uniqueTestString := fmt.Sprintf("global-mount-%s", testName)
			uniquePodDir := fmt.Sprintf("%s-%x", kubeletPodsDir, md5.Sum([]byte(uniqueTestString)))
			t.Run(testName+"[", func(t *testing.T) {
				t.Parallel()
				pv := &v1.PersistentVolume{
					ObjectMeta: metav1.ObjectMeta{
						Name: tc.volumeName,
						UID:  "pvuid",
					},
					Spec: v1.PersistentVolumeSpec{

    if (op->hasAttr(kXlaOutsideCompilationAttr)) {
      op->removeAttr(
          StringAttr::get(op->getContext(), kXlaOutsideCompilationAttr));
    }
  });
}

// The results of parallel executes is the combination of return values from
// both host and device.
llvm::SmallVector<Type, 4> GetParallelExecuteResultsTypes(
    ArrayRef<Value> return_value_from_host,

			// By parallelizing test cases we ensure that the order in which test cases are defined
			// doesn't automatically preclude some scenarios from happening.
			t.Parallel()

			out := &example.Pod{}
			err := store.Get(ctx, tt.key, storage.GetOptions{IgnoreNotFound: tt.ignoreNotFound, ResourceVersion: tt.rv}, out)
			if tt.expectNotFoundErr {

	for i, line := range lines {
		lines[i] = strings.SplitN(line, "] ", 2)[1]
	}
	return lines
}

func TestGetEncryptionConfigHash(t *testing.T) {
	t.Parallel()

	tests := []struct {
		name     string
		filepath string
		wantHash string
		wantErr  string
	}{
		{
			name:     "empty config file content",

            <pgp value="91268E187CDC92936DF90106AC01D965C791B637"/>
         </artifact>
      </component>
      <component group="org.scala-lang.modules" name="scala-parallel-collections_2.13" version="0.2.0">
         <artifact name="scala-parallel-collections_2.13-0.2.0.jar">
            <pgp value="C44A68FD10FF456C91E2757D18088D07854014B3"/>
         </artifact>
      </component>

// necessarily themselves be in "all". (That configuration does not arise in Go
// 1.11–1.15, but it will be possible in Go 1.16+.)
//
// Loading proceeds from the roots, using a parallel work-queue with a limit on
// the amount of active work (to avoid saturating disks, CPU cores, and/or
// network connections). Each package is added to the queue the first time it is

[[checkstyle_worker_api]]
==== Checkstyle tasks use toolchains and execute in parallel by default

The <<checkstyle_plugin.adoc#checkstyle_plugin,Checkstyle plugin>> now uses the Gradle worker API to run Checkstyle as an external worker process.
Multiple Checkstyle tasks may now run in parallel within a project.

				tt := tt
				t.Run(tt.name, func(t *testing.T) {
					if tt.oldestWins {
						test.SetForTest(t, &features.PersistOldestWinsHeuristicForVirtualServiceHostMatching, true)
					} else {
						t.Parallel() // feature flags and parallel tests don't mix
					}
					cfg := knownServices
					for _, tc := range tt.cfg {
						cfg = cfg + "\n---\n" + tc.Config(t, variant)
					}
					istio, k, err := crd.ParseInputs(cfg)

// license that can be found in the LICENSE file.

// Garbage collector (GC).
//
// The GC runs concurrently with mutator threads, is type accurate (aka precise), allows multiple
// GC thread to run in parallel. It is a concurrent mark and sweep that uses a write barrier. It is
// non-generational and non-compacting. Allocation is done using size segregated per P allocation

	metadataShallowVersions := make([][]xlMetaV2ShallowVersion, len(rawFileInfos))
	var v2bufs [][]byte
	if !readData {
		v2bufs = make([][]byte, len(rawFileInfos))
	}

	// Read `xl.meta` in parallel across disks.
	for index := range rawFileInfos {
		rf := rawFileInfos[index]
		if rf.Buf == nil {
			continue
		}
		if !readData {
			// Save the buffer so we can reuse it.
			v2bufs[index] = rf.Buf