// we try hard to make them robust in practice.  We could additionally try to add in a system
    // load timeout multiplier.  Or we could try to use a CPU time bound instead of wall clock time
    // bound.  But these ideas are harder to implement.  We do not try to detect or handle a
    // user-specified -XX:+DisableExplicitGC.
    //
    // TODO(user): Consider using

    // we try hard to make them robust in practice.  We could additionally try to add in a system
    // load timeout multiplier.  Or we could try to use a CPU time bound instead of wall clock time
    // bound.  But these ideas are harder to implement.  We do not try to detect or handle a
    // user-specified -XX:+DisableExplicitGC.
    //
    // TODO(user): Consider using

               "intervalFactor": 2,
               "legendFormat": "{{cluster}} - {{namespace}}\n"
            }
         ],
         "title": "CPU Usage",
         "type": "timeseries"
      },
      {
         "datasource": {
            "type": "datasource",
            "uid": "-- Mixed --"
         },
         "fieldConfig": {
            "defaults": {

	g := errgroup.WithNErrs(len(buckets))
	bucketMetas := make([]BucketMetadata, len(buckets))
	for index := range buckets {
		index := index
		g.Go(func() error {
			// Sleep and stagger to avoid blocked CPU and thundering
			// herd upon start up sequence.
			time.Sleep(25*time.Millisecond + time.Duration(rand.Int63n(int64(100*time.Millisecond))))

// If source and destination are the same device, then this creates a new handle
// that shares the underlying buffer. Otherwise, it currently requires at least
// one of the source or destination devices to be CPU (i.e., for the source or
// destination tensor to be placed in host memory).
// If async execution is enabled, the copy may be enqueued and the call will
// return "non-ready" handle. Else, this function returns after the copy has

Setting `max_sleep` to a *lower* value and setting `max_io` to a *higher* value would make heal go faster.

Each node is responsible of healing its local drives; Each drive will have multiple heal workers which is the quarter of the number of CPU cores of the node or the quarter of the configured nr_requests of the drive (https://www.kernel.org/doc/Documentation/block/queue-sysfs.txt). It is also possible to provide a custom number of workers by using this command: `mc admin config set alias/...

このシナリオでは、清掃員 (あなたを含む) のそれぞれがプロセッサとなり、それぞれの役割を果たします。

また、実行時間のほとんどは (待機ではなく) 実際の作業に費やされ、コンピュータでの作業は<abbr title="Central Processing Unit">CPU</abbr>によって行われます。これらの問題は「CPUバウンド」と言います。

---

CPUバウンド操作の一般的な例は、複雑な数学処理が必要なものです。

例えば:

* **オーディオ** や **画像処理**。
* **コンピュータビジョン**: 画像は数百万のピクセルで構成され、各ピクセルには3つの値/色があり、通常、これらのピクセルで何かを同時に計算する必要がある処理。

                                   status.get()),
      TFE_DeleteTensorHandle);
  ASSERT_TRUE(arrived);
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

  // Base case: two CPU inputs executes fine.
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> matmul(
      MatMulOp(context.get(), hcpu.get(), hcpu.get()), TFE_DeleteOp);
  TFE_TensorHandle* retval;
  int num_retvals = 1;

The Resource Management Working Group graduated three features to beta in the 1.10 release.  First, [CPU Manager](https://kubernetes.io/docs/tasks/administer-cluster/cpu-management-policies/), which allows users to request exclusive CPU cores.  This helps performance in a variety of use-cases, including network latency sensitive applications, as well as applications that benefit from CPU cache residency.  Next, [Huge Pages](https://kubernetes.io/docs/tasks/manage-hugepages/scheduling-hugepages/),...

Безусловно, на этом же сервере будут работать и **другие процессы**, которые не относятся к вашему приложению.

Интересная деталь заключается в том, что процент **использования центрального процессора (CPU)** каждым процессом может сильно меняться с течением времени, но объём занимаемой **оперативной памяти (RAM)** остаётся относительно **стабильным**.