String unused = interner.intern(Double.toHexString(Math.random()));
    }
    return reps;
  }

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

  @CanIgnoreReturnValue

for idx in {1..10}; do
	# generate random nr of blocks
	COUNT=$((RANDOM % 100 + 100))
	# generate random content
	dd if=/dev/urandom bs=50K count="${COUNT}" of="${SRC_DIR}"/file"$idx"
done

# create small object that will be inlined into xl.meta
dd if=/dev/urandom bs=50K count=1 of="${INLINED_DIR}"/inlined

  // tweak this to control hit rate
  @Param("2.5")
  double concentration;

  Random random = new Random();

  LoadingCache<Integer, Integer> cache;

  int max;

  static AtomicLong requests = new AtomicLong(0);
  static AtomicLong misses = new AtomicLong(0);

  @BeforeExperiment
  void setUp() {
    // random integers will be generated in this range, then raised to the

import java.math.BigInteger;
import java.util.Random;
import org.jspecify.annotations.NullUnmarked;

/**
 * Utilities for benchmarks.
 *
 * <p>In many cases, we wish to vary the order of magnitude of the input as much as we want to vary
 * the input itself, so most methods which generate values use an exponential distribution varying
 * the order of magnitude of the generated values uniformly at random.
 *
 * @author Louis Wasserman
 */

import com.google.caliper.Benchmark;
import java.util.Random;
import org.jspecify.annotations.NullUnmarked;

/**
 * Benchmarks for certain methods of {@code UnsignedLongs}.
 *
 * @author Eamonn McManus
 */
@NullUnmarked
public class UnsignedLongsBenchmark {
  private static final int ARRAY_SIZE = 0x10000;
  private static final int ARRAY_MASK = 0x0ffff;
  private static final Random randomSource = new Random(314159265358979L);

  @Param({"20", "2000"})
  int size;

  @Param({"2", "20"})
  int nonAlphaRatio; // one non-alpha char per this many chars

  @Param boolean noWorkToDo;

  Random random;
  String testString;

  @BeforeExperiment
  void setUp() {
    random = new Random(0xdeadbeef); // fix the seed so results are comparable across runs

    int nonAlpha = size / nonAlphaRatio;
    int alpha = size - nonAlpha;

  @BeforeExperiment
  void setUp() {
    Random random = new Random(randomSeed);
    xInts = new int[SAMPLE_SIZE];
    yInts = new int[SAMPLE_SIZE];
    xLongs = new long[SAMPLE_SIZE];
    yLongs = new long[SAMPLE_SIZE];
    constant = new int[SAMPLE_SIZE];
    for (int i = 0; i < SAMPLE_SIZE; i++) {
      xInts[i] = random.nextInt(Integer.MAX_VALUE);
      yInts[i] = random.nextInt(Integer.MAX_VALUE);

// initialization vector (IV) and the bucket/object path.
//
//  1. Encrypt:
//     Input: ClientKey, bucket, object, metadata, object_data
//     -              IV := Random({0,1}²⁵⁶)
//     -       ObjectKey := SHA256(ClientKey || Random({0,1}²⁵⁶))
//     -       KeyEncKey := HMAC-SHA256(ClientKey, IV || 'SSE-C' || 'DAREv2-HMAC-SHA256' || bucket || '/' || object)
//     -       SealedKey := DAREv2_Enc(KeyEncKey, ObjectKey)

  private Queue<Integer> queue;

  private final Random random = new Random();

  @BeforeExperiment
  void setUp() {
    queue = heap.create(comparator.get());
    for (int i = 0; i < size; i++) {
      queue.add(random.nextInt());
    }
  }

  @Benchmark
  void pollAndAdd(int reps) {
    for (int i = 0; i < reps; i++) {
      // TODO(kevinb): precompute random #s?
      queue.add(queue.poll() ^ random.nextInt());

  // tweak this to control hit rate
  @Param("2.5")
  double concentration;

  Random random = new Random();

  LoadingCache<Integer, Integer> cache;

  int max;

  static AtomicLong requests = new AtomicLong(0);
  static AtomicLong misses = new AtomicLong(0);

  @BeforeExperiment
  void setUp() {
    // random integers will be generated in this range, then raised to the