And as most of the execution time is taken by actual work (instead of waiting), and the work in a computer is done by a <abbr title="Central Processing Unit">CPU</abbr>, they call these problems "CPU bound".

---

Common examples of CPU bound operations are things that require complex math processing.

For example:

* **Audio** or **image processing**.

                        logger.info("Cpu Load {}% is greater than {}%. {} waiting thread(s). {} thread is timed out.", current, percent,
                                waitingThreadNames.size(), threadName);
                    }
                    return false;
                }
                if (logger.isInfoEnabled()) {

您还可以看到它显示了每个进程的 **PID**，父进程（这是 **进程管理器**）的 PID 为`27365`，每个工作进程的 PID 为：`27368`、`27369`， `27370`和`27367`。

## 部署概念

在这里，您了解了如何使用 **Gunicorn**（或 Uvicorn）管理 **Uvicorn 工作进程**来**并行**应用程序的执行，利用 CPU 中的 **多核**，并 能够满足**更多请求**。

从上面的部署概念列表来看，使用worker主要有助于**复制**部分，并对**重新启动**有一点帮助，但您仍然需要照顾其他部分：

* **安全 - HTTPS**
* **启动时运行**
* ***重新启动***
* 复制（运行的进程数）
* **内存**
* **启动之前的先前步骤**

## 容器和 Docker

}

var testCases = []struct {
	name  string
	array bool
}{
	{
		name: "parking-citations-10",
	},
}

func TestNDJSON(t *testing.T) {
	if !simdjson.SupportedCPU() {
		t.Skip("Unsupported cpu")
	}

	for _, tt := range testCases {
		t.Run(tt.name, func(t *testing.T) {
			ref := loadCompressed(t, tt.name)

			var err error
			dst := make(chan simdjson.Object, 100)

      return false;
    }

    if (!finalizeReference(firstReference, finalizeReferentMethod)) {
      return false;
    }

    /*
     * Loop as long as we have references available so as not to waste CPU looking up the Method
     * over and over again.
     */
    while (true) {
      Reference<?> furtherReference = queue.poll();
      if (furtherReference == null) {
        return true;
      }

 * Generally speaking, this class reduces object allocation and memory consumption at the price of
 * moderately increased constant factors of CPU. Only use this class when there is a specific reason
 * to prioritize memory over CPU.
 *
 * @author Dimitris Andreou
 * @author Jon Noack
 */
@GwtIncompatible // not worth using in GWT for now
@ElementTypesAreNonnullByDefault

  }

  auto* gpu_options = config.mutable_gpu_options();
  gpu_options->set_allow_growth(gpu_memory_allow_growth);

  (*config.mutable_device_count())["CPU"] = num_cpu_devices;

  // TODO(b/113217601): This is needed for EagerContext::runner_ to use a
  // threadpool, so that we avoid the possibility of running the runner_ in the

	processLocksWriteTotalMD          = NewGaugeMD(processLocksWriteTotal, "Number of current WRITE locks on this peer")
	processCPUTotalSecondsMD          = NewCounterMD(processCPUTotalSeconds, "Total user and system CPU time spent in seconds")
	processGoRoutineTotalMD           = NewGaugeMD(processGoRoutineTotal, "Total number of go routines running")

        blocker = (Blocker) state;
      }
      spinCount++;
      if (spinCount > MAX_BUSY_WAIT_SPINS) {
        /*
         * If we have spun a lot, just park ourselves. This will save CPU while we wait for a slow
         * interrupting thread. In theory, interruptTask() should be very fast, but due to
         * InterruptibleChannel and JavaLangAccess.blockedOn(Thread, Interruptible), it isn't

```

</div>

Vous disposez maintenant d'un serveur FastAPI optimisé dans un conteneur Docker. Configuré automatiquement pour votre
serveur actuel (et le nombre de cœurs du CPU).

## Vérifier