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

Then it's finally your turn, you place your order of 2 very fancy burgers for your crush and you.

You pay 💸.

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

The cashier goes to the kitchen.

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

            status, TF_INVALID_ARGUMENT,
            absl::StrCat(
                "Got a non-parallel tensor ",
                tensorflow::unwrap(absl::get<TFE_TensorHandle*>(input))
                    ->DebugString(),
                " as input to a parallel operation. First pack non-parallel "
                "tensors for each device into a parallel tensor explicitly.")
                .c_str());
        return absl::nullopt;
      }

      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()

        }
        try {
            final long start = System.currentTimeMillis();
            final Stream<QueryLog> stream = Stream.of(queryLogs);
            if (parallel) {
                stream.parallel();
            }
            final SuggestItem[] array = stream
                    .flatMap(queryLog -> contentsParser

/**
 * A class to represent the set of edges connecting an (implicit) origin node to a target node.
 *
 * <p>The {@link #nodeToOutEdge} map means this class only works on networks without parallel edges.
 * See {@link MultiEdgesConnecting} for a class that works with parallel edges.
 *
 * @author James Sexton
 * @param <E> Edge parameter type
 */
@ElementTypesAreNonnullByDefault
final class EdgesConnecting<E> extends AbstractSet<E> {

// two component devices, running `x = TPUReplicatedInput(inputs=[a, b])` on the
// parallel device creates a parallel tensor `x` with `a` on the first of
// `underlying_devices` and `b` on the second. Running `a_unpacked, b_unpacked =
// TPUReplicatedOutput(input=x, num_replicas=2)` un-packs the parallel tensor
// into its components.
//
// The filled `device` struct and the allocated `device_info` struct may be

  "durations" : [ {
    "testProject" : "excludeRuleMergingBuild",
    "linux" : 229
  } ]
}, {
  "scenario" : "org.gradle.performance.regression.corefeature.LargeDependencyGraphPerformanceTest.resolve large dependency graph (parallel = false, locking = true)",
  "durations" : [ {

        // each thread would cause collectives to deadlock. For consistency we
        // create a new sync executor for every thread.
        //
        // TODO(allenl): We should have an async API that works with the
        // parallel device.
        device_(device),
        executor_(
            TFE_NewExecutor(is_async, /*enable_streaming_enqueue=*/true,
                            /*in_flight_nodes_limit=*/in_flight_nodes_limit)),

        }
    }

    public Date getStartTime() {
        return request.getStartTime();
    }

    public boolean isParallel() {
        return parallel;
    }

    public void setParallel(boolean parallel) {
        this.parallel = parallel;
    }

    public RepositorySystemSession getRepositorySession() {
        return repositorySystemSession;
    }