t.Errorf(".spec.suspend does not match; -want,+got:\n%s", diff)
			}
			validateDefaultInt32(t, "Completions", actual.Spec.Completions, expected.Spec.Completions)
			validateDefaultInt32(t, "Parallelism", actual.Spec.Parallelism, expected.Spec.Parallelism)
			validateDefaultInt32(t, "BackoffLimit", actual.Spec.BackoffLimit, expected.Spec.BackoffLimit)

			if diff := cmp.Diff(expected.Spec.PodFailurePolicy, actual.Spec.PodFailurePolicy); diff != "" {

						SchedulerName: ptr.To("default-scheduler"),
					},
				},
			},
		},
		{
			name: "set non default parallelism",
			config: &configv1.KubeSchedulerConfiguration{
				Parallelism: ptr.To[int32](8),
			},
			expected: &configv1.KubeSchedulerConfiguration{
				Parallelism: ptr.To[int32](8),
				DebuggingConfiguration: componentbaseconfig.DebuggingConfiguration{
					EnableProfiling:           &enable,

	}
	return &jobStorage, server
}

func validNewJob() *batch.Job {
	completions := int32(1)
	parallelism := int32(1)
	return &batch.Job{
		ObjectMeta: metav1.ObjectMeta{
			Name:      "foo",
			Namespace: "default",
		},
		Spec: batch.JobSpec{
			Completions: &completions,
			Parallelism: &parallelism,
			Selector: &metav1.LabelSelector{
				MatchLabels: map[string]string{"a": "b"},
			},

# fail due to resource contention. 8 is a reasonable default for a
# e2-standard-2 node.
# Currently, parallelism only affects when REMOTE=true. For local test,
# ginkgo default parallelism (cores - 1) is used.
parallelism=${PARALLELISM:-8}
artifacts="${ARTIFACTS:-"/tmp/_artifacts/$(date +%y%m%dT%H%M%S)"}"
remote=${REMOTE:-"false"}
remote_mode=${REMOTE_MODE:-"gce"}

		wantErrs field.ErrorList
	}{
		"good": {
			config: validConfig,
		},
		"bad-parallelism-invalid-value": {
			config: invalidParallelismValue,
			wantErrs: field.ErrorList{
				&field.Error{
					Type:  field.ErrorTypeInvalid,
					Field: "parallelism",
				},
			},
		},
		"bad-resource-name-not-set": {
			config: resourceNameNotSet,
			wantErrs: field.ErrorList{

	}

	profilesPath := field.NewPath("profiles")
	if cc.Parallelism <= 0 {
		errs = append(errs, field.Invalid(field.NewPath("parallelism"), cc.Parallelism, "should be an integer value greater than zero"))
	}

	if len(cc.Profiles) == 0 {
		errs = append(errs, field.Required(profilesPath, ""))
	} else {

This idea of **asynchronous** code described above is also sometimes called **"concurrency"**. It is different from **"parallelism"**.

**Concurrency** and **parallelism** both relate to "different things happening more or less at the same time".

But the details between *concurrency* and *parallelism* are quite different.

To see the difference, imagine the following story about burgers:

### Concurrent Burgers

			},
			expectedUsername: "config",
			expectedConfig: kubeschedulerconfig.KubeSchedulerConfiguration{
				TypeMeta: metav1.TypeMeta{
					APIVersion: v1.SchemeGroupVersion.String(),
				},
				Parallelism:           16,
				DelayCacheUntilActive: false,
				DebuggingConfiguration: componentbaseconfig.DebuggingConfiguration{
					EnableProfiling:           true,
					EnableContentionProfiling: true,
				},

	return func(o *schedulerOptions) {
		o.profiles = p
		o.applyDefaultProfile = false
	}
}

// WithParallelism sets the parallelism for all scheduler algorithms. Default is 16.
func WithParallelism(threads int32) Option {
	return func(o *schedulerOptions) {
		o.parallelism = threads
	}
}

// WithPercentageOfNodesToScore sets percentageOfNodesToScore for Scheduler.

        devices) {
  int64_t num_replicas = replicate.getN();
  assert(new_inputs.size() == num_replicas);

  // As model parallelism is not yet supported, we assume that all ops are
  // placed in logical core 0.
  // TODO(b/148913020): Remove this constraint once model parallelism is
  // supported.
  assert(devices.find(tensorflow::GetDeviceAliasForLogicalCore(0))
             ->getSecond()