* 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

        assertTrue(mockFilterChain.wasDoFilterCalled());
    }

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

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

 * 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

このシナリオでは、各清掃員 (あなたを含む) がプロセッサであり、それぞれが自分の役割を果たします。

そして実行時間の大半は (待ちではなく) 実作業が占め、コンピュータでの作業は <abbr title="Central Processing Unit - 中央処理装置">CPU</abbr> によって行われます。これらの問題は「CPU バウンド」と呼ばれます。

---

CPU バウンドな操作の一般的な例は、複雑な数値処理が必要なものです。

例えば:

* **オーディオ** や **画像処理**。
* **コンピュータビジョン**: 画像は数百万のピクセルで構成され、各ピクセルには 3 つの値/色があり、通常、それらのピクセル上で同時に何かを計算する必要があります。

These "env" files match up with an environment matrix that roughly covers:

-   Different Python versions
-   Linux, MacOS, and Windows machines (these pool definitions are internal)
-   x86 and arm64
-   CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs

## How to Test Your Changes to TensorFlow

You may check how your changes will affect TensorFlow by:

1. Creating a PR and observing the presubmit test results

TF_CAPI_EXPORT void TF_SetXlaMinClusterSize(int size);

// Gets/Sets TF/XLA flag for whether(true) or not(false) to disable constant
// folding. This is for testing to ensure that XLA is being tested rather than
// Tensorflow's CPU implementation through constant folding.
TF_CAPI_EXPORT unsigned char TF_GetXlaConstantFoldingDisabled();
TF_CAPI_EXPORT void TF_SetXlaConstantFoldingDisabled(
    unsigned char should_enable);

   * using a secondary hash (Marsaglia XorShift) to try to find a
   * free slot.
   *
   * The table size is capped because, when there are more threads
   * than CPUs, supposing that each thread were bound to a CPU,
   * there would exist a perfect hash function mapping threads to
   * slots that eliminates collisions. When we reach capacity, we
   * search for this mapping by randomly varying the hash codes of

  (*sess_options.config.mutable_device_count())["CPU"] = num_cpus;
  status->status =
      tensorflow::DeviceFactory::AddCpuDevices(sess_options, prefix, &devices);

  // Remove the device that has the host device name since host device is alreay
  // in an initialized context.
  for (auto d = devices.begin(); d != devices.end();) {
    if (absl::StrContains(d->get()->name(), "CPU:0")) {
      d = devices.erase(d);
    } else {

    // we try hard to make them robust in practice.  We could additionally try to add in a system
    // load timeout multiplier.  Or we could try to use a CPU time bound instead of wall clock time
    // bound.  But these ideas are harder to implement.  We do not try to detect or handle a
    // user-specified -XX:+DisableExplicitGC.
    //
    // TODO(user): Consider using