Changes ------- This is v3 of sched_ext (SCX) patchset. The followings are changes from v2 (http://lkml.kernel.org/r/20230128001639.3510083-1-tj@xxxxxxxxxx). - ops.set_weight() added to allow BPF schedulers to track weight changes without polling p->scx.weight. - scx_bpf_task_cgroup() kfunc added to allow BPF scheduler to reliably determine the current cpu cgroup under rq lock protection. This required improving the kf_mask SCX operation verification mechanism and adding 0023-sched_ext-Track-tasks-that-are-subjects-of-the-in-fl.patch. - Updated to use the latest BPF improvements including KF_RCU and the inline iterator. - scx_example_flatcg added to 0024-sched_ext-Add-cgroup-support.patch. It uses the new BPF RB tree support to implement flattened cgroup hierarchy. - A DSQ now also contains an rbtree so that it can be used to implement vtime based scheduling among tasks sharing a DSQ conveniently and efficiently. For more details, see 0029-sched_ext-Add-vtime-ordered-priority-queue-to-dispat.patch. All eligible example schedulers are updated to default to weighted vtime scheduilng. - atropos scheduler's userspace code is substantially restructred and rewritten. The binary is renamed to scx_atropos and can auto-config the domains according to the cache topology. - Various other example scheduler updates including scx_example_dummy being renamed to scx_example_simple, the example schedulers defaulting to enabling switch_all and clarifying performance expectation of each example scheduler. - A bunch of fixes and improvements. Please refer to each patch for details. v1 (http://lkml.kernel.org/r/20221130082313.3241517-1-tj@xxxxxxxxxx) -> v2: - Rebased on top of bpf/for-next - a5f6b9d577eb ("Merge branch 'Enable struct_ops programs to be sleepable'"). There were several missing features including generic cpumask helpers and sleepable struct_ops operation support that v1 was working around. The rebase gets rid of all SCX specific temporary helpers. - Some kfunc helpers are context-sensitive and can only be called from specific operations. v1 didn't restrict kfunc accesses allowing them to be misused which can lead to crashes and other malfunctions. v2 makes more kfuncs safe to be called from anywhere and implements per-task mask based runtime access control for the rest. The longer-term plan is to make the BPF verifier enforce these restrictions. Combined with the above, sans mistakes and bugs, it shouldn't be possible to crash the machine through SCX and its helpers. - Core-sched support. While v1 implemented the pick_task operation, there were multiple missing pieces for working core-sched support. v2 adds 0027-sched_ext-Implement-core-sched-support.patch. SCX by default implements global FIFO ordering and allows the BPF schedulers to implement custom ordering via scx_ops.core_sched_before(). scx_example_qmap is updated so that the five queues' relative priorities are correctly reflected when core-sched is enabled. - Dropped balance_scx_on_up() which was called from put_prev_task_balance(). UP support is now contained in SCX proper. - 0002-sched-Encapsulate-task-attribute-change-sequence-int.patch adds SCHED_CHANGE_BLOCK() which encapsulates the preparation and restoration sequences used for task attribute changes. For SCX, this replaces sched_deq_and_put_task() and sched_enq_and_set_task() from v1. - 0011-sched-Add-reason-to-sched_move_task.patch dropped from v1. SCX now distinguishes cgroup and autogroup tg's using task_group_is_autogroup(). - Other misc changes including fixes for bugs that Julia Lawall noticed and patch descriptions updates with more details on how the introduced changes are going to be used. - MAINTAINERS entries added. The followings are discussion points which were raised but didn't result in code changes in this iteration. - There were discussions around exposing __setscheduler_prio() and, in v2, SCHED_CHANGE_BLOCK() in kernel/sched/sched.h. Switching scheduler implementations is innate for SCX. At the very least, it needs to be able to turn on and off the BPF scheduler which requires something equivalent to SCHED_CHANGE_BLOCK(). The use of __setscheduler_prio() depends on the behavior we want to present to userspace. The current one of using CFS as the fallback when BPF scheduler is not available seems more friendly and less error-prone to other options. - Another discussion point was around for_each_active_class() and friends which skip over CFS or SCX when it's known that the sched_class must be empty. I left it as-is for now as it seems to be cleaner and more robust than trying to plug each operation which may added unnecessary overheads. Overview -------- This patch set proposes a new scheduler class called ‘ext_sched_class’, or sched_ext, which allows scheduling policies to be implemented as BPF programs. More details will be provided on the overall architecture of sched_ext throughout the various patches in this set, as well as in the “How” section below. We realize that this patch set is a significant proposal, so we will be going into depth in the following “Motivation” section to explain why we think it’s justified. That section is laid out as follows, touching on three main axes where we believe that sched_ext provides significant value: 1. Ease of experimentation and exploration: Enabling rapid iteration of new scheduling policies. 2. Customization: Building application-specific schedulers which implement policies that are not applicable to general-purpose schedulers. 3. Rapid scheduler deployments: Non-disruptive swap outs of scheduling policies in production environments. After the motivation section, we’ll provide a more detailed (but still high-level) overview of how sched_ext works. Motivation ---------- 1. Ease of experimentation and exploration *Why is exploration important?* Scheduling is a challenging problem space. Small changes in scheduling behavior can have a significant impact on various components of a system, with the corresponding effects varying widely across different platforms, architectures, and workloads. While complexities have always existed in scheduling, they have increased dramatically over the past 10-15 years. In the mid-late 2000s, cores were typically homogeneous and further apart from each other, with the criteria for scheduling being roughly the same across the entire die. Systems in the modern age are by comparison much more complex. Modern CPU designs, where the total power budget of all CPU cores often far exceeds the power budget of the socket, with dynamic frequency scaling, and with or without chiplets, have significantly expanded the scheduling problem space. Cache hierarchies have become less uniform, with Core Complex (CCX) designs such as recent AMD processors having multiple shared L3 caches within a single socket. Such topologies resemble NUMA sans persistent NUMA node stickiness. Use-cases have become increasingly complex and diverse as well. Applications such as mobile and VR have strict latency requirements to avoid missing deadlines that impact user experience. Stacking workloads in servers is constantly pushing the demands on the scheduler in terms of workload isolation and resource distribution. Experimentation and exploration are important for any non-trivial problem space. However, given the recent hardware and software developments, we believe that experimentation and exploration are not just important, but _critical_ in the scheduling problem space. Indeed, other approaches in industry are already being explored. AMD has proposed an experimental patch set [0] which enables userspace to provide hints to the scheduler via “Userspace Hinting”. The approach adds a prctl() API which allows callers to set a numerical “hint” value on a struct task_struct. This hint is then optionally read by the scheduler to adjust the cost calculus for various scheduling decisions. [0]: https://lore.kernel.org/lkml/20220910105326.1797-1-kprateek.nayak@xxxxxxx/ Huawei have also expressed interest [1] in enabling some form of programmable scheduling. While we’re unaware of any patch sets which have been sent to the upstream list for this proposal, it similarly illustrates the need for more flexibility in the scheduler. [1]: https://lore.kernel.org/bpf/dedc7b72-9da4-91d0-d81d-75360c177188@xxxxxxxxxx/ Additionally, Google has developed ghOSt [2] with the goal of enabling custom, userspace driven scheduling policies. Prior presentations at LPC [3] have discussed ghOSt and how BPF can be used to accelerate scheduling. [2]: https://dl.acm.org/doi/pdf/10.1145/3477132.3483542 [3]: https://lpc.events/event/16/contributions/1365/ *Why can’t we just explore directly with CFS?* Experimenting with CFS directly or implementing a new sched_class from scratch is of course possible, but is often difficult and time consuming. Newcomers to the scheduler often require years to understand the codebase and become productive contributors. Even for seasoned kernel engineers, experimenting with and upstreaming features can take a very long time. The iteration process itself is also time consuming, as testing scheduler changes on real hardware requires reinstalling the kernel and rebooting the host. Core scheduling is an example of a feature that took a significant amount of time and effort to integrate into the kernel. Part of the difficulty with core scheduling was the inherent mismatch in abstraction between the desire to perform core-wide scheduling, and the per-cpu design of the kernel scheduler. This caused issues, for example ensuring proper fairness between the independent runqueues of SMT siblings. The high barrier to entry for working on the scheduler is an impediment to academia as well. Master’s/PhD candidates who are interested in improving the scheduler will spend years ramping-up, only to complete their degrees just as they’re finally ready to make significant changes. A lower entrance barrier would allow researchers to more quickly ramp up, test out hypotheses, and iterate on novel ideas. Research methodology is also severely hampered by the high barrier of entry to make modifications; for example, the Shenango [4] and Shinjuku scheduling policies used sched affinity to replicate the desired policy semantics, due to the difficulty of incorporating these policies into the kernel directly. [4]: https://www.usenix.org/system/files/nsdi19-ousterhout.pdf The iterative process itself also imposes a significant cost to working on the scheduler. Testing changes requires developers to recompile and reinstall the kernel, reboot their machines, rewarm their workloads, and then finally rerun their benchmarks. Though some of this overhead could potentially be mitigated by enabling schedulers to be implemented as kernel modules, a machine crash or subtle system state corruption is always only one innocuous mistake away. These problems are exacerbated when testing production workloads in a datacenter environment as well, where multiple hosts may be involved in an experiment; requiring a significantly longer ramp up time. Warming up memcache instances in the Meta production environment takes hours, for example. *How does sched_ext help with exploration?* sched_ext attempts to address all of the problems described above. In this section, we’ll describe the benefits to experimentation and exploration that are afforded by sched_ext, provide real-world examples of those benefits, and discuss some of the trade-offs and considerations in our design choices. One of our main goals was to lower the barrier to entry for experimenting with the scheduler. sched_ext provides ergonomic callbacks and helpers to ease common operations such as managing idle CPUs, scheduling tasks on arbitrary CPUs, handling preemptions from other scheduling classes, and more. While sched_ext does require some ramp-up, the complexity is self-contained, and the learning curve gradual. Developers can ramp up by first implementing simple policies such as global weighted vtime scheduling in only tens of lines of code, and then continue to learn the APIs and building blocks available with sched_ext as they build more featureful and complex schedulers. Another critical advantage provided by sched_ext is the use of BPF. BPF provides strong safety guarantees by statically analyzing programs at load time to ensure that they cannot corrupt or crash the system. sched_ext guarantees system integrity no matter what BPF scheduler is loaded, and provides mechanisms to safely disable the current BPF scheduler and migrate tasks back to a trusted scheduler. For example, we also implement in-kernel safety mechanisms to guarantee that a misbehaving scheduler cannot indefinitely starve tasks. BPF also enables sched_ext to significantly improve iteration speed for running experiments. Loading and unloading a BPF scheduler is simply a matter of running and terminating a sched_ext binary. BPF also provides programs with a rich set of APIs, such as maps, kfuncs, and BPF helpers. In addition to providing useful building blocks to programs that run entirely in kernel space (such as many of our example schedulers), these APIs also allow programs to leverage user space in making scheduling decisions. Specifically, the Atropos sample scheduler has a relatively simple weighted vtime or FIFO scheduling layer in BPF, paired with a load balancing component in userspace written in Rust. As described in more detail below, we also built a more general user-space scheduling framework called “rhone” by leveraging various BPF features. On the other hand, BPF does have shortcomings, as can be plainly seen from the complexity in some of the example schedulers. scx_example_pair.bpf.c illustrates this point well. To start, it requires a good amount of code to emulate cgroup-local-storage. In the kernel proper, this would simply be a matter of adding another pointer to the struct cgroup, but in BPF, it requires a complex juggling of data amongst multiple different maps, a good amount of boilerplate code, and some unwieldy bpf_loop()‘s and atomics. The code is also littered with explicit and often unnecessary sanity checks to appease the verifier. That being said, BPF is being rapidly improved. For example, Yonghong Song recently upstreamed a patch set [5] to add a cgroup local storage map type, allowing scx_example_pair.bpf.c to be simplified. There are plans to address other issues as well, such as providing statically-verified locking, and avoiding the need for unnecessary sanity checks. Addressing these shortcomings is a high priority for BPF, and as progress continues to be made, we expect most deficiencies to be addressed in the not-too-distant future. [5]: https://lore.kernel.org/bpf/20221026042835.672317-1-yhs@xxxxxx/ Yet another exploration advantage of sched_ext is helping widening the scope of experiments. For example, sched_ext makes it easy to defer CPU assignment until a task starts executing, allowing schedulers to share scheduling queues at any granularity (hyper-twin, CCX and so on). Additionally, higher level frameworks can be built on top to further widen the scope. For example, the aforementioned “rhone” [6] library allows implementing scheduling policies in user-space by encapsulating the complexity around communicating scheduling decisions with the kernel. This allows taking advantage of a richer programming environment in user-space, enabling experimenting with, for instance, more complex mathematical models. [6]: https://github.com/Decave/rhone sched_ext also allows developers to leverage machine learning. At Meta, we experimented with using machine learning to predict whether a running task would soon yield its CPU. These predictions can be used to aid the scheduler in deciding whether to keep a runnable task on its current CPU rather than migrating it to an idle CPU, with the hope of avoiding unnecessary cache misses. Using a tiny neural net model with only one hidden layer of size 16, and a decaying count of 64 syscalls as a feature, we were able to achieve a 15% throughput improvement on an Nginx benchmark, with an 87% inference accuracy. 2. Customization This section discusses how sched_ext can enable users to run workloads on application-specific schedulers. *Why deploy custom schedulers rather than improving CFS?* Implementing application-specific schedulers and improving CFS are not conflicting goals. Scheduling features explored with sched_ext which yield beneficial results, and which are sufficiently generalizable, can and should be integrated into CFS. However, CFS is fundamentally designed to be a general purpose scheduler, and thus is not conducive to being extended with some highly targeted application or hardware specific changes. Targeted, bespoke scheduling has many potential use cases. For example, VM scheduling can make certain optimizations that are infeasible in CFS due to the constrained problem space (scheduling a static number of long-running VCPUs versus an arbitrary number of threads). Additionally, certain applications might want to make targeted policy decisions based on hints directly from the application (for example, a service that knows the different deadlines of incoming RPCs). Google has also experimented with some promising, novel scheduling policies. One example is “central” scheduling, wherein a single CPU makes all scheduling decisions for the entire system. This allows most cores on the system to be fully dedicated to running workloads, and can have significant performance improvements for certain use cases. For example, central scheduling with VCPUs can avoid expensive vmexits and cache flushes, by instead delegating the responsibility of preemption checks from the tick to a single CPU. See scx_example_central.bpf.c for a simple example of a central scheduling policy built in sched_ext. Some workloads also have non-generalizable constraints which enable optimizations in a scheduling policy which would otherwise not be feasible. For example,VM workloads at Google typically have a low overcommit ratio compared to the number of physical CPUs. This allows the scheduler to support bounded tail latencies, as well as longer blocks of uninterrupted time. Yet another interesting use case is the scx_example_flatcg scheduler, which is in 0024-sched_ext-Add-cgroup-support.patch and provides a flattened hierarchical vtree for cgroups. This scheduler does not account for thundering herd problems among cgroups, and therefore may not be suitable for inclusion in CFS. However, in a simple benchmark using wrk[8] on apache serving a CGI script calculating sha1sum of a small file, it outperformed CFS by ~3% with CPU controller disabled and by ~10% with two apache instances competing with 2:1 weight ratio nested four level deep. [7] https://github.com/wg/wrk Certain industries require specific scheduling behaviors that do not apply broadly. For example, ARINC 653 defines scheduling behavior that is widely used by avionic software, and some out-of-tree implementations (https://ieeexplore.ieee.org/document/7005306) have been built. While the upstream community may decide to merge one such implementation in the future, it would also be entirely reasonable to not do so given the narrowness of use-case, and non-generalizable, strict requirements. Such cases can be well served by sched_ext in all stages of the software development lifecycle -- development, testing, deployment and maintenance. There are also classes of policy exploration, such as machine learning, or responding in real-time to application hints, that are significantly harder (and not necessarily appropriate) to integrate within the kernel itself. *Won’t this increase fragmentation?* We acknowledge that to some degree, sched_ext does run the risk of increasing the fragmentation of scheduler implementations. As a result of exploration, however, we believe that enabling the larger ecosystem to innovate will ultimately accelerate the overall development and performance of Linux. Additionally, our licensing and API stability policies should incentivize users to upstream their schedulers. BPF programs are required to be GPLv2, which is enforced by the verifier on program loads. With regards to API stability, just as with other semi-internal interfaces such as BPF kfuncs, we won’t be providing any API stability guarantees to BPF schedulers. While we intend to make an effort to provide compatibility when possible, we will not provide any explicit, strong guarantees as the kernel typically does with e.g. UAPI headers. For users who decide to keep their schedulers out-of-tree,the licensing and maintenance overheads will be fundamentally the same as for carrying out-of-tree patches. With regards to the schedulers included in this patch set, and any other schedulers we implement in the future, both Meta and Google will open-source all of the schedulers we implement which have any relevance to the broader upstream community. We expect that some of these, such as the example schedulers and scx_example_flatcg scheduler, will be upstreamed as part of the kernel tree. Distros will be able to package and release these schedulers with the kernel, allowing users to utilize these schedulers out-of-the-box without requiring any additional work or dependencies such as clang or building the scheduler programs themselves. Other schedulers and scheduling frameworks such as rhone may be open-sourced through separate per-project repos. 3. Rapid scheduler deployments Rolling out kernel upgrades is a slow and iterative process. At a large scale it can take months to roll a new kernel out to a fleet of servers. While this latency is expected and inevitable for normal kernel upgrades, it can become highly problematic when kernel changes are required to fix bugs. Livepatch [8] is available to quickly roll out critical security fixes to large fleets, but the scope of changes that can be applied with livepatching is fairly limited, and would likely not be usable for patching scheduling policies. With sched_ext, new scheduling policies can be rapidly rolled out to production environments. [8]: https://www.kernel.org/doc/html/latest/livepatch/livepatch.html As an example, one of the variants of the L1 Terminal Fault (L1TF) [9] vulnerability allows a VCPU running a VM to read arbitrary host kernel memory for pages in L1 data cache. The solution was to implement core scheduling, which ensures that tasks running as hypertwins have the same “cookie”. [9]: https://www.intel.com/content/www/us/en/architecture-and-technology/l1tf.html While core scheduling works well, it took a long time to finalize and land upstream. This long rollout period was painful, and required organizations to make difficult choices amongst a bad set of options. Some companies such as Google chose to implement and use their own custom L1TF-safe scheduler, others chose to run without hyper-threading enabled, and yet others left hyper-threading enabled and crossed their fingers. Once core scheduling was upstream, organizations had to upgrade the kernels on their entire fleets. As downtime is not an option for many, these upgrades had to be gradually rolled out, which can take a very long time for large fleets. An example of an sched_ext scheduler that illustrates core scheduling semantics is scx_example_pair.bpf.c, which co-schedules pairs of tasks from the same cgroup, and is resilient to L1TF vulnerabilities. While this example scheduler is certainly not suitable for production in its current form, a similar scheduler that is more performant and featureful could be written and deployed if necessary. Rapid scheduling deployments can similarly be useful to quickly roll-out new scheduling features without requiring kernel upgrades. At Google, for example, it was observed that some low-priority workloads were causing degraded performance for higher-priority workloads due to consuming a disproportionate share of memory bandwidth. While a temporary mitigation was to use sched affinity to limit the footprint of this low-priority workload to a small subset of CPUs, a preferable solution would be to implement a more featureful task-priority mechanism which automatically throttles lower-priority tasks which are causing memory contention for the rest of the system. Implementing this in CFS and rolling it out to the fleet could take a very long time. sched_ext would directly address these gaps. If another hardware bug or resource contention issue comes in that requires scheduler support to mitigate, sched_ext can be used to experiment with and test different policies. Once a scheduler is available, it can quickly be rolled out to as many hosts as necessary, and function as a stop-gap solution until a longer-term mitigation is upstreamed. How --- sched_ext is a new sched_class which allows scheduling policies to be implemented in BPF programs. sched_ext leverages BPF’s struct_ops feature to define a structure which exports function callbacks and flags to BPF programs that wish to implement scheduling policies. The struct_ops structure exported by sched_ext is struct sched_ext_ops, and is conceptually similar to struct sched_class. The role of sched_ext is to map the complex sched_class callbacks to the more simple and ergonomic struct sched_ext_ops callbacks. Unlike some other BPF program types which have ABI requirements due to exporting UAPIs, struct_ops has no ABI requirements whatsoever. This provides us with the flexibility to change the APIs provided to schedulers as necessary. BPF struct_ops is also already being used successfully in other subsystems, such as in support of TCP congestion control. The only struct_ops field that is required to be specified by a scheduler is the ‘name’ field. Otherwise, sched_ext will provide sane default behavior, such as automatically choosing an idle CPU on the task wakeup path if .select_cpu() is missing. *Dispatch queues* To bridge the workflow imbalance between the scheduler core and sched_ext_ops callbacks, sched_ext uses simple FIFOs called dispatch queues (dsq's). By default, there is one global dsq (SCX_DSQ_GLOBAL), and one local per-CPU dsq (SCX_DSQ_LOCAL). SCX_DSQ_GLOBAL is provided for convenience and need not be used by a scheduler that doesn't require it. As described in more detail below, SCX_DSQ_LOCAL is the per-CPU FIFO that sched_ext pulls from when putting the next task on the CPU. The BPF scheduler can manage an arbitrary number of dsq's using scx_bpf_create_dsq() and scx_bpf_destroy_dsq(). *Scheduling cycle* The following briefly shows a typical workflow for how a waking task is scheduled and executed. 1. When a task is waking up, .select_cpu() is the first operation invoked. This serves two purposes. It both allows a scheduler to optimize task placement by specifying a CPU where it expects the task to eventually be scheduled, and the latter is that the selected CPU will be woken if it’s idle. 2. Once the target CPU is selected, .enqueue() is invoked. It can make one of the following decisions: - Immediately dispatch the task to either the global dsq (SCX_DSQ_GLOBAL) or the current CPU’s local dsq (SCX_DSQ_LOCAL). - Immediately dispatch the task to a user-created dispatch queue. - Queue the task on the BPF side, e.g. in an rbtree map for a vruntime scheduler, with the intention of dispatching it at a later time from .dispatch(). 3. When a CPU is ready to schedule, it first looks at its local dsq. If empty, it invokes .consume() which should make one or more scx_bpf_consume() calls to consume tasks from dsq's. If a scx_bpf_consume() call succeeds, the CPU has the next task to run and .consume() can return. If .consume() is not defined, sched_ext will by-default consume from only the built-in SCX_DSQ_GLOBAL dsq. 4. If there's still no task to run, .dispatch() is invoked which should make one or more scx_bpf_dispatch() calls to dispatch tasks from the BPF scheduler to one of the dsq's. If more than one task has been dispatched, go back to the previous consumption step. *Verifying callback behavior* sched_ext always verifies that any value returned from a callback is valid, and will issue an error and unload the scheduler if it is not. For example, if .select_cpu() returns an invalid CPU, or if an attempt is made to invoke the scx_bpf_dispatch() with invalid enqueue flags. Furthermore, if a task remains runnable for too long without being scheduled, sched_ext will detect it and error-out the scheduler. Closing Thoughts ---------------- Both Meta and Google have experimented quite a lot with schedulers in the last several years. Google has benchmarked various workloads using user space scheduling, and have achieved performance wins by trading off generality for application specific needs. At Meta, we have not yet deployed sched_ext on any production workloads, though our preliminary experiments indicate that sched_ext would provide significant performance wins when deployed at scale. If successfully upstreamed, we expect to leverage it extensively to run various experiments and develop customized schedulers for a number of critical workloads. In closing, both Meta and Google believe that sched_ext will significantly evolve how the broader community explores the scheduling problem space, empowering continued improvement to the in-kernel scheduler, while also enabling targeted policies for custom applications. We’ll be able to experiment easier and faster, explore uncharted areas, and deploy emergency scheduler changes when necessary. The same applies to anyone who wants to work on the scheduler, including academia and specialized industries. sched_ext will push forward the state of the art when it comes to scheduling and performance in Linux. Written By ---------- David Vernet <dvernet@xxxxxxxx> Josh Don <joshdon@xxxxxxxxxx> Tejun Heo <tj@xxxxxxxxxx> Barret Rhoden <brho@xxxxxxxxxx> Supported By ------------ Paul Turner <pjt@xxxxxxxxxx> Neel Natu <neelnatu@xxxxxxxxxx> Patrick Bellasi <derkling@xxxxxxxxxx> Hao Luo <haoluo@xxxxxxxxxx> Dimitrios Skarlatos <dskarlat@xxxxxxxxxx> Patchset -------- This patchset is on top of bpf/for-next as of 2023-03-16: b8a2e3f93d41 ("cgroup: Make current_cgns_cgroup_dfl() safe to call after exit_task_namespace()") and contains the following patches: NOTE: The doc added by 0030 contains a high-level overview and might be good place to start. 0001-cgroup-Implement-cgroup_show_cftypes.patch 0002-sched-Encapsulate-task-attribute-change-sequence-int.patch 0003-sched-Restructure-sched_class-order-sanity-checks-in.patch 0004-sched-Allow-sched_cgroup_fork-to-fail-and-introduce-.patch 0005-sched-Add-sched_class-reweight_task.patch 0006-sched-Add-sched_class-switching_to-and-expose-check_.patch 0007-sched-Factor-out-cgroup-weight-conversion-functions.patch 0008-sched-Expose-css_tg-__setscheduler_prio-and-SCHED_CH.patch 0009-sched-Enumerate-CPU-cgroup-file-types.patch 0010-sched-Add-reason-to-sched_class-rq_-on-off-line.patch 0011-sched-Add-normal_policy.patch 0012-sched_ext-Add-boilerplate-for-extensible-scheduler-c.patch 0013-sched_ext-Implement-BPF-extensible-scheduler-class.patch 0014-sched_ext-Add-scx_example_simple-and-scx_example_qma.patch 0015-sched_ext-Add-sysrq-S-which-disables-the-BPF-schedul.patch 0016-sched_ext-Implement-runnable-task-stall-watchdog.patch 0017-sched_ext-Allow-BPF-schedulers-to-disallow-specific-.patch 0018-sched_ext-Allow-BPF-schedulers-to-switch-all-eligibl.patch 0019-sched_ext-Implement-scx_bpf_kick_cpu-and-task-preemp.patch 0020-sched_ext-Make-watchdog-handle-ops.dispatch-looping-.patch 0021-sched_ext-Add-task-state-tracking-operations.patch 0022-sched_ext-Implement-tickless-support.patch 0023-sched_ext-Track-tasks-that-are-subjects-of-the-in-fl.patch 0024-sched_ext-Add-cgroup-support.patch 0025-sched_ext-Implement-SCX_KICK_WAIT.patch 0026-sched_ext-Implement-sched_ext_ops.cpu_acquire-releas.patch 0027-sched_ext-Implement-sched_ext_ops.cpu_online-offline.patch 0028-sched_ext-Implement-core-sched-support.patch 0029-sched_ext-Add-vtime-ordered-priority-queue-to-dispat.patch 0030-sched_ext-Documentation-scheduler-Document-extensibl.patch 0031-sched_ext-Add-a-basic-userland-vruntime-scheduler.patch 0032-sched_ext-Add-a-rust-userspace-hybrid-example-schedu.patch 0001 : Cgroup prep. 0002-0011: Scheduler prep. 0012-0014: sched_ext core implementation and a couple example BPF scheduler. 0015-0018: Utility features including safety mechanisms and switch-all. 0019-0022: Kicking and preempting other CPUs, task state transition tracking and tickless support. Demonstrated with an example central scheduler which makes all scheduling decisions on one CPU. 0023-0026: cgroup support and the ability to wait for other CPUs after kicking them. Demonstrated with an example pair scheduler which guarantees that a hyperthread pair always executes tasks from the same cgroup at any given time. 0027 : Add CPU hotplug callbacks. 0028 : Add core-sched support. 0029 : Add DSQ rbtree support. 0030 : Add documentation. 0031-0032: Add two example schedulers. One demonstrating deferring most scheduling decisions to userland. The other demonstrating a hybrid approach where load balancing decisions are made by userspace written in rust. The patchset is also available in the following git branch: https://github.com/htejun/sched_ext sched_ext-v3 diffstat follows. Documentation/scheduler/index.rst | 1 Documentation/scheduler/sched-ext.rst | 230 + MAINTAINERS | 3 drivers/tty/sysrq.c | 1 include/asm-generic/vmlinux.lds.h | 1 include/linux/cgroup-defs.h | 8 include/linux/cgroup.h | 5 include/linux/sched.h | 5 include/linux/sched/ext.h | 709 ++++ include/linux/sched/task.h | 3 include/uapi/linux/sched.h | 1 init/Kconfig | 5 init/init_task.c | 12 kernel/Kconfig.preempt | 24 kernel/bpf/bpf_struct_ops_types.h | 4 kernel/cgroup/cgroup.c | 97 kernel/fork.c | 17 kernel/sched/build_policy.c | 5 kernel/sched/core.c | 496 +- kernel/sched/deadline.c | 4 kernel/sched/debug.c | 6 kernel/sched/ext.c | 4326 ++++++++++++++++++++++++++ kernel/sched/ext.h | 245 + kernel/sched/fair.c | 9 kernel/sched/idle.c | 2 kernel/sched/rt.c | 4 kernel/sched/sched.h | 158 kernel/sched/topology.c | 4 tools/sched_ext/.gitignore | 9 tools/sched_ext/Makefile | 216 + tools/sched_ext/atropos/.gitignore | 3 tools/sched_ext/atropos/Cargo.toml | 28 tools/sched_ext/atropos/build.rs | 70 tools/sched_ext/atropos/rustfmt.toml | 8 tools/sched_ext/atropos/src/atropos_sys.rs | 10 tools/sched_ext/atropos/src/bpf/atropos.bpf.c | 751 ++++ tools/sched_ext/atropos/src/bpf/atropos.h | 44 tools/sched_ext/atropos/src/main.rs | 942 +++++ tools/sched_ext/gnu/stubs.h | 1 tools/sched_ext/scx_common.bpf.h | 289 + tools/sched_ext/scx_example_central.bpf.c | 334 ++ tools/sched_ext/scx_example_central.c | 94 tools/sched_ext/scx_example_flatcg.bpf.c | 872 +++++ tools/sched_ext/scx_example_flatcg.c | 232 + tools/sched_ext/scx_example_flatcg.h | 49 tools/sched_ext/scx_example_pair.bpf.c | 627 +++ tools/sched_ext/scx_example_pair.c | 143 tools/sched_ext/scx_example_pair.h | 10 tools/sched_ext/scx_example_qmap.bpf.c | 401 ++ tools/sched_ext/scx_example_qmap.c | 107 tools/sched_ext/scx_example_simple.bpf.c | 128 tools/sched_ext/scx_example_simple.c | 101 tools/sched_ext/scx_example_userland.bpf.c | 269 + tools/sched_ext/scx_example_userland.c | 402 ++ tools/sched_ext/scx_example_userland_common.h | 19 tools/sched_ext/user_exit_info.h | 50 56 files changed, 12362 insertions(+), 232 deletions(-) Thanks. -- tejun
