final long freeMemory = Runtime.getRuntime().freeMemory();
        final String msg = String.format(
                "%d words from %d %s: {\"time\":{\"parse\":%d,\"index\":%d},\"cpu\":%f,\"mem\":{\"heap\":\"%dmb\",\"used\":\"%dmb\"}}",
                items.length, size, type, parseTime, indexTime, cpuLoad, maxMemory / (1024 * 1024),
                (maxMemory - freeMemory) / (1024 * 1024));

- The CPU Manager will now validate the state of the node,  enabling Kubernetes to maintain the CPU topology even if resources change. ([#66718](https://github.com/kubernetes/kubernetes/pull/66718), [@ipuustin](https://github.com/ipuustin))

  //   have observed 12 micros on 64-bit linux systems to wake up a parked thread). So if the
  //   timeout is small we shouldn't park(). This needs to be traded off with the cpu overhead of
  //   spinning, so we use SPIN_THRESHOLD_NANOS which is what AbstractQueuedSynchronizer uses for
  //   similar purposes.
  // * We want to behave reasonably for timeouts of 0

  //   have observed 12 micros on 64-bit linux systems to wake up a parked thread). So if the
  //   timeout is small we shouldn't park(). This needs to be traded off with the cpu overhead of
  //   spinning, so we use SPIN_THRESHOLD_NANOS which is what AbstractQueuedSynchronizer uses for
  //   similar purposes.
  // * We want to behave reasonably for timeouts of 0

  <mime-type type="application/vnd.ctct.ws+xml"/>
  <mime-type type="application/vnd.cups-pdf"/>
  <mime-type type="application/vnd.cups-postscript"/>
  <mime-type type="application/vnd.cups-ppd">
    <glob pattern="*.ppd"/>
  </mime-type>
  <mime-type type="application/vnd.cups-raster"/>
  <mime-type type="application/vnd.cups-raw"/>
  <mime-type type="application/vnd.curl.car">
    <glob pattern="*.car"/>

	}
}

func getMinIOProcessCPUTime() MetricDescription {
	return MetricDescription{
		Namespace: nodeMetricNamespace,
		Subsystem: processSubsystem,
		Name:      cpu,
		Help:      "Total user and system CPU time spent in seconds",
		Type:      counterMetric,
	}
}

func getMinioProcMetrics() *MetricsGroupV2 {
	mg := &MetricsGroupV2{
		cacheInterval: 10 * time.Second,
	}

 * either of these extremes, {@code Striped} allows the user to trade between required concurrency
 * and memory footprint. For example, if a set of tasks are CPU-bound, one could easily create a
 * very compact {@code Striped<Lock>} of {@code availableProcessors() * 4} stripes, instead of
 * possibly thousands of locks which could be created in a {@code Map<K, Lock>} structure.
 *

        TF_NewOperation(host_graph_, func_name_, func_node_name_);
    for (auto input : inputs) {
      TF_AddInput(desc, input);
    }
    // Set device to CPU because some ops inside the function might not be
    // available on GPU.
    TF_SetDevice(desc, "/cpu:0");
    *op = TF_FinishOperation(desc, s_);
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
    ASSERT_NE(*op, nullptr);
  }

  FunctionDef fdef() {

          "interval": "",
          "legendFormat": "{{server}}",
          "range": true,
          "refId": "A"
        }
      ],
      "title": "CPU Usage",
      "type": "gauge"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "${DS_PROMETHEUS}"
      },

- Fixed relative CPU priority for pods where containers explicitly request zero cpu by giving the lowest priority instead of falling back to the cpu limit to avoid possible cpu starvation of other pods. ([#108832](https://github.com/kubernetes/kubernetes/pull/108832), [@waynepeking348](https://github.com/waynepeking348))