sched_ext: idle: Per-node idle cpumasks

sched_ext: idle: Per-node idle cpumasks

Using a single global idle mask can lead to inefficiencies and a lot of
stress on the cache coherency protocol on large systems with multiple
NUMA nodes, since all the CPUs can create a really intense read/write
activity on the single global cpumask.

Therefore, split the global cpumask into multiple per-NUMA node cpumasks
to improve scalability and performance on large systems.

The concept is that each cpumask will track only the idle CPUs within
its corresponding NUMA node, treating CPUs in other NUMA nodes as busy.
In this way concurrent access to the idle cpumask will be restricted
within each NUMA node.

The split of multiple per-node idle cpumasks can be controlled using the
SCX_OPS_BUILTIN_IDLE_PER_NODE flag.

By default SCX_OPS_BUILTIN_IDLE_PER_NODE is not enabled and a global
host-wide idle cpumask is used, maintaining the previous behavior.

NOTE: if a scheduler explicitly enables the per-node idle cpumasks (via
SCX_OPS_BUILTIN_IDLE_PER_NODE), scx_bpf_get_idle_cpu/smtmask() will
trigger an scx error, since there are no system-wide cpumasks.

= Test =

Hardware:
 - System: DGX B200
    - CPUs: 224 SMT threads (112 physical cores)
    - Processor: INTEL(R) XEON(R) PLATINUM 8570
    - 2 NUMA nodes

Scheduler:
 - scx_simple [1] (so that we can focus at the built-in idle selection
   policy and not at the scheduling policy itself)

Test:
 - Run a parallel kernel build `make -j $(nproc)` and measure the average
   elapsed time over 10 runs:

          avg time | stdev
          ---------+------
 before:   52.431s | 2.895
  after:   50.342s | 2.895

= Conclusion =

Splitting the global cpumask into multiple per-NUMA cpumasks helped to
achieve a speedup of approximately +4% with this particular architecture
and test case.

The same test on a DGX-1 (40 physical cores, Intel Xeon E5-2698 v4 @
2.20GHz, 2 NUMA nodes) shows a speedup of around 1.5-3%.

On smaller systems, I haven't noticed any measurable regressions or
improvements with the same test (parallel kernel build) and scheduler
(scx_simple).

Moreover, with a modified scx_bpfland that uses the new NUMA-aware APIs
I observed an additional +2-2.5% performance improvement with the same
test.

[1] https://github.com/sched-ext/scx/blob/main/scheds/c/scx_simple.bpf.c

Cc: Yury Norov [NVIDIA] <yury.norov@gmail.com>
Signed-off-by: Andrea Righi <arighi@nvidia.com>
Reviewed-by: Yury Norov [NVIDIA] <yury.norov@gmail.com>
Signed-off-by: Tejun Heo <tj@kernel.org>