Merge drm/drm-fixes into drm-misc-fixes

Forwarding to v6.6-rc1.

Signed-off-by: Thomas Zimmermann <tzimmermann@suse.de>

Merge tag 'wq-for-6.6' of git://git.kernel.org/pub/scm/linux/kernel/git/tj/wq

Pull workqueue updates from Tejun Heo:

- Unbound workqueues now support more flexible affinity scopes.

The default

Merge tag 'wq-for-6.6' of git://git.kernel.org/pub/scm/linux/kernel/git/tj/wq

Pull workqueue updates from Tejun Heo:

- Unbound workqueues now support more flexible affinity scopes.

The default behavior is to soft-affine according to last level cache
boundaries. A work item queued from a given LLC is executed by a
worker running on the same LLC but the worker may be moved across
cache boundaries as the scheduler sees fit. On machines which
multiple L3 caches, which are becoming more popular along with
chiplet designs, this improves cache locality while not harming work
conservation too much.

Unbound workqueues are now also a lot more flexible in terms of
execution affinity. Differeing levels of affinity scopes are
supported and both the default and per-workqueue affinity settings
can be modified dynamically. This should help working around amny of
sub-optimal behaviors observed recently with asymmetric ARM CPUs.

This involved signficant restructuring of workqueue code. Nothing was
reported yet but there's some risk of subtle regressions. Should keep
an eye out.

- Rescuer workers now has more identifiable comms.

- workqueue.unbound_cpus added so that CPUs which can be used by
workqueue can be constrained early during boot.

- Now that all the in-tree users have been flushed out, trigger warning
if system-wide workqueues are flushed.

* tag 'wq-for-6.6' of git://git.kernel.org/pub/scm/linux/kernel/git/tj/wq: (31 commits)
workqueue: fix data race with the pwq->stats[] increment
workqueue: Rename rescuer kworker
workqueue: Make default affinity_scope dynamically updatable
workqueue: Add "Affinity Scopes and Performance" section to documentation
workqueue: Implement non-strict affinity scope for unbound workqueues
workqueue: Add workqueue_attrs->__pod_cpumask
workqueue: Factor out need_more_worker() check and worker wake-up
workqueue: Factor out work to worker assignment and collision handling
workqueue: Add multiple affinity scopes and interface to select them
workqueue: Modularize wq_pod_type initialization
workqueue: Add tools/workqueue/wq_dump.py which prints out workqueue configuration
workqueue: Generalize unbound CPU pods
workqueue: Factor out clearing of workqueue-only attrs fields
workqueue: Factor out actual cpumask calculation to reduce subtlety in wq_update_pod()
workqueue: Initialize unbound CPU pods later in the boot
workqueue: Move wq_pod_init() below workqueue_init()
workqueue: Rename NUMA related names to use pod instead
workqueue: Rename workqueue_attrs->no_numa to ->ordered
workqueue: Make unbound workqueues to use per-cpu pool_workqueues
workqueue: Call wq_update_unbound_numa() on all CPUs in NUMA node on CPU hotplug
...

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workqueue: Implement non-strict affinity scope for unbound workqueues

An unbound workqueue can be served by multiple worker_pools to improve
locality. The segmentation is achieved by grouping CPUs i

workqueue: Implement non-strict affinity scope for unbound workqueues

An unbound workqueue can be served by multiple worker_pools to improve
locality. The segmentation is achieved by grouping CPUs into pods. By
default, the cache boundaries according to cpus_share_cache() define the
CPUs are grouped. Let's a workqueue is allowed to run on all CPUs and the
system has two L3 caches. The workqueue would be mapped to two worker_pools
each serving one L3 cache domains.

While this improves locality, because the pod boundaries are strict, it
limits the total bandwidth a given issuer can consume. For example, let's
say there is a thread pinned to a CPU issuing enough work items to saturate
the whole machine. With the machine segmented into two pods, no matter how
many work items it issues, it can only use half of the CPUs on the system.

While this limitation has existed for a very long time, it wasn't very
pronounced because the affinity grouping used to be always by NUMA nodes.
With cache boundaries as the default and support for even finer grained
scopes (smt and cpu), it is now an a lot more pressing problem.

This patch implements non-strict affinity scope where the pod boundaries
aren't enforced strictly. Going back to the previous example, the workqueue
would still be mapped to two worker_pools; however, the affinity enforcement
would be soft. The workers in both pools would have their cpus_allowed set
to the whole machine thus allowing the scheduler to migrate them anywhere on
the machine. However, whenever an idle worker is woken up, the workqueue
code asks the scheduler to bring back the task within the pod if the worker
is outside. ie. work items start executing within its affinity scope but can
be migrated outside as the scheduler sees fit. This removes the hard cap on
utilization while maintaining the benefits of affinity scopes.

After the earlier ->__pod_cpumask changes, the implementation is pretty
simple. When non-strict which is the new default:

* pool_allowed_cpus() returns @pool->attrs->cpumask instead of
->__pod_cpumask so that the workers are allowed to run on any CPU that
the associated workqueues allow.

* If the idle worker task's ->wake_cpu is outside the pod, kick_pool() sets
the field to a CPU within the pod.

This would be the first use of task_struct->wake_cpu outside scheduler
proper, so it isn't clear whether this would be acceptable. However, other
methods of migrating tasks are significantly more expensive and are likely
prohibitively so if we want to do this on every work item. This needs
discussion with scheduler folks.

There is also a race window where setting ->wake_cpu wouldn't be effective
as the target task is still on CPU. However, the window is pretty small and
this being a best-effort optimization, it doesn't seem to warrant more
complexity at the moment.

While the non-strict cache affinity scopes seem to be the best option, the
performance picture interacts with the affinity scope and is a bit
complicated to fully discuss in this patch, so the behavior is made easily
selectable through wqattrs and sysfs and the next patch will add
documentation to discuss performance implications.

v2: pool->attrs->affn_strict is set to true for per-cpu worker_pools.

Signed-off-by: Tejun Heo <tj@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Linus Torvalds <torvalds@linux-foundation.org>

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workqueue: Add multiple affinity scopes and interface to select them

Add three more affinity scopes - WQ_AFFN_CPU, SMT and CACHE - and make CACHE
the default. The code changes to actually add the ad

workqueue: Add multiple affinity scopes and interface to select them

Add three more affinity scopes - WQ_AFFN_CPU, SMT and CACHE - and make CACHE
the default. The code changes to actually add the additional scopes are
trivial.

Also add module parameter "workqueue.default_affinity_scope" to override the
default scope and "affinity_scope" sysfs file to configure it per workqueue.
wq_dump.py and documentations are updated accordingly.

This enables significant flexibility in configuring how unbound workqueues
behave. If affinity scope is set to "cpu", it'll behave close to a per-cpu
workqueue. On the other hand, "system" removes all locality boundaries.

Many modern machines have multiple L3 caches often while being mostly
uniform in terms of memory access. Thus, workqueue's previous behavior of
spreading work items in each NUMA node had negative performance implications
from unncessarily crossing L3 boundaries between issue and execution.
However, picking a finer grained affinity scope also has a downside in that
an issuer in one group can't utilize CPUs in other groups.

While dependent on the specifics of workload, there's usually a noticeable
penalty in crossing L3 boundaries, so let's default to CACHE. This issue
will be further addressed and documented with examples in future patches.

Signed-off-by: Tejun Heo <tj@kernel.org>

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workqueue: Add tools/workqueue/wq_dump.py which prints out workqueue configuration

Lack of visibility has always been a pain point for workqueues. While the
recently added wq_monitor.py improved the

workqueue: Add tools/workqueue/wq_dump.py which prints out workqueue configuration

Lack of visibility has always been a pain point for workqueues. While the
recently added wq_monitor.py improved the situation, it's still difficult to
understand what worker pools are active in the system, how workqueues map to
them and why. The lack of visibility into how workqueues are configured is
going to become more noticeable as workqueue improves locality awareness and
provides more mechanisms to customize locality related behaviors.

Now that the basic framework for more flexible locality support is in place,
this is a good time to improve the situation. This patch adds
tools/workqueues/wq_dump.py which prints out the topology configuration,
worker pools and how workqueues are mapped to pools. Read the command's help
message for more details.

Signed-off-by: Tejun Heo <tj@kernel.org>

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