}
	}
	uaAppend("; ", "")

	if cpus, err := gopsutilcpu.Info(); err == nil && len(cpus) > 0 {
		cpuMap := make(map[string]struct{}, len(cpus))
		coreMap := make(map[string]struct{}, len(cpus))
		for i := range cpus {
			cpuMap[cpus[i].PhysicalID] = struct{}{}
			coreMap[cpus[i].CoreID] = struct{}{}
		}
		cpu := cpus[0]

   * failed CAS on base update), the table is initialized to size 2.
   * The table size is doubled upon further contention until
   * reaching the nearest power of two greater than or equal to the
   * number of CPUS. Table slots remain empty (null) until they are
   * needed.
   *
   * A single spinlock ("busy") is used for initializing and
   * resizing the table, as well as populating slots with new Cells.

   * failed CAS on base update), the table is initialized to size 2.
   * The table size is doubled upon further contention until
   * reaching the nearest power of two greater than or equal to the
   * number of CPUS. Table slots remain empty (null) until they are
   * needed.
   *
   * A single spinlock ("busy") is used for initializing and
   * resizing the table, as well as populating slots with new Cells.

net.ipv4.tcp_mtu_probing=1
net.ipv4.tcp_base_mss=1280

# Disable ipv6
net.ipv6.conf.all.disable_ipv6=1
net.ipv6.conf.default.disable_ipv6=1
net.ipv6.conf.lo.disable_ipv6=1

[bootloader]
# Avoid firing timers for all CPUs at the same time. This is irrelevant for
# full nohz systems

        assertThrows(IllegalArgumentException.class, () -> cli.calculateDegreeOfConcurrency("XXXC"));

        int cpus = Runtime.getRuntime().availableProcessors();
        assertEquals((int) (cpus * 2.2), cli.calculateDegreeOfConcurrency("2.2C"));
        assertEquals(1, cli.calculateDegreeOfConcurrency("0.0001C"));

## Recap

You can use multiple worker processes with the `--workers` CLI option with the `fastapi` or `uvicorn` commands to take advantage of **multi-core CPUs**, to run **multiple processes in parallel**.

You could use these tools and ideas if you are setting up **your own deployment system** while taking care of the other deployment concepts yourself.

## 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.

	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",