* if any intersection is found. The sizes of both arguments are assumed to be so small, and the
 * likelihood of an intersection so great, that it is not worth the CPU cost of sorting or the
 * memory cost of hashing.
 */
internal fun Array<String>.hasIntersection(
  other: Array<String>?,
  comparator: Comparator<in String>,
): Boolean {

## Deployment Concepts { #deployment-concepts }

Here you saw how to use multiple **workers** to **parallelize** the execution of the application, take advantage of **multiple cores** in the CPU, and be able to serve **more requests**.

各プロセスの **PID** が表示され、親プロセスの `27365` (これは **プロセスマネージャ**) と、各ワーカー・プロセスの **PID** が表示されます： `27368`、`27369`、`27370`、`27367`になります。

## デプロイメントのコンセプト

ここでは、アプリケーションの実行を**並列化**し、CPUの**マルチコア**を活用し、**より多くのリクエスト**に対応できるようにするために、**Gunicorn**（またはUvicorn）を使用して**Uvicornワーカー・プロセス**を管理する方法を見ていきました。

上記のデプロイのコンセプトのリストから、ワーカーを使うことは主に**レプリケーション**の部分と、**再起動**を少し助けてくれます：

* セキュリティ - HTTPS
* 起動時の実行
* 再起動

These "env" files match up with an environment matrix that roughly covers:

-   Different Python versions
-   Linux, MacOS, and Windows machines (these pool definitions are internal)
-   x86 and arm64
-   CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs

## How to Test Your Changes to TensorFlow

You may check how your changes will affect TensorFlow by:

1. Creating a PR and observing the presubmit test results

	for {
		if err := sm.load(ctx, newObjectLayerFn()); err == nil {
			<-ctx.Done()
			return
		}
		duration := max(time.Duration(r.Float64()*float64(time.Second*10)),
			// Make sure to sleep at least a second to avoid high CPU ticks.
			time.Second)
		time.Sleep(duration)
	}
}

// load resync metrics saved on disk into memory
func (sm *siteResyncMetrics) load(ctx context.Context, objAPI ObjectLayer) error {
	if objAPI == nil {

## Conceitos de Implantação { #deployment-concepts }

Aqui você viu como usar vários **trabalhadores** para **paralelizar** a execução do aplicativo, aproveitar **vários núcleos** na CPU e conseguir atender **mais solicitações**.

 * 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

Yürütme süresinin çoğu (beklemek yerine) iş yapıldığından ve bilgisayardaki iş bir <abbr title="Central Processing Unit">CPU</abbr> tarafından yapıldığından, bu sorunlara "CPU bound" diyorlar".

---

CPU'ya bağlı işlemlerin yaygın örnekleri, karmaşık matematik işlemleri gerektiren işlerdir.

Örneğin:

* **Ses** veya **görüntü işleme**.

* die Header
* der Autorisierungsheader
* der `Authorization`-Header
* der Forwarded-Header

* das Dependency-Injection-System
* die Dependency
* das Dependable
* der Dependant

* I/O-lastig
* CPU-lastig
* Nebenläufigkeit
* Parallelität
* Multiprocessing

* die Umgebungsvariable
* die Umgebungsvariable
* der `PATH`
* die `PATH`-Umgebungsvariable

* die Authentifizierung

## Deployment-Konzepte { #deployment-concepts }

Hier haben Sie gesehen, wie Sie mehrere **Worker** verwenden, um die Ausführung der Anwendung zu **parallelisieren**, **mehrere Kerne** der CPU zu nutzen und in der Lage zu sein, **mehr Requests** zu bearbeiten.