## Recapitular

Você pode usar vários processos de trabalho com a opção CLI `--workers` com os comandos `fastapi` ou `uvicorn` para aproveitar as vantagens de **CPUs multi-core** e executar **vários processos em paralelo**.

Você pode usar essas ferramentas e ideias se estiver configurando **seu próprio sistema de implantação** enquanto cuida dos outros conceitos de implantação.

## Zusammenfassung

Sie können **Gunicorn** (oder auch Uvicorn) als Prozessmanager mit Uvicorn-Workern verwenden, um **Multikern-CPUs** zu nutzen und **mehrere Prozesse parallel** auszuführen.

Sie können diese Tools und Ideen nutzen, wenn Sie **Ihr eigenes Deployment-System** einrichten und sich dabei selbst um die anderen Deployment-Konzepte kümmern.

- [PRF](#prf): HMAC-SHA-256
- [AEAD](#aead): AES-256-GCM if the CPU supports AES-NI, ChaCha20-Poly1305 otherwise. More specifically AES-256-GCM is only selected for X86-64 CPUs with AES-NI extension.

Further any secret key (apart from the KMS-generated ones) is 256 bits long. The KMS-generated keys may be 256 bits but this depends on the KMS capabilities and configuration.

	aoMetricsGroup         = grid.NewArrayOf[*MetricV2](func() *MetricV2 { return &MetricV2{} })
	madminBgHealState      = grid.NewJSONPool[madmin.BgHealState]()
	madminCPUs             = grid.NewJSONPool[madmin.CPUs]()
	madminMemInfo          = grid.NewJSONPool[madmin.MemInfo]()
	madminNetInfo          = grid.NewJSONPool[madmin.NetInfo]()
	madminOSInfo           = grid.NewJSONPool[madmin.OSInfo]()

      TFE_NewContext(opts.get(), status.get()), TFE_DeleteContext);
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  // Create a parallel device with two CPUs
  const char* first_device_name =
      "/job:localhost/replica:0/task:0/device:CUSTOM:0";
  std::array<const char*, 2> first_underlying_devices{
      "/job:localhost/replica:0/task:0/device:CPU:0",

///

## Ressourcennutzung

Ihr(e) Server ist (sind) eine **Ressource**, welche Sie mit Ihren Programmen, der Rechenzeit auf den CPUs und dem verfügbaren RAM-Speicher verbrauchen oder **nutzen** können.

			peersLogOnceIf(logger.SetReqInfo(ctx, reqInfo), nErr.Err, nErr.Host.String())
		}
	}
}

// GetCPUs - Get all CPU information.
func (sys *NotificationSys) GetCPUs(ctx context.Context) []madmin.CPUs {
	reply := make([]madmin.CPUs, len(sys.peerClients))

	g := errgroup.WithNErrs(len(sys.peerClients))
	for index, client := range sys.peerClients {
		if client == nil {
			continue
		}
		index := index

in the pictures of him), while the rest waited in silence.  At
last the Dodo said, `EVERYBODY has won, and all must have
prizes.'

  `But who is to give the prizes?' quite a chorus of voices
asked.

  `Why, SHE, of course,' said the Dodo, pointing to Alice with
one finger; and the whole party at once crowded round her,
calling out in a confused way, `Prizes! Prizes!'

   char *s = s1;
 
   /* Find the end of S1.  */
-  do
-    c = *s1++;
-  while (c != '\0');
-
-  /* Make S1 point before next character, so we can increment
-     it while memory is read (wins on pipelined cpus).  */
-  s1 -= 2;
+  s1 += strlen (s1);
 
-  if (n >= 4)
-    {
-      size_t n4 = n >> 2;
-      do
-	{
-	  c = *s2++;
-	  *++s1 = c;
-	  if (c == '\0')
-	    return s;

///

## Resource Utilization

Your server(s) is (are) a **resource**, you can consume or **utilize**, with your programs, the computation time on the CPUs, and the RAM memory available.

How much of the system resources do you want to be consuming/utilizing? It might be easy to think "not much", but in reality, you will probably want to consume **as much as possible without crashing**.