在这个场景中，每个清洁工（包括你）都将是一个处理器，完成这个工作的一部分。

由于大多数执行时间是由实际工作（而不是等待）占用的，并且计算机中的工作是由 <abbr title="Central Processing Unit - 中央处理器">CPU</abbr> 完成的，所以他们称这些问题为"CPU 密集型"。

---

CPU 密集型操作的常见示例是需要复杂的数学处理。

例如：

* **音频**或**图像**处理；
* **计算机视觉**: 一幅图像由数百万像素组成，每个像素有3种颜色值，处理通常需要同时对这些像素进行计算；
* **机器学习**: 它通常需要大量的"矩阵"和"向量"乘法。想象一个包含数字的巨大电子表格，并同时将所有数字相乘；

    Optional<Boolean> alsoMakeDependents();

    /**
     * Returns the number of threads used for parallel builds.
     *
     * @return an {@link Optional} containing the number of threads (or "1C" for one thread per CPU core), or empty if not specified
     */
    @Nonnull
    Optional<String> threads();

    /**
     * Returns the id of the build strategy to use.
     *

      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;
      }

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

	debugGoCollectorPath collectorPath = "/debug/go"

	clusterHealthCollectorPath       collectorPath = "/cluster/health"

```
<abbr title="Internet of Things">IoT</abbr>
<abbr title="Central Processing Unit">CPU</abbr>
<abbr title="too long; didn't read"><strong>TL;DR:</strong></abbr>
```

Result (German):

```
<abbr title="Internet of Things - Internet der Dinge">IoT</abbr>
<abbr title="Central Processing Unit - Zentrale Verarbeitungseinheit">CPU</abbr>

## 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**.

  // Find and return a function record added by its name.
  virtual core::RefCountPtr<FunctionRecord> FindRecord(
      const string& name) const = 0;

  // Return the ParsedName of Host CPU device.
  virtual const DeviceNameUtils::ParsedName& HostCPUParsedName() const = 0;
  virtual const string& HostCPUName() const = 0;

  // Configure soft device placement policy.

	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 {

				} else {
					onlineServers++
				}
				mu.Unlock()
			}(clnt)
		}
		wg.Wait()

		select {
		case <-ctx.Done():
			return ctx.Err()
		default:
			// Sleep and stagger to avoid blocked CPU and thundering
			// herd upon start up sequence.
			time.Sleep(25*time.Millisecond + time.Duration(rand.Int63n(int64(100*time.Millisecond))))
			retries++
			// after 20 retries start logging that servers are not reachable yet

   * using a secondary hash (Marsaglia XorShift) to try to find a
   * free slot.
   *
   * The table size is capped because, when there are more threads
   * than CPUs, supposing that each thread were bound to a CPU,
   * there would exist a perfect hash function mapping threads to
   * slots that eliminates collisions. When we reach capacity, we
   * search for this mapping by randomly varying the hash codes of