1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
|
/*
* 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.collect;
import com.google.caliper.Param;
import com.google.caliper.Runner;
import com.google.caliper.SimpleBenchmark;
import com.google.common.base.Function;
import com.google.common.collect.MapMaker;
import com.google.common.primitives.Ints;
import java.util.Map;
import java.util.Random;
import java.util.concurrent.atomic.AtomicLong;
/**
* Simple single-threaded benchmark for a computing map with maximum size.
*
* @author Charles Fry
*/
public class MapMakerSingleThreadBenchmark 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();
Map<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 = new MapMaker()
.concurrencyLevel(segments)
.maximumSize(maximumSize)
.makeComputingMap(
new Function<Integer, Integer>() {
@Override public Integer apply(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.get(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.get(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(MapMakerSingleThreadBenchmark.class, args);
}
// for proper distributions later:
// import JSci.maths.statistics.ProbabilityDistribution;
// int key = (int) dist.inverse(random.nextDouble());
}
|
