// Insert regardless of whether we have a match in m.pcs.
	// Even if we have a match, we want to keep the newest version
	// of that stack, since we're much more likely tos see it again
	// as we iterate through the trace linearly. Simultaneously, we
	// are likely to never see the old stack again.
	m.pcs[pcs] = stack
	m.stacks[stack] = rec
	return rec
}

func (m *stackMap) profile() []traceviewer.ProfileRecord {

	// In both cases the algorithm is a linear scan over the two
	// lists - T's methods and V's methods - simultaneously.
	// Since method tables are stored in a unique sorted order
	// (alphabetical, with no duplicate method names), the scan
	// through V's methods must hit a match for each of T's
	// methods along the way, or else V does not implement T.
	// This lets us run the scan in overall linear time instead of

// that is modifying the code can have feedback about the performance impact
// on their changes. It also runs multiple number of rules test cases to check
// if the number of rules grows linearly.
func TestNumberIptablesRules(t *testing.T) {
	testCases := []struct {
		name                string
		epsFunc             func(eps *discovery.EndpointSlice)
		svcFunc             func(svc *v1.Service)

    checkNotNull(presentBehavior);
    checkNotNull(absentBehavior);
    if (!(list instanceof RandomAccess)) {
      list = Lists.newArrayList(list);
    }
    // TODO(lowasser): benchmark when it's best to do a linear search

    int lower = 0;
    int upper = list.size() - 1;

    while (lower <= upper) {
      int middle = (lower + upper) >>> 1;
      int c = comparator.compare(key, list.get(middle));

    checkNotNull(presentBehavior);
    checkNotNull(absentBehavior);
    if (!(list instanceof RandomAccess)) {
      list = Lists.newArrayList(list);
    }
    // TODO(lowasser): benchmark when it's best to do a linear search

    int lower = 0;
    int upper = list.size() - 1;

    while (lower <= upper) {
      int middle = (lower + upper) >>> 1;
      int c = comparator.compare(key, list.get(middle));

// one byte of non-whitespace.
func hasNonWhitespace(s string) bool {
	for _, b := range s {
		switch b {
		// Encoded-words can only be separated by linear white spaces which does
		// not include vertical tabs (\v).
		case ' ', '\t', '\n', '\r':
		default:
			return true
		}
	}
	return false
}

// qDecode decodes a Q encoded string.

      return WalkResult::advance();
    OpBuilder builder(reduce_dataset);
    Location loc = reduce_dataset.getLoc();

    // Get reduce function signature for dataset iteration types.
    // Note: lookupSymbol is a linear lookup which means the overall
    // complexity = # ReduceDataset ops x # of functions in module.
    func::FuncOp reduce_func =
        function->getParentOfType<ModuleOp>().lookupSymbol<FuncOp>(

limitations under the License.
==============================================================================*/

// The XlaDevice executes a TensorFlow graph using the XLA linear algebra
// runtime.
//
// Operators assigned to an XlaDevice are compiled into XLA computations.
// Tensors on an XlaDevice are thin wrappers around XLA ScopedShapedBuffers.
//

	unlock(&ticks.lock)
}

// minTimeForTicksPerSecond is the minimum elapsed time we require to consider our ticksPerSecond
// measurement to be of decent enough quality for profiling.
//
// There's a linear relationship here between minimum time and error from the true value.
// The error from the true ticks-per-second in a linux/amd64 VM seems to be:
// -   1 ms -> ~0.02% error
// -   5 ms -> ~0.004% error

// this CaseRange represents a sequence of the form (say)
// [Upper] [Lower] [Upper] [Lower].
const (
	UpperLower = MaxRune + 1 // (Cannot be a valid delta.)
)

// linearMax is the maximum size table for linear search for non-Latin1 rune.
// Derived by running 'go test -calibrate'.
const linearMax = 18

// is16 reports whether r is in the sorted slice of 16-bit ranges.
func is16(ranges []Range16, r uint16) bool {