E a maior parte do tempo de execução é tomada por trabalho (ao invés de ficar esperando), e o trabalho em um computador é feito pela <abbr title="Unidade de Processamento Central">CPU</abbr>, que podem gerar problemas que são chamados de "limite de CPU".

---

Exemplos comuns de limite de CPU são coisas que exigem processamento matemático complexo.

Por exemplo:

    id("base")
}
----
=====
[.multi-language-sample]
=====
.app/build.gradle
[source,groovy]
----
plugins {
    id('base')
}
----
=====
====

The `base` plugin adds central lifecycle tasks.
Now when we run `./gradlew app:tasks`, we can see the `assemble` and `build` tasks are available:

[source,text]
----
$ ./gradlew :app:tasks

> Task :app:tasks

            such as method definitions and class definitions.
        </para>
    </section>

    <section>
        <title>Core types</title>
        <para>Listed below are some of the central types which are used in Gradle scripts:</para>
        <table>
            <title>Core types</title>
            <tr>
                <td>org.gradle.api.Project</td>
            </tr>
            <tr>

            <pgp value="CB3808E03CD602A6DC5B882552931F4B72B4F54C"/>
         </artifact>
      </component>
      <component group="net.rubygrapefruit" name="ansi-control-sequence-util" version="0.3">
         <artifact name="ansi-control-sequence-util-0.3.jar">
            <pgp value="5B131E826582CF79510DAA11CD3E539F208832D0"/>
         </artifact>
      </component>

 *   <li>These tests are "conformance tests", and do not attempt to test throughput, latency,
 *       scalability or other performance factors (see the separate "jtreg" tests for a set intended
 *       to check these for the most central aspects of functionality.) So, most tests use the
 *       smallest sensible numbers of threads, collection sizes, etc needed to check basic
 *       conformance.

And as most of the execution time is taken by actual work (instead of waiting), and the work in a computer is done by a <abbr title="Central Processing Unit">CPU</abbr>, they call these problems "CPU bound".

---

Common examples of CPU bound operations are things that require complex math processing.

For example:

Und da die meiste Ausführungszeit durch tatsächliche Arbeit (anstatt durch Warten) in Anspruch genommen wird und die Arbeit in einem Computer von einer <abbr title="Central Processing Unit – Zentrale Recheneinheit">CPU</abbr> erledigt wird, werden diese Probleme als „CPU-lastig“ („CPU bound“) bezeichnet.

---

无论是否轮流执行（并发），都需要相同的时间来完成，而你也会完成相同的工作量。

但在这种情况下，如果你能带上 8 名前收银员/厨师，现在是清洁工一起清扫，他们中的每一个人（加上你）都能占据房子的一个区域来清扫，你就可以在额外的帮助下并行的更快地完成所有工作。

在这个场景中，每个清洁工（包括您）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">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；

		`Australia/Victoria`,
		`Australia/West`,
		`Australia/Yancowinna`,
		`Brazil/Acre`,
		`Brazil/DeNoronha`,
		`Brazil/East`,
		`Brazil/West`,
		`Canada/Atlantic`,
		`Canada/Central`,
		`Canada/Eastern`,
		`Canada/Mountain`,
		`Canada/Newfoundland`,
		`Canada/Pacific`,
		`Canada/Saskatchewan`,
		`Canada/Yukon`,
		`CET`,
		`Chile/Continental`,
		`Chile/EasterIsland`,

	s.freeIndexForScan = 1

	// Set up the range for allocation.
	s.userArenaChunkFree = makeAddrRange(base, base+s.elemsize)

	// Put the large span in the mcentral swept list so that it's
	// visible to the background sweeper.
	h.central[spc].mcentral.fullSwept(h.sweepgen).push(s)

	// Set up an allocation header. Avoid write barriers here because this type
	// is not a real type, and it exists in an invalid location.