// highPrecisionTime represents a single point in time.
// On all systems except Windows, using time.Time is fine.
type highPrecisionTime struct {
	now time.Time
}

// highPrecisionTimeNow returns high precision time for benchmarking.
func highPrecisionTimeNow() highPrecisionTime {
	return highPrecisionTime{now: time.Now()}
}

// highPrecisionTimeSince returns duration since b.
func highPrecisionTimeSince(b highPrecisionTime) time.Duration {

//
// TODO: If Windows runtime implements high resolution timing then highPrecisionTime
// can be removed.
type highPrecisionTime struct {
	now int64
}

// highPrecisionTimeNow returns high precision time for benchmarking.
func highPrecisionTimeNow() highPrecisionTime {
	var t highPrecisionTime
	// This should always succeed for Windows XP and above.
	t.now = windows.QueryPerformanceCounter()
	return t
}

						PutByteBuffer(resp)
						n++
					}
					atomic.AddInt64(&ops, int64(n))
					atomic.AddInt64(&lat, latency)
				})
				spent := time.Since(t)
				if spent > 0 && n > 0 {
					// Since we are benchmarking n parallel servers we need to multiply by n.
					// This will give an estimate of the total ops/s.
					latency := float64(atomic.LoadInt64(&lat)) / float64(time.Millisecond)

        allowEmpty = false,
        description = "Command line option for the performance test task to enable profiling. For example `async-profiler`, `async-profiler-heap`, `async-profiler-all` or `jfr`. Use `none` for benchmarking only."
    )
}

object AdHocPerformanceScenarioLinux : AdHocPerformanceScenario(Os.LINUX)
object AdHocPerformanceScenarioWindows : AdHocPerformanceScenario(Os.WINDOWS)

	memprofilerate    = flag.Int64("memprofilerate", 0, "set runtime.MemProfileRate to `rate`")
	benchmarkFlag     = flag.String("benchmark", "", "set to 'mem' or 'cpu' to enable phase benchmarking")
	benchmarkFileFlag = flag.String("benchmarkprofile", "", "emit phase profiles to `base`_phase.{cpu,mem}prof")

	flagW ternaryFlag
	FlagW = new(bool) // the -w flag, computed in main from flagW
)

				stop := applyGCLoad(b)
				runOne(b)
				// Make sure to stop the timer before we wait! The load created above
				// is very heavy-weight and not easy to stop, so we could end up
				// confusing the benchmarking framework for small b.N.
				b.StopTimer()
				stop()
			})
		}
	}

	// Measure the cost of counting goroutines
	b.Run("small-nil", run(func() bool {
		GoroutineProfile(nil)
		return true

        int stride = 8;
        int minLength = Math.min(left.length, right.length);
        int strideLimit = minLength & ~(stride - 1);
        int i;

        /*
         * Compare 8 bytes at a time. Benchmarking on x86 shows a stride of 8 bytes is no slower
         * than 4 bytes even on 32-bit. On the other hand, it is substantially faster on 64-bit.
         */
        for (i = 0; i < strideLimit; i += stride) {

        int stride = 8;
        int minLength = Math.min(left.length, right.length);
        int strideLimit = minLength & ~(stride - 1);
        int i;

        /*
         * Compare 8 bytes at a time. Benchmarking on x86 shows a stride of 8 bytes is no slower
         * than 4 bytes even on 32-bit. On the other hand, it is substantially faster on 64-bit.
         */
        for (i = 0; i < strideLimit; i += stride) {

   * elements will appear in the returned list in the same order they appeared in {@code elements}.
   *
   * <p><b>Performance note:</b> According to our
   * benchmarking
   * on Open JDK 7, {@link #immutableSortedCopy} generally performs better (in both time and space)
   * than this method, and this method in turn generally performs better than copying the list and

   * elements will appear in the returned list in the same order they appeared in {@code elements}.
   *
   * <p><b>Performance note:</b> According to our
   * benchmarking
   * on Open JDK 7, {@link #immutableSortedCopy} generally performs better (in both time and space)
   * than this method, and this method in turn generally performs better than copying the list and