// utilization without hurting throughput.
//
// Note that the bucket in the leaky bucket mechanism can never go negative,
// so the GC never gets credit for a lot of CPU time spent without the GC
// running. This is intentional, as an application that stays idle for, say,
// an entire day, could build up enough credit to fail to prevent a death

	}
	return d2
}

// DurationTracker is a simple interface for tracking functions duration,
// it is safe for concurrent use by multiple goroutines.
type DurationTracker interface {
	// Track measures time spent in the given function f and
	// aggregates measured duration using aggregateFunction.
	// if Track is invoked with f from multiple goroutines concurrently,
	// then f must be safe to be invoked concurrently by multiple goroutines.

	ScavengeTotalTime  int64

	IdleTime int64 // Time Ps spent in _Pidle.
	UserTime int64 // Time Ps spent in _Prunning or _Psyscall that's not any of the above.

	TotalTime int64 // GOMAXPROCS * (monotonic wall clock time elapsed)
}

// accumulateGCPauseTime add dt*stwProcs to the GC CPU pause time stats. dt should be
// the actual time spent paused, for orthogonality. maxProcs should be GOMAXPROCS,

    // Number of times the cluster has been (re-)compiled.
    int64_t compile_count = 0;

    // The number of times this cluster has been executed.
    int64_t execution_count = 0;

    // Cumulative time spent compiling the cluster.
    int64_t cumulative_compile_time_us = 0;

    // True if we have decided that this cluster is too dynamic (i.e. its shapes
    // change too frequently) to profitably JIT compile.  Once a cluster is

			select {
			case <-dctx.Done():
				if !timeout.Stop() {
					<-timeout.C
				}
			case <-timeout.C:
				spent := time.Since(started)
				goOffline(fmt.Errorf("unable to write+read for %v", spent.Round(time.Millisecond)), spent)
			}
		}()

		func() {
			defer dcancel()

			err := p.storage.WriteAll(ctx, minioMetaTmpBucket, fn, toWrite)
			if err != nil {

    "model_path");
auto* load_latency_by_stage = monitoring::Sampler<2>::New(
    {
        "/tensorflow/cc/saved_model/load_latency_by_stage",  // metric name
        "Distribution of wall time spent (in microseconds) in each stage "
        "(restore graph from disk, run init graph op, etc) when loading the "
        "model",
        "model_path",
        "stage",
    },

You can also measure the cumulative time your build agents spent building a changeset, which will give you a sense of the amount of work the CI infrastructure has to exert. The cache's effect here is less money spent on CI resources, as you don't need as many CI agents to maintain the same number of changes built.

using tpu::MlirToHloArgs;
using tpu::ShardingAndIndex;

auto* phase2_bridge_compilation_time = tsl::monitoring::Sampler<1>::New(
    {"/tensorflow/core/tf2xla/api/v2/phase2_compilation_time",
     "The wall-clock time spent on executing graphs in milliseconds.",
     "configuration"},
    // Power of 1.5 with bucket count 45 (> 23 hours)
    {tsl::monitoring::Buckets::Exponential(1, 1.5, 45)});

	"istio.io/istio/pkg/test/util/yml"
)

func (i *istioImpl) Close() error {
	t0 := time.Now()
	scopes.Framework.Infof("=== BEGIN: Cleanup Istio [Suite=%s] ===", i.ctx.Settings().TestID)

	// Write time spent for cleanup and deploy to ARTIFACTS/trace.yaml and logs to allow analyzing test times
	defer func() {
		delta := time.Since(t0)
		i.ctx.RecordTraceEvent("istio-cleanup", delta.Seconds())

            // It isn't clear if storing them uncompressed too would bring a performance benefit,
            // as reading less from the disk may save more time than spent unpacking.
            return compressionMethod != CompressionMethod.STORED;
        }

        private static long computeCrc32Of(byte[] contents) {
            return Hashing.crc32().hashBytes(contents).padToLong();