* gce/kube-down: Parallelize IGM deletion, batch more ([#27302](https://github.com/kubernetes/kubernetes/pull/27302), [@zmerlynn](https://github.com/zmerlynn))
* Enable dynamic allocation of heapster/eventer cpu request/limit ([#27185](https://github.com/kubernetes/kubernetes/pull/27185), [@gmarek](https://github.com/gmarek))

				} else {
					onlineServers++
				}
				mu.Unlock()
			}(clnt)
		}
		wg.Wait()

		select {
		case <-ctx.Done():
			return ctx.Err()
		default:
			// Sleep and stagger to avoid blocked CPU and thundering
			// herd upon start up sequence.
			time.Sleep(25*time.Millisecond + time.Duration(rand.Int63n(int64(100*time.Millisecond))))
			retries++
			// after 20 retries start logging that servers are not reachable yet

        assertTrue(mockFilterChain.wasDoFilterCalled());
    }

    // ===================================================================================
    //                                                                  CPU Below Threshold
    //                                                                  ==================

    @Test
    public void test_doFilter_webBelowThreshold() throws IOException, ServletException {

이 시나리오에서 (여러분을 포함한) 각 청소부는 프로세서가 되어, 맡은 일을 수행합니다.

그리고 실행 시간의 대부분이 기다림이 아니라 실제 작업에 쓰이고, 컴퓨터에서 작업은 <abbr title="Central Processing Unit - 중앙 처리 장치">CPU</abbr>가 수행하므로, 이런 문제를 "CPU bound"라고 부릅니다.

---

CPU bound 작업의 흔한 예시는 복잡한 수학 처리가 필요한 것들입니다.

예를 들어:

* **오디오** 또는 **이미지** 처리
* **컴퓨터 비전**: 이미지는 수백만 개의 픽셀로 구성되며, 각 픽셀은 3개의 값/색을 갖습니다. 보통 그 픽셀들에 대해 동시에 무언가를 계산해야 합니다.

	github.com/gomodule/redigo v1.9.2
	github.com/google/uuid v1.6.0
	github.com/inconshreveable/mousetrap v1.1.0
	github.com/json-iterator/go v1.1.12
	github.com/klauspost/compress v1.18.0
	github.com/klauspost/cpuid/v2 v2.2.10
	github.com/klauspost/filepathx v1.1.1
	github.com/klauspost/pgzip v1.2.6
	github.com/klauspost/readahead v1.4.0
	github.com/klauspost/reedsolomon v1.12.4
	github.com/lib/pq v1.10.9

 * if any intersection is found. The sizes of both arguments are assumed to be so small, and the
 * likelihood of an intersection so great, that it is not worth the CPU cost of sorting or the
 * memory cost of hashing.
 */
internal fun Array<String>.hasIntersection(
  other: Array<String>?,
  comparator: Comparator<in String>,
): Boolean {

          sudo udevadm control --reload-rules
          sudo udevadm trigger --name-match=kvm

      - name: Verify KVM
        run: |
          sudo apt-get install -y cpu-checker
          kvm-ok || echo "KVM is not accelerated"
          kvm-ok || exit 1

      - name: Setup Gradle
        uses: gradle/actions/setup-gradle@v5

      - name: Gradle cache

	for {
		if err := sm.load(ctx, newObjectLayerFn()); err == nil {
			<-ctx.Done()
			return
		}
		duration := max(time.Duration(r.Float64()*float64(time.Second*10)),
			// Make sure to sleep at least a second to avoid high CPU ticks.
			time.Second)
		time.Sleep(duration)
	}
}

// load resync metrics saved on disk into memory
func (sm *siteResyncMetrics) load(ctx context.Context, objAPI ObjectLayer) error {
	if objAPI == nil {

* die Header
* der Autorisierungsheader
* der `Authorization`-Header
* der Forwarded-Header

* das Dependency-Injection-System
* die Dependency
* das Dependable
* der Dependant

* I/O-lastig
* CPU-lastig
* Nebenläufigkeit
* Parallelität
* Multiprocessing

* die Umgebungsvariable
* die Umgebungsvariable
* der `PATH`
* die `PATH`-Umgebungsvariable

* die Authentifizierung

 * Generally speaking, this class reduces object allocation and memory consumption at the price of
 * moderately increased constant factors of CPU. Only use this class when there is a specific reason
 * to prioritize memory over CPU.
 *
 * @author Dimitris Andreou
 * @author Jon Noack
 */
@GwtIncompatible // not worth using in GWT for now