<property name="modules.dir" value="${basedir}/modules" />
	<property name="target.dir" value="${basedir}/target/modules" />

	<!-- Maven Repository -->
	<property name="maven.snapshot.repo.url" value="https://maven.codelibs.org/" />
	<property name="maven.release.repo.url" value="https://maven.codelibs.org/" />
	<property name="opensearch.version" value="2.17.1" />

	<target name="install.modules">
		<mkdir dir="${target.dir}" />

  }

  @Benchmark
  public long timeComplete_Normal(int reps) throws Exception {
    long r = 0;
    List<Facade<Integer>> list = new ArrayList<>(reps);
    for (int i = 0; i < reps; i++) {
      final Facade<Integer> localFuture = impl.newFacade();
      list.add(localFuture);
      localFuture.set(i);
    }
    for (int i = 0; i < reps; i++) {
      r += list.get(i).get();
    }
    return r;
  }

  @Benchmark
  int branchFreeLtIntInlined(int reps) {
    int tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & SAMPLE_MASK;
      int x = xInts[j];
      int y = yInts[j];
      int z = constant[j];
      tmp += z + ((x - y) >>> (Integer.SIZE - 1));
    }
    return tmp;
  }

  @Benchmark
  int branchFreeLtInt(int reps) {
    int tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & SAMPLE_MASK;

  @Benchmark
  int weakInterner(int reps) {
    Interner<String> interner = Interners.newWeakInterner();
    for (int i = 0; i < reps; i++) {
      String unused = interner.intern(Double.toHexString(Math.random()));
    }
    return reps;
  }

  @Benchmark
  int strongInterner(int reps) {
    Interner<String> interner = Interners.newStrongInterner();
    for (int i = 0; i < reps; i++) {

    return datasets[i & 0xFF].clone();
  }

  @Benchmark
  double median(int reps) {
    double dummy = 0.0;
    for (int i = 0; i < reps; i++) {
      dummy += algorithm.singleQuantile(1, 2, dataset(i));
    }
    return dummy;
  }

  @Benchmark
  double percentile90(int reps) {
    double dummy = 0.0;
    for (int i = 0; i < reps; i++) {
      dummy += algorithm.singleQuantile(90, 100, dataset(i));
    }

    return datasets[i & 0xFF].clone();
  }

  @Benchmark
  double median(int reps) {
    double dummy = 0.0;
    for (int i = 0; i < reps; i++) {
      dummy += algorithm.singleQuantile(1, 2, dataset(i));
    }
    return dummy;
  }

  @Benchmark
  double percentile90(int reps) {
    double dummy = 0.0;
    for (int i = 0; i < reps; i++) {
      dummy += algorithm.singleQuantile(90, 100, dataset(i));
    }

    }
  }

  @Benchmark
  long divide(int reps) {
    long tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & ARRAY_MASK;
      tmp += UnsignedLongs.divide(longs[j], divisors[j]);
    }
    return tmp;
  }

  @Benchmark
  long remainder(int reps) {
    long tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & ARRAY_MASK;

  @Benchmark
  int joinerWithStringDelimiter(int reps) {
    int dummy = 0;
    for (int i = 0; i < reps; i++) {
      dummy ^= JOINER_ON_STRING.join(components).length();
    }
    return dummy;
  }

  /** {@link Joiner} with a character delimiter. */
  @Benchmark
  int joinerWithCharacterDelimiter(int reps) {
    int dummy = 0;
    for (int i = 0; i < reps; i++) {

      ints[i] = RANDOM_SOURCE.nextInt();
    }
  }

  @Benchmark
  int pow(int reps) {
    int tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & ARRAY_MASK;
      tmp += IntMath.pow(positive[j], exponent[j]);
    }
    return tmp;
  }

  @Benchmark
  int mod(int reps) {
    int tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & ARRAY_MASK;
      tmp += IntMath.mod(ints[j], positive[j]);

    }
  }

  @Benchmark
  int pow(int reps) {
    int tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & ARRAY_MASK;
      tmp += LongMath.pow(positive[j], exponents[j]);
    }
    return tmp;
  }

  @Benchmark
  int mod(int reps) {
    int tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & ARRAY_MASK;
      tmp += LongMath.mod(longs[j], positive[j]);