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LWN on Linux: LTS, API, Pointer Leaks and Linux Plumbers Conference (LPC)

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Linux
  • Cramming features into LTS kernel releases

    While the 4.14 development cycle has not been the busiest ever (12,500 changesets merged as of this writing, slightly more than 4.13 at this stage of the cycle), it has been seen as a rougher experience than its predecessors. There are all kinds of reasons why one cycle might be smoother than another, but it is not unreasonable to wonder whether the fact that 4.14 is a long-term support (LTS) release has affected how this cycle has gone. Indeed, when he released 4.14-rc3, Linus Torvalds complained that this cycle was more painful than most, and suggested that the long-term support status may be a part of the problem. A couple of recent pulls into the mainline highlight the pressures that, increasingly, apply to LTS releases.

    As was discussed in this article, the 4.14 kernel will include some changes to the kernel timer API aimed at making it more efficient, more like contemporary in-kernel APIs, and easier to harden. While API changes are normally confined to the merge window, this change was pulled into the mainline for the 4.14-rc3 release. The late merge has led to a small amount of grumbling in the community.

  • Improving the kernel timers API

    The kernel's timer interface has been around for a long time, and its API shows it. Beyond a lack of conformance with current in-kernel interface patterns, the timer API is not as efficient as it could be and stands in the way of ongoing kernel-hardening efforts. A late addition to the 4.14 kernel paves the way toward a wholesale change of this API to address these problems.

  • What's the best way to prevent kernel pointer leaks?

    An attacker who seeks to compromise a running kernel by overwriting kernel data structures or forcing a jump to specific kernel code must, in either case, have some idea of where the target objects are in memory. Techniques like kernel address-space layout randomization have been created in the hope of denying that knowledge, but that effort is wasted if the kernel leaks information about where it has been placed in memory. Developers have been plugging pointer leaks for years but, as a recent discussion shows, there is still some disagreement over the best way to prevent attackers from learning about the kernel's address-space layout.

    There are a number of ways for a kernel pointer value to find its way out to user space, but the most common path by far is the printk() function. There are on the order of 50,000 printk() calls in the kernel, any of which might include the value of a kernel pointer. Other places in the kernel use the underlying vsprintf() mechanism to format data for virtual files; they, too, often leak pointer values. A blanket ban on printing pointer values could solve this problem — if it could be properly enforced — but it would also prevent printing such values when they are really needed. Debugging kernel problems is one obvious use case for printing pointers, but there are others.

  • Continuous-integration testing for Intel graphics

    Two separate talks, at two different venues, give us a look into the kinds of testing that the Intel graphics team is doing. Daniel Vetter had a short presentation as part of the Testing and Fuzzing microconference at the Linux Plumbers Conference (LPC). His colleague, Martin Peres, gave a somewhat longer talk, complete with demos, at the X.Org Developers Conference (XDC). The picture they paint is a pleasing one: there is lots of testing going on there. But there are problems as well; that amount of testing runs afoul of bugs elsewhere in the kernel, which makes the job harder.

    Developing for upstream requires good testing, Peres said. If the development team is not doing that, features that land in the upstream kernel will be broken, which is not desirable. Using continuous-integration (CI) along with pre-merge testing allows the person making a change to make sure they did not break anything else in the process of landing their feature. That scales better as the number of developers grows and it allows developers to concentrate on feature development, rather than bug fixing when someone else finds the problem. It also promotes a better understanding of the code base; developers learn more "by breaking stuff", which lets them see the connections and dependencies between different parts of the code.

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