*   `tf.raw_ops.Bucketize` op on CPU.
        *   `tf.where` op for data types
            `tf.int32`/`tf.uint32`/`tf.int8`/`tf.uint8`/`tf.int64`.
        *   `tf.random.normal` op for output data type `tf.float32` on CPU.
        *   `tf.random.uniform` op for output data type `tf.float32` on CPU.
        *   `tf.random.categorical` op for output data type `tf.int64` on CPU.

*   `tensorflow.experimental.tensorrt`:

	systemDriveCollectorPath            collectorPath = "/system/drive"
	systemMemoryCollectorPath           collectorPath = "/system/memory"
	systemCPUCollectorPath              collectorPath = "/system/cpu"
	systemProcessCollectorPath          collectorPath = "/system/process"

	debugGoCollectorPath collectorPath = "/debug/go"

	clusterHealthCollectorPath       collectorPath = "/cluster/health"

 * 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

  }

  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")

      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;
      }

    }

    public void test_getUserAgentType_safariWithoutChrome() {
        getMockRequest().addHeader("user-agent",
                "Mozilla/5.0 (iPhone; CPU iPhone OS 14_6 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.1.1 Mobile/15E148 Safari/604.1");
        assertEquals(UserAgentType.SAFARI, userAgentHelper.getUserAgentType());
    }

Et comme la plupart du temps d'exécution est pris par du "vrai" travail (et non de l'attente), et que le travail dans un ordinateur est fait par un <abbr title="Central Processing Unit">CPU</abbr>, ce sont des problèmes dits "CPU bound".

---

Des exemples communs d'opérations "CPU bounds" sont les procédés qui requièrent des traitements mathématiques complexes.

Par exemple :

* Traitements d'**audio** et d'**images**.

        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

## Conceptos de Despliegue

Aquí viste cómo usar múltiples **workers** para **paralelizar** la ejecución de la aplicación, aprovechar los **múltiples núcleos** del CPU, y poder servir **más requests**.

De la lista de conceptos de despliegue de antes, usar workers ayudaría principalmente con la parte de **replicación**, y un poquito con los **reinicios**, pero aún necesitas encargarte de los otros: