}

	var frames []Frame
	for {
		frames = append(frames, Frame{
			PC:       pc,
			Func:     nil,
			Function: gostring(arg.funcName),
			File:     gostring(arg.file),
			Line:     int(arg.lineno),
			Entry:    arg.entry,
			// funcInfo is zero, which implies !funcInfo.valid().
			// That ensures that we use the File/Line info given here.
		})
		if arg.more == 0 {
			break
		}

#!/usr/bin/env bash

echo "Running $0"

if [ -n "$TEST_DEBUG" ]; then
	set -x
fi

trap 'catch $LINENO' ERR

# shellcheck disable=SC2120
catch() {
	if [ $# -ne 0 ]; then
		echo "error on line $1"
		for site in sitea siteb sitec; do
			echo "$site server logs ========="
			cat "/tmp/${site}_1.log"
			echo "==========================="
			cat "/tmp/${site}_2.log"
		done
	fi

	echo "Cleaning up instances of MinIO"

      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>(

	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

#!/usr/bin/env bash

set -x

trap 'catch $LINENO' ERR

# shellcheck disable=SC2120
catch() {
	if [ $# -ne 0 ]; then
		echo "error on line $1"
		for site in sitea siteb sitec sited; do
			echo "$site server logs ========="
			cat "/tmp/${site}_1.log"
			echo "==========================="
			cat "/tmp/${site}_2.log"
		done
	fi

	echo "Cleaning up instances of MinIO"
	pkill minio
	pkill -9 minio
	rm -rf /tmp/multisitea

	// We think of the mutator utilization over time as the
	// box-filtered utilization function, which we call the
	// "windowed mutator utilization function". The resulting
	// function is continuous and piecewise linear (unless
	// window==0, which we handle elsewhere), where the boundaries
	// between segments occur when either edge of the window
	// encounters a change in the instantaneous mutator

  private static final int CUTOFF = (int) (MAX_TABLE_SIZE * DESIRED_LOAD_FACTOR);

  /**
   * Returns an array size suitable for the backing array of a hash table that uses open addressing
   * with linear probing in its implementation. The returned size is the smallest power of two that
   * can hold setSize elements with the desired load factor. Always returns at least setSize + 2.
   */
  @VisibleForTesting

		arch:  arch,
		cconv: cconv,
	})
}

type regSet struct {
	lo    int
	hi    int
	Rconv func(int) string
}

// Few enough architectures that a linear scan is fastest.
// Not even worth sorting.
var regSpace []regSet

/*
	Each architecture defines a register space as a unique
	integer range.

}

LogicalResult ClusteringState::VerifyDominanceProperty(
    unsigned src_root, unsigned dst_root, Operation *insertion_point) {
  // TODO(ezhulenev): Optimize this linear scan with a map lookup.
  for (auto &member : members) {
    unsigned root = FindRoot(member.root);
    if (root != src_root) continue;

    // Block arguments do not really participate in clustering, they are only

	// Try to find the g that needs to be notified.
	// If it hasn't made it to the list yet we won't find it,
	// but it won't park itself once it sees the new notify number.
	//
	// This scan looks linear but essentially always stops quickly.
	// Because g's queue separately from taking numbers,
	// there may be minor reorderings in the list, but we
	// expect the g we're looking for to be near the front.