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LWN's Latest Kernel Reports (Paywall Lapsed)

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Linux
  • Some 5.14 development statistics

    The 5.14 kernel was released on August 29 after a nine-week development period. This cycle was not as active as its predecessor, which set a record for the number of developers involved, but there was still a lot going on and a number of long-awaited features were merged. Now that the release is out, the time has come for our traditional look at where the code in 5.14 came from and how it got there.

    To create 5.14, the kernel community applied 14,735 non-merge changesets from 1,912 developers; 261 of those developers made their first kernel contribution during this cycle. There were 861,000 lines of code added to the kernel and 321,000 lines removed, for a net growth of 540,000 lines.

  • Nftables reaches 1.0

    The Linux kernel is a fast-moving project, but change can still be surprisingly slow to come at times. The nftables project to replace the kernel's packet-filtering subsystem has its origins in 2008, but is still not being used by most (or perhaps even many) production firewalls. The transition may be getting closer, though, as highlighted by the release of nftables 1.0.0 on August 19.
    The first public nftables release was made by Patrick McHardy in early 2009. At that time, the kernel had a capable packet-filtering subsystem in the form of iptables, of course, that was in widespread use, but there were a number of problems driving a change. These include the fact that the kernel had (and still has) more than one packet-filtering mechanism: there is one for IPv4, another for IPv6, yet another for ARP, and so on. Each of those subsystems is mostly independent, with a lot of duplicated code. Beyond that, iptables contains an excessive amount of built-in protocol knowledge and suffers from a difficult API that, among other things, makes it impossible to update a single rule without replacing the entire set.

    The core idea behind nftables was to throw away all of that protocol-aware machinery and replace it with a simple virtual machine that could be programmed from user space. Administrators would still write rules referring to specific packet-header fields and such, but user-space tooling would translate those rules into low-level fetch and compare operations, then load the result into the kernel. That resulted in a smaller packet-filtering engine that was also far more flexible; it also had the potential to perform better. It looked like a win, overall, once the minor problem of transitioning a vast number of users had been overcome.

  • Not-a-GPU accelerator drivers cross the line

    As a general rule, the kernel community is happy to merge working device drivers without much concern for the availability of any associated user-space code. What happens in user space is beyond the kernel's concern and unaffected by the kernel's license. There is an exception, though, in the form of drivers for graphical processors (GPUs), which cannot be merged in the absence of a working, freely-licensed user-space component. The question of which drivers are subject to that rule has come up a few times in recent years; that discussion has now come to a decision point with an effort to block some Habana Labs driver updates from entry into the 5.15 kernel.

    The GPU-driver rule is the result of a "line in the sand" drawn by direct-rendering (DRM) maintainer Dave Airlie in 2010. The kernel side of most GPU drivers is a simple conduit between user space and the device; it implements something similar to a network connection. The real complexity of these drivers is in the user-space component, which uses the kernel-provided channel to control the GPU via a (usually) proprietary protocol. The DRM maintainers have long taken the position that, without a working user-space implementation, they are unable to judge, maintain, or test the kernel portion of the driver. They have held firm for over a decade now, and feel that this policy is an important part of the progress that this subsystem has made over that time.

    At its core, a GPU is an accelerator that is optimized to perform certain types of processing much more quickly than even the fastest CPU can. Graphics was the first domain in which these accelerators found widespread use, but it is certainly not the last. More recently, there has been a developing market in accelerators intended to perform machine-learning tasks; one of those, the Habana Gaudi, is supported by the Linux kernel.

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today's howtos

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