u ./pforenc.test -test.run=^$$ -test.bench='Encode/n=2048/vals=debian-mix/impl=go$$' -test.benchtime=488281x 2>&1 >/dev/null   awk -F, ' printf "%-16s %6.2f /val\n", $$3, $$1/1e9 '

The perf-per-value numbers are high level numbers that indicate how much work the implementation is doing. Reducing the number usually increases speed. To see the counters for each instruction (and source code lines), I use make perf, followed by perf report. A quick shortcut is perf annotate, which directly shows the hottest function.

Optimizations (scalar) Let s first see how far we can get without reaching for SIMD instructions. (The examples are not necessarily in commit order, but cherry-picked for clarity.)

Profile-Guided Optimization (PGO) PGO stands for Profile-Guided Optimization and is a feature that Go introduced as a preview in Go 1.20 (released in February 2023) and shipped as ready for general production use in Go 1.21 (released in August 2023). The idea is to capture a CPU profile that records where your program spends most of its CPU time, which you then provide to the Go compiler to give it more data to make better decisions. Most importantly, this way the Go compiler can inline functions much more aggressively than its usual heuristics allow, which does have a measurably positive effect in my series of optimization commits. Another optimization that a PGO profile allows the compiler to do is conditional devirtualization but our TurboPFor code does not use any interfaces. My strategy is to enable PGO before doing any other optimizations, so that we have the full inlining budget available that PGO gives us, and can measure the effect of other commits clearly. Surprisingly, turning on PGO actually decreases our performance (-13% geomean), but a closer investigation reveals that we just got unlucky. Let me explain. Aside from inlining and conditional devirtualization, PGO also influences alignment: The Go compiler sets PCALIGNMAX(64, 31) on the first block of a loop (the loop body ) for all loops in hot functions (per the PGO profile), i.e. Go will insert up to 31 bytes of padding to make the block land on a 64-byte boundary. Documentation like AMD s Software Optimization Guide for the AMD Zen5 Microarchitecture (2024, #58455) explicitly recommends aligning hot loops that way:

[ ] for hot loops, some further knowledge of trade-offs can be helpful. Because the processor can read an aligned 64-byte fetch block every cycle, it is suggested to either align the start of the loop to the beginning of a 64-byte cache line [ ]

Indeed, when compiling with -gcflags=all=-d=alignhot=0 to disable the alignment, performance remains as good as without PGO. How can the padding hurt more than help? The answer is: It s not the padding itself! It s a side-effect of the padding moving instructions to different addresses. In the unlucky arrangement, a macro-fused CMPQ+JGE instruction pair now ends up exactly on a 32-byte boundary. However, the Go compiler ensures fused branch sequences must never cross or end at a 32-byte boundary to fix Intel erratum SKX102 (discussion: Go issue #35881) by inserting NOPs. This NOP padding, unlike the loop alignment padding, is not free; these extra instructions slow down our otherwise dispatch-bound loops. Because the commits after the PGO enabling commit change the code, this unlucky situation is avoided for the rest of the optimization series (by chance).

Reducing memory allocations Memory allocations are quite expensive, at least in comparison to encoding/decoding integers, so I followed my usual strategy of first reducing memory allocations as much as possible. In my goturbopfor teaching decoder, whenever the code needed a scratch buffer, it would allocate it right then and there with make():

// p4dec32 decodes one block of TurboPFor-encoded 32 bit ints
func (d *decoder) p4dec32(input []byte, output []uint32) (read int)  
    //  
  switch blockType  
  case blockBitpackingExceptions:
    bx, input := input[0], input[1:]
    n := len(output)

    exmap := input
    nex := 0 // number of exceptions
    for i := 0; i < n; i++  
      if exmap[i/8]&(1<<uint(i%8)) != 0  
        nex++
       
     
    input = input[(n+7)/8:]

    exceptions := make([]uint32, nex)
    input = input[bitunpack32(input, exceptions, bx):]
    input = input[d.bitunpack(input, output, b):]

    for i := 0; i < n; i++  
      if exmap[i/8]&(1<<uint(i%8)) != 0  
        output[i] += exceptions[0] << b
        exceptions = exceptions[1:]
       
     

    return before - len(input)
   
 

The Go compiler can turn make(T, n) calls into stack allocations, if n is known at compile-time. But, in this case nex is not known at compile-time. We can verify that Go calls into the runtime (runtime.makeslice) by dumping the object code (assembly) with source annotated (-S):

% cd ~/go/src/github.com/stapelberg/goturbopfor
% git reset --hard 49b7c05cc61e77f0257568eb73833467714d2b4a
% go test -c  # go1.27.0
% go tool objdump -S goturbopfor.test   perl -nlE 'say if /p4dec32/ .. /^$/'
TEXT github.com/stapelberg/goturbopfor.(*decoder).p4dec32(SB) /home/michael/go/src/github.com/stapelberg/goturbopfor/goturbopfor.go
func (d *decoder) p4dec32(input []byte, output []uint32) (read int)  
  0x549f60		4c8da42460ffffff	LEAQ 0xffffff60(SP), R12
  0x549f68		4d3b6610		CMPQ R12, 0x10(R14)
  0x549f6c		0f86d9070000		JBE 0x54a74b
  0x549f72		55			PUSHQ BP
  0x549f73		4889e5			MOVQ SP, BP
  0x549f76		4881ec18010000		SUBQ $0x118, SP
  0x549f7d		48899c2430010000	MOVQ BX, 0x130(SP)
  0x549f85		4889b42448010000	MOVQ SI, 0x148(SP)
	if len(output) == 0  
  0x549f8d		4d85c0			TESTQ R8, R8
  0x549f90		0f84a7030000		JE 0x54a33d
  0x549f96		660f1f840000000000	NOPW 0(AX)(AX*1)
  0x549f9f		90			NOPL
[ ]
		exceptions := make([]uint32, nex)
  0x54a4be		488d057bec1700		LEAQ 0x17ec7b(IP), AX
  0x54a4c5		4c89fb			MOVQ R15, BX
  0x54a4c8		4889d9			MOVQ BX, CX
  0x54a4cb		e8f0ddf3ff		CALL runtime.makeslice(SB)
[ ]

An easy speed-up was to avoid allocations through reuse (in goturbopfor). In the DCS pfordec package (with the improved API design), I ended up with a vals [256]uint32 field in the StreamDecoder type, which brings us from 773 Mval/s to 858 Mval/s on the debian-mix:

% benchstat -filter '/impl:go /vals:debian-mix .unit:(Mval/s)' \
  baseline.txt bench.txt
goos: linux
goarch: amd64
pkg: github.com/Debian/dcs/internal/turbopfor/pfordec
cpu: AMD Ryzen 9 9950X3D 16-Core Processor
             baseline.txt               bench.txt               
                Mval/s        Mval/s     vs base                
n=2048        1.089k   1%   1.175k   0%   +7.85% (p=0.002 n=6)
n=2039         974.7   0%   1046.0   0%   +7.32% (p=0.002 n=6)
n=160          434.9   1%    513.6   5%  +18.11% (p=0.002 n=6)
geomean        772.9         857.7       +10.98%

Aside from the speed-up, avoiding memory allocations is generally nice in benchmarks because it removes the garbage collector from the equation and makes it less likely that your benchmarks get other processes OOM-killed on the same machine.

Generics for bit width specialization In general, we want to make it easy for the compiler to understand as much as possible about our algorithm. Consider this bitpack implementation:

func bitpack(dest []byte, vals []uint32, bitWidth int) []byte  
  mask := uint32(1<<bitWidth - 1)
  var acc uint64
  var have int
  for _, val := range vals  
    acc  = uint64(val&mask) << have
    have += bitWidth
    for have >= 32  
      dest = binary.LittleEndian.AppendUint32(dest, uint32(acc))
      acc >>= 32
      have -= 32
     
   
  for have > 0  
    dest = append(dest, byte(acc))
    acc >>= 8
    have -= 8
   
  return dest
 

Let s think through what determines the iterations and control flow this function uses:

1. The number of input values (vals), but not their actual value.
2. The bit width to pack into (bitWidth).

With a bit of careful rearrangement, we can provide the compiler with both, a fixed number of input values (say, 32), and a bit width, both known at compile time. Why is this worthwhile? Because we can manually unroll the loop, let the compiler eliminate much of the repetition and get much faster compiled code as a result! Let s first fix the number of input values to 32 and rewrite the loop to calculate the position offsets within dest instead of changing dest on each value (with AppendUint32):

func bitpack32Unrolled(dest []byte, vals *[32]uint32, bitWidth int)  
  // only one bounds check for 32 values
  dest = dest[: 4*bitWidth : 4*bitWidth]
  mask := uint32(1<<bitWidth - 1)
  var acc uint64
  var have, pos int
  // Manually unrolled loop starts here.
  // Each iteration is identical except for the vals[x] index.
  acc  = uint64(vals[0]&mask) << have
  have += bitWidth
  if have >= 32  
    binary.LittleEndian.PutUint32(dest[pos:pos+4], uint32(acc))
    pos += 4
    acc >>= 32
    have -= 32
   

  // vals[1] .. vals[30] elided for brevity

  // Each loop iteration is 8 lines of Go code, so for 32 input values,
  // bitpack32Unrolled contains 8*32 = 256 lines of code.

  acc  = uint64(vals[31]&mask) << have
  have += bitWidth
  if have >= 32  
    binary.LittleEndian.PutUint32(dest[pos:pos+4], uint32(acc))
    pos += 4
    acc >>= 32
    have -= 32
   

  // have == 0; for all bitWidths
 

Next, we want to specialize not just for 32 input values, but also for each of the 32 bit widths. Can we do better than hand-copying bitpack32Unrolled 32 times (= 8192 lines of Go code)? Yes, we can use Go generics to help us with the code generation! In Go, array types like [4]byte (not slices like []byte!) contain the length of the array as part of their type, meaning [1]byte (an array of length 1) is a different type than [2]byte. Instead of passing the bit width as a function parameter, we can declare 32 different types (one for each bit width) and recover the bit width (at compile time!) from the type system:

type bitWidthT interface  
  [1]byte   [2]byte   [3]byte   [4]byte   [5]byte  
  [6]byte   [7]byte   [8]byte   [9]byte   [10]byte  
  [11]byte   [12]byte   [13]byte   [14]byte   [15]byte  
  [16]byte   [17]byte   [18]byte   [19]byte   [20]byte  
  [21]byte   [22]byte   [23]byte   [24]byte   [25]byte  
  [26]byte   [27]byte   [28]byte   [29]byte   [30]byte  
  [31]byte   [32]byte
 

func bitpack32Unrolled[T bitWidthT](dest []byte, vals *[32]uint32)  
  var zero T
  bitWidth := len(zero)                  // known at compile time
  dest = dest[: 4*bitWidth : 4*bitWidth] // make cap known at compile time
  mask := uint32(1<<bitWidth - 1)
  var acc uint64
  var have, pos int
  // Manually unrolled loop starts here.
  // Each iteration is identical except for the vals[x] index.
  acc  = uint64(vals[0]&mask) << have
  have += bitWidth
  if have >= 32  
    binary.LittleEndian.PutUint32(dest[pos:pos+4], uint32(acc))
    pos += 4
    acc >>= 32
    have -= 32
   

  // vals[1] .. vals[31] elided for brevity
 

When we instantiate bitpack32Unrolled[bitWidthT] with all 32 different types ([1]byte, [2]byte, , [32]byte), the compiler substitutes the bitWidthT type parameter and produces 32 copies of the function, which we can find in our compiled executable with names like github.com/Debian/dcs/internal/turbopfor/pforenc.bitpack32Unrolled[go.shape.[12]uint8]. The shape of a generic type is based on its memory layout, so a shape for [1]byte must be different than the shape for [2]byte. Because the bitWidth is now known at compile time, the Go compiler can generate close to the optimal machine code for each bit width, which we can confirm using go tool objdump. The code is branchless (after the one bounds check per 32 values) and aside from the loads and stores (from/to memory) consists only of shifts and bit operations, all with constant operands:

% go test -c && go tool objdump -S pforenc.test
[ ]
TEXT github.com/Debian/dcs/internal/turbopfor/pforenc.bitpack32Unrolled[go.shape.[28]uint8](SB) /home/michael/dcs/internal/turbopfor/pforenc/bitpackunroll.go
func bitpack32Unrolled[T bitWidthT](dest []byte, vals *[32]uint32)  
  0x660580              55                      PUSHQ BP
  0x660581              4889e5                  MOVQ SP, BP
  0x660584              48895c2418              MOVQ BX, 0x18(SP)
        dest = dest[: 4*bitWidth : 4*bitWidth] // make cap known at compile time
  0x660589              4883ff70                CMPQ DI, $0x70
  0x66058d              0f820b030000            JB 0x66089e
        acc  = uint64(vals[0]&mask) << have
  0x660593              8b06                    MOVL 0(SI), AX
  0x660595              25ffffff0f              ANDL $0xfffffff, AX
        acc  = uint64(vals[1]&mask) << have
  0x66059a              8b4e04                  MOVL 0x4(SI), CX
  0x66059d              81e1ffffff0f            ANDL $0xfffffff, CX
  0x6605a3              48c1e11c                SHLQ $0x1c, CX
  0x6605a7              4809c8                  ORQ CX, AX
                acc >>= 32
  0x6605aa              4889c1                  MOVQ AX, CX
  0x6605ad              48c1e820                SHRQ $0x20, AX
                binary.LittleEndian.PutUint32(dest[pos:pos+4], uint32(acc))
  0x6605b1              90                      NOPL
        b[0] = byte(v)
  0x6605b2              890b                    MOVL CX, 0(BX)
        acc  = uint64(vals[2]&mask) << have
  0x6605b4              8b4e08                  MOVL 0x8(SI), CX
  0x6605b7              81e1ffffff0f            ANDL $0xfffffff, CX
  0x6605bd              48c1e118                SHLQ $0x18, CX
  0x6605c1              4809c1                  ORQ AX, CX
                acc >>= 32
  0x6605c4              4889c8                  MOVQ CX, AX
  0x6605c7              48c1e920                SHRQ $0x20, CX
                binary.LittleEndian.PutUint32(dest[pos:pos+4], uint32(acc))
  0x6605cb              90                      NOPL
        b[0] = byte(v)
  0x6605cc              894304                  MOVL AX, 0x4(BX)

Now we need to actually call bitpack32 from the general bitpack function:

func bitpack(dest []byte, vals []uint32, bitWidth int) []byte  
  if bitWidth == 0  
    return dest // no payload, sparse block with only exceptions
   
  if len(vals) >= 32  
    size := 4 * bitWidth
    for len(vals) >= 32  
      existing := len(dest)
      dest = slices.Grow(dest, size)[:existing+size]
      bitpack32(dest[existing:] /*append*/, (*[32]uint32)(vals), bitWidth)
      vals = vals[32:]
     
   
  mask := uint32(1<<bitWidth - 1)
  var acc uint64
  var have int
  for _, val := range vals  
    acc  = uint64(val&mask) << have
    have += bitWidth
    for have >= 32  
      dest = binary.LittleEndian.AppendUint32(dest, uint32(acc))
      acc >>= 32
      have -= 32
     
   
  for have > 0  
    dest = append(dest, byte(acc))
    acc >>= 8
    have -= 8
   
  return dest
 

func bitpack32(dest []byte, vals *[32]uint32, bitWidth int)  
  switch bitWidth  
  case 1: bitpack32Unrolled[[1]byte](dest, vals)
  case 2: bitpack32Unrolled[[2]byte](dest, vals)
  case 3: bitpack32Unrolled[[3]byte](dest, vals)
  case 4: bitpack32Unrolled[[4]byte](dest, vals)
  case 5: bitpack32Unrolled[[5]byte](dest, vals)
  case 6: bitpack32Unrolled[[6]byte](dest, vals)
  case 7: bitpack32Unrolled[[7]byte](dest, vals)
  case 8: bitpack32Unrolled[[8]byte](dest, vals)
  case 9: bitpack32Unrolled[[9]byte](dest, vals)
  case 10: bitpack32Unrolled[[10]byte](dest, vals)
  case 11: bitpack32Unrolled[[11]byte](dest, vals)
  case 12: bitpack32Unrolled[[12]byte](dest, vals)
  case 13: bitpack32Unrolled[[13]byte](dest, vals)
  case 14: bitpack32Unrolled[[14]byte](dest, vals)
  case 15: bitpack32Unrolled[[15]byte](dest, vals)
  case 16: bitpack32Unrolled[[16]byte](dest, vals)
  case 17: bitpack32Unrolled[[17]byte](dest, vals)
  case 18: bitpack32Unrolled[[18]byte](dest, vals)
  case 19: bitpack32Unrolled[[19]byte](dest, vals)
  case 20: bitpack32Unrolled[[20]byte](dest, vals)
  case 21: bitpack32Unrolled[[21]byte](dest, vals)
  case 22: bitpack32Unrolled[[22]byte](dest, vals)
  case 23: bitpack32Unrolled[[23]byte](dest, vals)
  case 24: bitpack32Unrolled[[24]byte](dest, vals)
  case 25: bitpack32Unrolled[[25]byte](dest, vals)
  case 26: bitpack32Unrolled[[26]byte](dest, vals)
  case 27: bitpack32Unrolled[[27]byte](dest, vals)
  case 28: bitpack32Unrolled[[28]byte](dest, vals)
  case 29: bitpack32Unrolled[[29]byte](dest, vals)
  case 30: bitpack32Unrolled[[30]byte](dest, vals)
  case 31: bitpack32Unrolled[[31]byte](dest, vals)
  case 32: bitpack32Unrolled[[32]byte](dest, vals)
   
 

Encoding remainder blocks is quite a bit faster (full blocks use the vertical layout anyway):

% benchstat -filter '/impl:go /n:160 .unit:(Mval/s)' baseline.txt bench.txt
goos: linux
goarch: amd64
pkg: github.com/Debian/dcs/internal/turbopfor/pforenc
cpu: AMD Ryzen 9 9950X3D 16-Core Processor
                           baseline.txt               bench.txt               
                              Mval/s        Mval/s     vs base                
vals=bitpacking-bw1          751.2   3%   1120.5   0%  +49.15% (p=0.002 n=6)
vals=bitpacking-bw2          716.8   2%   1176.0   0%  +64.07% (p=0.002 n=6)
vals=bitpacking-bw7          700.0   1%   1078.5   0%  +54.08% (p=0.002 n=6)
vals=bitpacking-bw1-exc      524.8   1%    736.8   0%  +40.40% (p=0.002 n=6)
vals=bitpacking-bw2-exc      543.7   1%    758.2   0%  +39.46% (p=0.002 n=6)
vals=bitpacking-bw7-exc      566.7   1%    787.7   0%  +38.99% (p=0.002 n=6)
vals=bitpacking-vb-exc       442.6   1%    616.5   0%  +39.29% (p=0.002 n=6)
vals=sparse-exc              532.4   0%    787.8   0%  +47.97% (p=0.002 n=6)
vals=sparse-vb-exc           408.9   1%    597.8   0%  +46.20% (p=0.002 n=6)
vals=debian-mix              559.5   0%    783.8   9%  +40.09% (p=0.002 n=6)

This performance win comes at the cost of binary size increase. In this case, the .text section (executable code) grows by about 20 KB and the .gopclntab section grows by another 26 KB. Definitely a price I am very willing to pay, but the case might not be as clear in all circumstances.

Optimization: Bigger strides with SIMD Even without reaching for SIMD instructions, a TurboPFor implementation can be made faster by making it work bigger strides. Take this code from the goturbopfor teaching decoder which counts the number of exceptions by checking if each value s bit is set in the exception bitmap:

case blockBitpackingExceptions:
  bx, input := input[0], input[1:]
  n := len(output)

  exmap, input := input, input[(n+7)/8:]
  nex := 0 // number of exceptions
  for i := range n  
    if exmap[i/8]&(1<<uint(i%8)) != 0  
      nex++
     
   
  exceptions := d.scratch[:nex]

We can use the bits.OnesCount64 functions to count ones bits in the exception bitmap, 64 values at a time. For remainder blocks, the rest is processed 8 values (1 byte) at a time:

i := 0
for ; i+8 <= n/8; i += 8  
  xm8 := binary.LittleEndian.Uint64(exmap[i:])
  nex += bits.OnesCount64(xm8)
 
for ; i < (n+7)/8; i++  
  xmb := exmap[i]
  // Clear the bits which do not belong to the exception map:
  if rem := n - i*8; rem < 8  
    xmb &= 1<<rem - 1
   
  // Go compiles OnesCount32 into an intrinsic,
  // but not OnesCount8, so we convert to uint32:
  nex += bits.OnesCount32(uint32(xmb))
 

OnesCount64 uses a 64-bit register. For comparison, AVX2 SIMD instructions use 256-bit registers (= 8 uint32) and AVX512 SIMD instructions use 512-bit registers. In the following sections, we will first set up our build tags for conditional compilation to use a trivial SIMD instruction, then walk through an AVX2 and AVX512 SIMD kernel.

SIMD build tags Let s assume we have the following scalar code: constant.go:

package pfordec

func fillConstant(output []uint32, val uint32)  
  for i := range output  
    output[i] = val
   
 

To increase throughput, we can use AVX2 instructions if they are available on the CPU on which the program runs, i.e. using runtime dispatch. We ll first rename fillConstant to fillConstantScalar (it s now the fallback path): constant.go:

package pfordec

func fillConstantScalar(output []uint32, val uint32)  
  for i := range output  
    output[i] = val
   
 

Next, we ll supply two different implementations (constant_nosimd.go and constant_amd64.go), the latter of which is selected when compiling for GOARCH=amd64 with GOEXPERIMENT=simd (the latter will hopefully be dropped in a later version of Go). The nosimd variant just dispatches to the fillConstantScalar, which will likely be inlined:

//go:build !goexperiment.simd   !amd64

package pfordec

func fillConstant(output []uint32, val uint32)  
  fillConstantScalar(output, val)
 

The constant_amd64.go variant assigns the hasAVX2 global variable by doing a CPUID check and then jumps to the scalar fallback if !hasAVX2, i.e. the CPU is too old:

//go:build goexperiment.simd && amd64

package pfordec

import "simd/archsimd"

var hasAVX2 = archsimd.X86.AVX2()

func fillConstant(output []uint32, val uint32)  
  if !hasAVX2  
    fillConstantScalar(output, val)
    return
   
  val8 := archsimd.BroadcastUint32x8(val)
  i := 0
  for ; i+8 <= len(output); i += 8  
    val8.StoreArray((*[8]uint32)(output[i : i+8]))
   
  // use the scalar implementation for the last <= 7 elements
  fillConstantScalar(output[i:], val)
 

We can go one step further by conditionally compiling const hasAVX2 = true when GOAMD64 is set to v3 or higher (i.e. the amd64.v3 build tag is set). As a practical example from Debian Code Search, we currently need the following checks / dispatches:

code	function	vector instruction set	GOAMD64
encoder	bitpack256v	AVX2	GOAMD64=v3
encoder	exbitmap	AVX512	GOAMD64=v4
encoder	scan	AVX512+VBMI+GFNI+BITALG	n/a
decoder	bitunpack	AVX2	GOAMD64=v3
decoder	bitunpack256v32	AVX2	GOAMD64=v3
decoder	bitunpack256v32Ex	AVX512	GOAMD64=v4

In DCS, the effect is measurably positive, but small.

The 256 uint32 vertical layout First, here is the layout explanation from my 2019 TurboPFor analysis blog post:

In regular (non-SIMD) bitpacking, integers are stored on disk one after the other, padded to a full byte, as a byte is the smallest addressable unit when reading data from disk. For example, if you bitpack only one 3 bit int, you will end up with 5 bits of padding. SIMD bitpacking works like regular bitpacking, but processes 8 uint32 little-endian values at the same time, leveraging the AVX instruction set. The following illustration shows the order in which 3-bit integers are decoded from disk:

The scalar implementation uses an array of 8 uint64 to process 8 values at a time:

func bitunpack256v32(input []byte, dest []uint32, bitWidth int) (read int)  
  mask := uint64(1)<<bitWidth - 1
  orig := len(input)
  var bits uint
  var acc [8]uint64 // accumulator: current+next bits
  for op := 0; op < len(dest);  
    if bits < uint(bitWidth)  
      // read 8 more uint32s
      for i := range 8  
        acc[i]  = uint64(binary.LittleEndian.Uint32(input)) << bits
        input = input[4:]
       
      bits += 32
     
    for i := range 8  
      dest[op] = uint32(acc[i] & mask)
      op++
      acc[i] >>= bitWidth
     
    bits -= uint(bitWidth)
   
  return orig - len(input)
 

The SIMD version also processes 8 values, but without a for i := range 8 loop! One difference is that we no longer have the luxury of using uint64 for acc (holding rest and current bits); because AVX2 registers only fit 8 uint32 (not 8 uint64). Instead, we split acc into rest8 and cur8.

func bitunpack256v32(fullinput []byte, fulldest []uint32, bitWidth int) (read int)  
  dest := fulldest[:256]
  if bitWidth == 0  
    clear(dest)
    return 0
   
  n := 32 * int(bitWidth)
  input := fullinput[:n] // tell the Go compiler how long the input is
  mask8 := archsimd.BroadcastUint32x8(uint32(1)<<bitWidth - 1)
  bitWidth8 := archsimd.BroadcastUint32x8(uint32(bitWidth))
  var bits uint
  pos := 0
  // var acc [8]uint64
  var rest8 archsimd.Uint32x8
  var cur8 archsimd.Uint32x8
  for op := 0; op < 256; op += 8  
    if bits < uint(bitWidth)  
      // read 8 more uint32s
      // acc[i]  = uint64(binary.LittleEndian.Uint32(input)) << bits
      next := archsimd.LoadUint8x32(input[pos : pos+32]).ReshapeToUint32s()
      pos += 32  // input = input[4:]
      cur8 = rest8.Or(next.ShiftAllLeft(uint64(bits)))
      // acc[i] >>= bitWidth
      rest8 = next.ShiftAllRight(uint64(uint(bitWidth) - bits))
      bits += 32
      else  
      cur8 = rest8
      // acc[i] >>= bitWidth
      rest8 = rest8.ShiftRight(bitWidth8)
     
    // dest[op] = uint32(acc[i] & mask)
    cur8.And(mask8).Store(dest[op : op+8])
    bits -= uint(bitWidth)
   
  return n
 

The SIMD version benchmarks about 3x as fast as the scalar version. Another significant speedup is to use generics for bit width specialization for this SIMD kernel so that bitWidth becomes a compile-time constant and the compiler can generate better code.

Positional Popcount For my TurboPFor encoder, I implemented the same techniques as described above:

1. Bitpack full blocks with SIMD (AVX2)
2. Gather exceptions using SIMD (AVX512)
3. Use generics to specialize per bit width

These changes are sufficient to roughly match the cgo performance, but then Claude Fable 5 found another 2x speed-up on top of that! The key observation is that once encoding blocks is fast, the preceding step of scanning the input values to decide which block type to use becomes the bottleneck. Here is the encoder s main encode function, which first does one pass over the input values (scan) and then prices all different block types at all relevant bit widths (requires fast access to the scan histogram):

func (be *BlockEncoder) encode(dest []byte, vals []uint32, layout blockLayout) []byte  
  var stats stats
  scan(&stats, vals) // gathers statistics from every value in vals
  bitWidth := bits.Len32(stats.or)
  if stats.or == stats.and  
    return be.encodeConstant(dest, vals, bitWidth)
   
  n := len(vals)
  // bitpacking is the default, unless we find a more efficient block type.
  bestType := blockBitpacking
  bestB := bitWidth
  best := priceBitpack(n, bitWidth, layout)

  // Walk from high bitWidths to low: to break ties, we prefer
  // the encoding with fewer exceptions (for faster decoding).
  for b := bitWidth - 1; b >= 0; b--   // up to 32 iterations
    nex := int(stats.cnt[b])
    size := priceBitpackExceptions(n, b, bitWidth, nex, layout)
    if size < best  
      bestType = blockBitpackingExceptions
      bestB = b
      best = size
     
    // Over-approximate the number of VB bytes.
    vb := nex + // exceptions using 1, 2, 3, 4, or 5 VB bytes
      int(stats.cnt[b+7]+ // exceptions using 2, 3, 4, or 5 VB bytes
        stats.cnt[b+14]+ // exceptions using 3, 4, or 5 VB bytes
        stats.cnt[b+19]+ // exceptions using 4 or 5 VB bytes
        stats.cnt[b+24]) // exceptions using 5 VB bytes
    size = headerBytes + headerExBytes + payloadBytes(n, b, layout) + vb + nex
    if size < best  
      bestType = blockBitpackingVBExceptions
      bestB = b
      best = size
     
   
  switch bestType  
  case blockBitpacking:
    return be.encodeBitpack(dest, vals, layout, bitWidth)
  case blockBitpackingExceptions:
    return be.encodeBitpackExc(dest, vals, layout, bestB, bitWidth-bestB)
  case blockBitpackingVBExceptions:
    return be.encodeBitpackVBExc(dest, vals, layout, bestB, int(stats.cnt[bestB]))
  default:
    panic("BUG: bestType not implemented")
   
 

I ll show you a slightly shortened version of scan, the function which is the bottleneck:

type stats struct  
  // cnt[n] = how many values where bits.Len32(val)>n,
  // i.e. how many exceptions are required for bitWidth=n.
  // Padded so that cnt[b+24] is always in bounds.
  cnt [32 + 24]uint32
 

func scan(output *stats, vals []uint32)  
  for _, val := range vals  
    for b := range bits.Len32(val)  
      output.cnt[b]++ // b bits are not enough to store val
     
   
 

Let s consider the following 3 example values to understand the resulting cnt:

input	input (bin)	bits.Len32
23	0b0000010111	5
5	0b0000000101	3
666	0b1010011010	10

The resulting cnt exception count histogram would contain (cnt shortened to c):

c[0]	c[1]	c[2]	c[3]	c[4]	c[5]	c[6]	c[7]	c[8]	c[9]	c[10]
3	3	3	2	2	1	1	1	1	1	0

In words, this means that at bit width 10, we could encode all the values without any exceptions. But most values do not need 10 bits, so a bit width of 5 would be more efficient, but requires storing one exception. Encoding at bit width 4 requires 2 exceptions, and so on. The scan function above is intentionally kept simple for illustration. We can make it faster by moving the per-bit-width loop outside the per-element loop. The fast version still needs about 12 instructions per value. With SIMD, we can reduce this to by 8x to only 1.5 instructions per value!

The trick: smear masks enable positional popcount The trick is to turn each input value into its smear mask (imagine taking the first 1 bit and smearing it across the remaining positions). Here are the smear masks for our example:

input	input (bin)	bits.Len32	smear mask
23	0b0000010111	5	0b0000011111
5	0b0000000101	3	0b0000000111
666	0b1010011010	10	0b1111111111

Turning a value into its smear mask is computationally cheap: Go implements BitLen(x) (functions like bits.Len32) by calculating 32 - LZCNT(x). We can calculate the smear mask of a value with ^uint32(0) >> LZCNT(x), i.e. starting with a 32-one-bits mask and shifting it by the number of leading zeros. Now, to obtain e.g. cnt[4], we can count the 1 bits at bit position 4 of all input values. The POPCNT instruction counts bits very efficiently, but it counts one bits within a register, so it counts rows, not columns. Counting columns is called Positional Population Count. I found the following papers that describe positional popcount with SIMD:

• 2019: Efficient Computation of Positional Population Counts Using SIMD Instructions (Klarqvist, Mu a, Lemire) introduces an AVX512 implementation using Carry-Save Adder (CSA) networks, but in my testing, that approach is slower for TurboPFor encoding.
• 2024: Histogramming bytes with positional popcount (GF2P8AFFINEQB edition) (Harold Aptroot) shared a C++ implementation illustrating the technique I am using. If you are not familiar with how C++ vector intrinsics look, take a look at this example.
• 2025: Faster Positional-Population Counts for AVX2, AVX-512, and ASIMD (Clausecker, Lemire, Schintke) improves performance over the 2019 paper, but cites the Aptroot blog post as Future Work , promising even faster processing for smaller inputs.

Positional Popcount: a visual explanation To understand the AVX512 implementation of positional popcount, I found it most helpful to visualize an AVX512 register (512 bits, i.e. 64 bytes). The graphic below uses the Uint64x8 layout, meaning it divides the register into 8 lanes of 64 bits (= 8 bytes) each. This illustration shows the whole process: how uint32s are loaded into an AVX512 register (all 4 of its bytes, in sequence) and where we end up, i.e. the 32 positional popcounts: Let s break down this process into its individual steps. First, we turn each loaded value into its smear mask as explained above. The VPOPCNTB vector instruction calculates POPCNT (1 byte) of 64 bytes at once, but first we need to shuffle the bytes inside the register: in load order, we have a full uint32 (4 bytes), followed by another uint32, per lane. First, we permute the bytes (VPERMB) such that all the first bytes of each value end up in one lane ( transpose the bytes ): Next, we transpose the bits using the GF2P8AFFINEQB instruction, which sounds scary but turns out to be quite flexible for bit manipulation of all kinds. The GF2P8AFFINEQB instruction is also the star of the show in Go s Green Tea Garbage Collector (2025). Here is the bit transpose, shown in the AVX512 register layout (see below for a different layout): I found it easier to understand the transpose step when arranging the 8 bytes of lane 0 from top-to-bottom (instead of left-to-right), because then it looks like a 90 degree clockwise rotation: Now we can use VPOPCNTB to count the bits in all 64 bytes at once: After all loop iterations (processing 16 values each) are done, we add the two groups (first 8 values, second 8 values) to obtain the 32 exception counts:

Positional Popcount: Go SIMD Here is the Go code that implements what I described visually above:

func scanSIMD(output *stats, vals []uint32)  
  ones16 := archsimd.BroadcastUint32x16(^uint32(0)) // 16 32-one-bits masks
  shuffle := archsimd.LoadUint8x64Array(&scanShuffle)
  units := archsimd.LoadUint8x64Array(&scanUnits)
  var acc archsimd.Uint8x64
  idx := 0
  for ; idx+16 <= len(vals); idx += 16  
    v := archsimd.LoadUint32x16(vals[idx : idx+16])
    // Replace all values with their smear masks.
    smear := ones16.ShiftRight(v.LeadingZeros()).ReshapeToUint8s()
    // Transpose: shuffle the bytes, then transpose the bits.
    matrices := smear.Permute(shuffle).ReshapeToUint64s()
    transposed := units.GaloisFieldAffineTransform(matrices, 0)
    // Popcount 64 bytes at once into the accumulator.
    acc = acc.Add(transposed.OnesCount())
   
  // Store the accumulator into output.cnt:
  // Widen the two groups of byte counts to uint16 lanes (so that
  // 128+128 = 256 fits), fold them into cnt[b] for b=0..31,
  // then widen again to the uint32 lanes of output.cnt.
  sum := acc.GetLo().ExtendToUint16().Add(acc.GetHi().ExtendToUint16())
  sum.GetLo().ExtendToUint32().Store(output.cnt[0:16])
  sum.GetHi().ExtendToUint32().Store(output.cnt[16:32])
  // scalar tail for the 0..15 remaining values
  for _, val := range vals[idx:]  
    for b := range bits.Len32(val)  
      output.cnt[b]++
     
   
 

Have a look at the commit introducing positional popcount to DCS for the full code (including shuffle tables and ISA checks) as well as the detailed benchmark results.

Go even faster? The SIMD optimizations I showed above beat the cgo TurboPFor library that Debian Code Search used before. When comparing apples to apples, i.e. backporting the AVX512 kernels and positional popcount technique to C TurboPFor, Go benchmarks a little slower at 1.4x C. Could we make my Go TurboPFor implementation even faster, to truly match the C speed? Yes! But also no. Let me explain:

1. We could use more SIMD instructions to remove all code that still processes one value at a time. For example, in my encoder s encodeBitpackVBExc function. Or we could price all bit widths concurrently in encode. Or in the decoder s exception apply code path.
   But all of these SIMD instructions make understanding (and changing) the code harder, so I am cautious regarding which ones I introduce.
2. A big part of the performance gap is due to Go s bounds checks. While it costs performance, bounds checking is great for safety, so I will not turn off bounds checking. The Go compiler eliminates a number of bounds checks when it understands it s safe to do so. One optimization avenue could be to make the prove pass in the Go compiler smarter to eliminate more bounds checks.
3. When doing mid-stack inlining (proposal #19348) (2017), Go sometimes needs to put NOP instructions into the binary so that it can attach inlining markers. For dispatch-bound functions, these extra NOPs can measurable slow down execution.
4. The Go compiler currently allows specifying the architecture (GOARCH=amd64) and microarchitecture (GOAMD64=v3), but not a specific CPU architecture (like AMD Zen 4). Therefore, CPU-specific workarounds for one vendor affect all the generated code. The specific one I encountered in my code is that the Go compiler emits XORL CX,CX before every POPCNT to break a false-output-dependency from the Intel Sandy Bridge Skylake era, which is unnecessary on AMD Zen CPUs.
   I suspect that Go intentionally does not offer this level of customizability.
5. After all of the above points are addressed, what remains is better code generation in specific cases. To illustrate what I mean, consider the example of incrementing a loop variable, where Go re-derives an index every time:
   Go: POPCNTL; ADDQ DI,CX; LEAQ (base)(CX*4) (3 instructions)
   clang: popcnt; lea rax,[rax+4*rdi] (2 instructions)
   Depending on the specific case, improving the compiler might be easy or prohibitively complex. Often, such improvements are hard to measure conclusively.

Conclusion Go s SIMD support makes available in Go code without having to resort to cgo or assembly a powerful part of modern CPUs which allows speeding up the kind of computation that TurboPFor needs by an order of magnitude! I found it very valuable to use a coding agent (Claude Code, with Opus 5 and Fable 5 in this case) to help with the many tedious parts of such performance work (and still it took me weeks!). The LLM can read objdump output much faster than I can, can see patterns and correlations I might never identify, never becomes frustrated after a compiler error or runtime panic, and never runs out of patience to run one more experiment, as long as I give it measurable and reachable goals. The performance of the SIMD code which one can get from the Go compiler is pretty close to what a good C compiler like clang provides. The CPU performance counters show value decoding speeds of 7 instructions/cycle (IPC) on a machine where the maximum is 8 IPC. To me, SIMD support is a very welcome addition to Go.

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. If you like this or other open-source work I do, you can sponsor me at GitHub.

webstatus=$(curl --head --silent --no-fail --output /dev/null \
                 --write-out "% http_code " https://r2u.stat.illinois.edu   true)
if test "$ webstatus " = "200"; then
    echo "The r2u repository is reachable."
else
    extip=$(curl --silent https://ipinfo.io/ip)
    echo "::notice::The primary r2u repository is **not reachable** from $ extip ."
fi

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. If you like this or other open-source work I do, you can now sponsor me at GitHub.