* make it load its own version of FinalizableReferenceQueue. Then we need to interact with that
   * parallel version through reflection in order to exercise the parallel
   * FinalizableReferenceQueue, and then check that the parallel ClassLoader can be
   * garbage-collected after that.
   */

  public static class MyFinalizableWeakReference extends FinalizableWeakReference<Object> {

    "groups" : [ {
      "testProject" : "excludeRuleMergingBuild",
      "coverage" : {
        "per_commit" : [ "linux" ]
      }
    } ]
  }, {
    "testId" : "org.gradle.performance.regression.corefeature.LargeDependencyGraphPerformanceTest.resolve large dependency graph (parallel = false, locking = true)",
    "groups" : [ {

      log += "three:run@${taskFaker.nanoTime} parallel=${taskFaker.isParallel}"
    }

    taskFaker.advanceUntil(0.µs)
    assertThat(log).isEmpty()

    taskFaker.advanceUntil(100.µs)
    assertThat(log).containsExactlyInAnyOrder(
      "one:run@100000 parallel=true",
      "two:run@100000 parallel=true",
      "three:run@100000 parallel=true",
    )

    taskFaker.assertNoMoreTasks()

  "durations" : [ {
    "testProject" : "excludeRuleMergingBuild",
    "linux" : 233
  } ]
}, {
  "scenario" : "org.gradle.performance.regression.corefeature.LargeDependencyGraphPerformanceTest.resolve large dependency graph (parallel = false, locking = true)",
  "durations" : [ {

   * an edge connecting node A to node B if this {@link Network} has an edge connecting A to B.
   *
   * <p>If this network {@link #allowsParallelEdges() allows parallel edges}, parallel edges will be
   * treated as if collapsed into a single edge. For example, the {@link #degree(Object)} of a node
   * in the {@link Graph} view may be less than the degree of the same node in this {@link Network}.
   */

 * <li>{@link #roleFieldName} - Role field name.</li>
 * <li>{@link #langFieldName} - Language field name.</li>
 * <li>{@link #badWords} - List of bad words.</li>
 * <li>{@link #parallel} - Flag for parallel processing.</li>
 * <li>{@link #readingConverter} - Reading converter.</li>
 * <li>{@link #contentsReadingConverter} - Contents reading converter.</li>
 * <li>{@link #normalizer} - Normalizer.</li>

     * @param readingConverter The reading converter
     * @param contentsReadingConverter The contents reading converter
     * @param normalizer The normalizer
     * @param parallel Whether to use parallel processing
     */
    public ContentIndexingOperations(final Client client, final SuggestSettings settings, final ExecutorService threadPool,

<img src="/img/async/parallel-burgers/parallel-burgers-04.png" class="illustration">

그러다 점원/요리사 👨‍🍳 가 카운터 앞에서 오랫동안 기다린 🕙 끝에 마침내 햄버거를 가지고 돌아옵니다.

<img src="/img/async/parallel-burgers/parallel-burgers-05.png" class="illustration">

여러분은 햄버거를 받아 짝사랑 상대와 테이블로 갑니다.

그냥 먹고, 끝입니다. ⏹

<img src="/img/async/parallel-burgers/parallel-burgers-06.png" class="illustration">

<img src="/img/async/parallel-burgers/parallel-burgers-02.png" class="illustration">

收銀員走進廚房準備食物。

你站在櫃檯前等待 🕙，以免其他人先拿走你的漢堡，因為這裡沒有號碼牌系統。

<img src="/img/async/parallel-burgers/parallel-burgers-03.png" class="illustration">

由於你和戀人都忙著不讓別人搶走你的漢堡，等漢堡準備好時，你根本無法專心和戀人互動。😞

這是「同步」(synchronous)工作，你和收銀員/廚師 👨‍🍳 是「同步化」的。你必須等到 🕙 收銀員/廚師 👨‍🍳 完成漢堡並交給你的那一刻，否則別人可能會拿走你的餐點。

### 并行汉堡 { #parallel-burgers }

现在让我们假设不是"并发汉堡"，而是"并行汉堡"。

你和你的恋人一起去吃并行快餐。

你站在队伍中，同时是厨师的几个收银员（比方说8个）从前面的人那里接单。

你之前的每个人都在等待他们的汉堡准备好后才离开柜台，因为8名收银员都会在下一份订单前马上准备好汉堡。

<img src="/img/async/parallel-burgers/parallel-burgers-01.png" class="illustration">

然后，终于轮到你了，你为你的恋人和你订购了两个非常精美的汉堡。

你付钱了 💸。