// nodeStatusUpdateFrequency in kubelet and renewInterval in NodeLease
	// controller. The node health signal update frequency is the minimal of the
	// two.
	// There are several constraints:
	// 1. nodeMonitorGracePeriod must be N times more than  the node health signal
	//    update frequency, where N means number of retries allowed for kubelet to

type StatefulSetUpdateStrategyType string

const (
	// RollingUpdateStatefulSetStrategyType indicates that update will be
	// applied to all Pods in the StatefulSet with respect to the StatefulSet
	// ordering constraints. When a scale operation is performed with this
	// strategy, new Pods will be created from the specification version indicated
	// by the StatefulSet's updateRevision.

type StatefulSetUpdateStrategyType string

const (
	// RollingUpdateStatefulSetStrategyType indicates that update will be
	// applied to all Pods in the StatefulSet with respect to the StatefulSet
	// ordering constraints. When a scale operation is performed with this
	// strategy, new Pods will be created from the specification version indicated
	// by the StatefulSet's updateRevision.

  // * Future completion is defined by when #value becomes non-null/non SetFuture
  // * Future completion can be observed if the waiters field contains a TOMBSTONE

  // Timed Get
  // There are a few design constraints to consider
  // * We want to be responsive to small timeouts, unpark() has non trivial latency overheads (I
  //   have observed 12 micros on 64-bit linux systems to wake up a parked thread). So if the

  // * Future completion is defined by when #value becomes non-null/non SetFuture
  // * Future completion can be observed if the waiters field contains a TOMBSTONE

  // Timed Get
  // There are a few design constraints to consider
  // * We want to be responsive to small timeouts, unpark() has non trivial latency overheads (I
  //   have observed 12 micros on 64-bit linux systems to wake up a parked thread). So if the

//
// There are several steps between the time that an M experiences contention and
// when that contention may be added to the profile. This comes from our
// constraints: We need to keep the critical section of each lock small,
// especially when those locks are contended. The reporting code cannot acquire
// new locks until the M has released all other locks, which means no memory

  google.protobuf.Struct podAnnotations = 11 [deprecated = true];

  // Pod anti-affinity label selector.
  //
  // Specify the pod anti-affinity that allows you to constrain which nodes
  // your pod is eligible to be scheduled based on labels on pods that are
  // already running on the node rather than based on labels on nodes.
  // There are currently two types of anti-affinity:

=== Performance improvements

Apart from skipping the configuration phase, the configuration cache provides some additional performance improvements:

- All tasks run in parallel by default, subject to dependency constraints.
- Dependency resolution is cached.
- Configuration state and dependency resolution state is discarded from heap after writing the task graph. This reduces the peak heap usage required for a given set of tasks.

		rk := p.From.Reg
		rj := p.To.Reg
		rd := p.RegTo2

		// See section 2.2.7.1 of https://loongson.github.io/LoongArch-Documentation/LoongArch-Vol1-EN.html
		// for the register usage constraints.
		if rd == rj || rd == rk {
			c.ctxt.Diag("illegal register combination: %v\n", p)
		}
		o1 = OP_RRR(atomicInst[p.As], uint32(rk), uint32(rj), uint32(rd))
	}

	out[0] = o1
	out[1] = o2

// they're fresh from the operating system. It updates heapArena metadata that is
// critical for future page allocations.
//
// There are no locking constraints on this method.
func (h *mheap) allocNeedsZero(base, npage uintptr) (needZero bool) {
	for npage > 0 {
		ai := arenaIndex(base)
		ha := h.arenas[ai.l1()][ai.l2()]