with:
          distribution: 'temurin'
          java-version: |
            ${{ matrix.java-version }}
            21

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

      - name: Run Tests
        run: ./gradlew test allTests -Pokhttp.platform=${{ matrix.provider }} -Ptest.java.version=${{ matrix.java-version }}

  openjdklatest:
    runs-on: ubuntu-latest

    steps:

        final String jwtClaim = "{\"matrix\":[[1,2],[3,4]]}";
        final Map<String, Object> attributes = new HashMap<>();

        authenticator.parseJwtClaim(jwtClaim, attributes);

        assertTrue(attributes.get("matrix") instanceof List);
        @SuppressWarnings("unchecked")
        final List<Object> matrix = (List<Object>) attributes.get("matrix");
        assertEquals(2, matrix.size());

    needs: initial-build
    runs-on: ${{ matrix.os }}
    strategy:
      fail-fast: false
      matrix:
        os: [ubuntu-latest, macos-latest, windows-latest]
        java: ['17', '21', '25']
    steps:
      - name: Set up JDK ${{ matrix.java }}
        uses: actions/setup-java@be666c2fcd27ec809703dec50e508c2fdc7f6654 # v5.2.0
        with:
          java-version: ${{ matrix.java }}
          distribution: 'temurin'

  /* Want to test simple MatMul example with abstract tensors:
    [[1,2],   *  [[5,6],   =   [[19,22],
     [3,4]]       [7,8]]        [43,50]]
  */

  // Build 1st Matrix.
  int64_t dims[] = {2, 2};  // Matrices will be 2 x 2
  int num_dims = sizeof(dims) / sizeof(dims[0]);

  float vals1[] = {1.0f, 2.0f, 3.0f, 4.0f};

xml:id","xlink:title","xml:space","xmlns:xlink"]),B=a(/\{\{[\w\W]*|[\w\W]*\}\}/gm),G=a(/<%[\w\W]*|[\w\W]*%>/gm),W=a(/\$\{[\w\W]*/gm),Y=a(/^data-[\-\w.\u00B7-\uFFFF]+$/),j=a(/^aria-[\-\w]+$/),X=a(/^(?:(?:(?:f|ht)tps?|mailto|tel|callto|sms|cid|xmpp|matrix):|[^a-z]|[a-z+.\-]+(?:[^a-z+.\-:]|$))/i),q=a(/^(?:\w+script|data):/i),$=a(/[\u0000-\u0020\u00A0\u1680\u180E\u2000-\u2029\u205F\u3000]/g),K=a(/^html$/i),V=a(/^[a-z][.\w]*(-[.\w]+)+$/i);var Z=Object.freeze({__proto__:null,ARIA_ATTR:j,ATTR_WHITESPAC...

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.

---

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.
* **머신러닝**: 보통 많은 "matrix"와 "vector" 곱셈이 필요합니다. 숫자가 있는 거대한 스프레드시트를 생각하고, 그 모든 수를 동시에 곱한다고 생각해보세요.
* **딥러닝**: 머신러닝의 하위 분야이므로 동일하게 적용됩니다. 다만 곱해야 할 숫자가 있는 스프레드시트가 하나가 아니라, 아주 큰 집합이며, 많은 경우 그 모델을 만들고/또는 사용하기 위해 특별한 프로세서를 사용합니다.

property `tests.bwc.git_fetch_latest` to `false` and the BWC builds will skip
fetching the latest from the remote.

== Testing in FIPS 140-2 mode

We have a CI matrix job that periodically runs all our tests with the JVM configured
to be FIPS 140-2 compliant with the use of the BouncyCastle FIPS approved Security Provider.

* **Maschinelles Lernen**: Normalerweise sind viele „Matrix“- und „Vektor“-Multiplikationen erforderlich. Stellen Sie sich eine riesige Tabelle mit Zahlen vor, in der Sie alle Zahlen gleichzeitig multiplizieren.

	prometheus.MustRegister(minioVersionInfo)
}

// newMinioCollector describes the collector
// and returns reference of minioCollector
// It creates the Prometheus Description which is used
// to define metric and  help string
func newMinioCollector() *minioCollector {
	return &minioCollector{
		desc: prometheus.NewDesc("minio_stats", "Statistics exposed by MinIO server", nil, nil),
	}
}