1 files changed, 0 insertions, 111 deletions
diff --git a/guava-tests/benchmark/com/google/common/cache/LoadingCacheSingleThreadBenchmark.java b/guava-tests/benchmark/com/google/common/cache/LoadingCacheSingleThreadBenchmark.java
deleted file mode 100644
index 85e6bad..0000000
--- a/guava-tests/benchmark/com/google/common/cache/LoadingCacheSingleThreadBenchmark.java
+++ /dev/null
@@ -1,111 +0,0 @@
-/*
- * Copyright (C) 2010 The Guava Authors
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package com.google.common.cache;
-
-import com.google.caliper.Param;
-import com.google.caliper.Runner;
-import com.google.caliper.SimpleBenchmark;
-import com.google.common.primitives.Ints;
-
-import java.util.Random;
-import java.util.concurrent.atomic.AtomicLong;
-
-/**
- * Single-threaded benchmark for {@link LoadingCache}.
- *
- * @author Charles Fry
- */
-public class LoadingCacheSingleThreadBenchmark extends SimpleBenchmark {
-  @Param({"1000", "2000"}) int maximumSize;
-  @Param("5000") int distinctKeys;
-  @Param("4") int segments;
-
-  // 1 means uniform likelihood of keys; higher means some keys are more popular
-  // 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);
-
-  @Override protected void setUp() {
-    // random integers will be generated in this range, then raised to the
-    // power of (1/concentration) and floor()ed
-    max = Ints.checkedCast((long) Math.pow(distinctKeys, concentration));
-
-    cache = CacheBuilder.newBuilder()
-        .concurrencyLevel(segments)
-        .maximumSize(maximumSize)
-        .build(
-            new CacheLoader<Integer, Integer>() {
-              @Override public Integer load(Integer from) {
-                return (int) misses.incrementAndGet();
-              }
-            });
-
-    // To start, fill up the cache.
-    // Each miss both increments the counter and causes the map to grow by one,
-    // so until evictions begin, the size of the map is the greatest return
-    // value seen so far
-    while (cache.getUnchecked(nextRandomKey()) < maximumSize) {}
-
-    requests.set(0);
-    misses.set(0);
-  }
-
-  public int time(int reps) {
-    int dummy = 0;
-    for (int i = 0; i < reps; i++) {
-      dummy += cache.getUnchecked(nextRandomKey());
-    }
-    requests.addAndGet(reps);
-    return dummy;
-  }
-
-  private int nextRandomKey() {
-    int a = random.nextInt(max);
-
-    /*
-     * For example, if concentration=2.0, the following takes the square root of
-     * the uniformly-distributed random integer, then truncates any fractional
-     * part, so higher integers would appear (in this case linearly) more often
-     * than lower ones.
-     */
-    return (int) Math.pow(a, 1.0 / concentration);
-  }
-
-  @Override protected void tearDown() {
-    double req = requests.get();
-    double hit = req - misses.get();
-
-    // Currently, this is going into /dev/null, but I'll fix that
-    System.out.println("hit rate: " + hit / req);
-  }
-
-  public static void main(String[] args) {
-    Runner.main(LoadingCacheSingleThreadBenchmark.class, args);
-  }
-
-  // for proper distributions later:
-  // import JSci.maths.statistics.ProbabilityDistribution;
-  // int key = (int) dist.inverse(random.nextDouble());
-}