1 /* 2 * Copyright (C) 2013 The Android Open Source Project 3 * 4 * Licensed under the Apache License, Version 2.0 (the "License"); 5 * you may not use this file except in compliance with the License. 6 * You may obtain a copy of the License at 7 * 8 * http://www.apache.org/licenses/LICENSE-2.0 9 * 10 * Unless required by applicable law or agreed to in writing, software 11 * distributed under the License is distributed on an "AS IS" BASIS, 12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 * See the License for the specific language governing permissions and 14 * limitations under the License. 15 */ 16 17 #ifndef ART_RUNTIME_BASE_HISTOGRAM_INL_H_ 18 #define ART_RUNTIME_BASE_HISTOGRAM_INL_H_ 19 20 #include <algorithm> 21 #include <cmath> 22 #include <limits> 23 #include <ostream> 24 25 #include "histogram.h" 26 27 #include "base/bit_utils.h" 28 #include "base/time_utils.h" 29 #include "utils.h" 30 31 namespace art { 32 33 template <class Value> inline void Histogram<Value>::AddValue(Value value) { 34 CHECK_GE(value, static_cast<Value>(0)); 35 if (value >= max_) { 36 Value new_max = ((value + 1) / bucket_width_ + 1) * bucket_width_; 37 DCHECK_GT(new_max, max_); 38 GrowBuckets(new_max); 39 } 40 BucketiseValue(value); 41 } 42 43 template <class Value> inline void Histogram<Value>::AdjustAndAddValue(Value value) { 44 AddValue(value / kAdjust); 45 } 46 47 template <class Value> inline Histogram<Value>::Histogram(const char* name) 48 : kAdjust(0), 49 kInitialBucketCount(0), 50 name_(name), 51 max_buckets_(0), 52 sample_size_(0) { 53 } 54 55 template <class Value> 56 inline Histogram<Value>::Histogram(const char* name, Value initial_bucket_width, 57 size_t max_buckets) 58 : kAdjust(1000), 59 kInitialBucketCount(8), 60 name_(name), 61 max_buckets_(max_buckets), 62 bucket_width_(initial_bucket_width) { 63 Reset(); 64 } 65 66 template <class Value> 67 inline void Histogram<Value>::GrowBuckets(Value new_max) { 68 while (max_ < new_max) { 69 // If we have reached the maximum number of buckets, merge buckets together. 70 if (frequency_.size() >= max_buckets_) { 71 CHECK_ALIGNED(frequency_.size(), 2); 72 // We double the width of each bucket to reduce the number of buckets by a factor of 2. 73 bucket_width_ *= 2; 74 const size_t limit = frequency_.size() / 2; 75 // Merge the frequencies by adding each adjacent two together. 76 for (size_t i = 0; i < limit; ++i) { 77 frequency_[i] = frequency_[i * 2] + frequency_[i * 2 + 1]; 78 } 79 // Remove frequencies in the second half of the array which were added to the first half. 80 while (frequency_.size() > limit) { 81 frequency_.pop_back(); 82 } 83 } 84 max_ += bucket_width_; 85 frequency_.push_back(0); 86 } 87 } 88 89 template <class Value> inline size_t Histogram<Value>::FindBucket(Value val) const { 90 // Since this is only a linear histogram, bucket index can be found simply with 91 // dividing the value by the bucket width. 92 DCHECK_GE(val, min_); 93 DCHECK_LE(val, max_); 94 const size_t bucket_idx = static_cast<size_t>((val - min_) / bucket_width_); 95 DCHECK_GE(bucket_idx, 0ul); 96 DCHECK_LE(bucket_idx, GetBucketCount()); 97 return bucket_idx; 98 } 99 100 template <class Value> 101 inline void Histogram<Value>::BucketiseValue(Value val) { 102 CHECK_LT(val, max_); 103 sum_ += val; 104 sum_of_squares_ += val * val; 105 ++sample_size_; 106 ++frequency_[FindBucket(val)]; 107 max_value_added_ = std::max(val, max_value_added_); 108 min_value_added_ = std::min(val, min_value_added_); 109 } 110 111 template <class Value> inline void Histogram<Value>::Initialize() { 112 for (size_t idx = 0; idx < kInitialBucketCount; idx++) { 113 frequency_.push_back(0); 114 } 115 // Cumulative frequency and ranges has a length of 1 over frequency. 116 max_ = bucket_width_ * GetBucketCount(); 117 } 118 119 template <class Value> inline size_t Histogram<Value>::GetBucketCount() const { 120 return frequency_.size(); 121 } 122 123 template <class Value> inline void Histogram<Value>::Reset() { 124 sum_of_squares_ = 0; 125 sample_size_ = 0; 126 min_ = 0; 127 sum_ = 0; 128 min_value_added_ = std::numeric_limits<Value>::max(); 129 max_value_added_ = std::numeric_limits<Value>::min(); 130 frequency_.clear(); 131 Initialize(); 132 } 133 134 template <class Value> inline Value Histogram<Value>::GetRange(size_t bucket_idx) const { 135 DCHECK_LE(bucket_idx, GetBucketCount()); 136 return min_ + bucket_idx * bucket_width_; 137 } 138 139 template <class Value> inline double Histogram<Value>::Mean() const { 140 DCHECK_GT(sample_size_, 0ull); 141 return static_cast<double>(sum_) / static_cast<double>(sample_size_); 142 } 143 144 template <class Value> inline double Histogram<Value>::Variance() const { 145 DCHECK_GT(sample_size_, 0ull); 146 // Using algorithms for calculating variance over a population: 147 // http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance 148 Value sum_squared = sum_ * sum_; 149 double sum_squared_by_n_squared = 150 static_cast<double>(sum_squared) / 151 static_cast<double>(sample_size_ * sample_size_); 152 double sum_of_squares_by_n = 153 static_cast<double>(sum_of_squares_) / static_cast<double>(sample_size_); 154 return sum_of_squares_by_n - sum_squared_by_n_squared; 155 } 156 157 template <class Value> 158 inline void Histogram<Value>::PrintBins(std::ostream& os, const CumulativeData& data) const { 159 DCHECK_GT(sample_size_, 0ull); 160 for (size_t bin_idx = 0; bin_idx < data.freq_.size(); ++bin_idx) { 161 if (bin_idx > 0 && data.perc_[bin_idx] == data.perc_[bin_idx - 1]) { 162 bin_idx++; 163 continue; 164 } 165 os << GetRange(bin_idx) << ": " << data.freq_[bin_idx] << "\t" 166 << data.perc_[bin_idx] * 100.0 << "%\n"; 167 } 168 } 169 170 template <class Value> 171 inline void Histogram<Value>::DumpBins(std::ostream& os) const { 172 DCHECK_GT(sample_size_, 0ull); 173 bool dumped_one = false; 174 for (size_t bin_idx = 0; bin_idx < frequency_.size(); ++bin_idx) { 175 if (frequency_[bin_idx] != 0U) { 176 if (dumped_one) { 177 // Prepend a comma if not the first bin. 178 os << ","; 179 } else { 180 dumped_one = true; 181 } 182 os << GetRange(bin_idx) << ":" << frequency_[bin_idx]; 183 } 184 } 185 } 186 187 template <class Value> 188 inline void Histogram<Value>::PrintConfidenceIntervals(std::ostream &os, double interval, 189 const CumulativeData& data) const { 190 static constexpr size_t kFractionalDigits = 3; 191 DCHECK_GT(interval, 0); 192 DCHECK_LT(interval, 1.0); 193 const double per_0 = (1.0 - interval) / 2.0; 194 const double per_1 = per_0 + interval; 195 const TimeUnit unit = GetAppropriateTimeUnit(Mean() * kAdjust); 196 os << Name() << ":\tSum: " << PrettyDuration(Sum() * kAdjust) << " " 197 << (interval * 100) << "% C.I. " << FormatDuration(Percentile(per_0, data) * kAdjust, unit, 198 kFractionalDigits) 199 << "-" << FormatDuration(Percentile(per_1, data) * kAdjust, unit, kFractionalDigits) << " " 200 << "Avg: " << FormatDuration(Mean() * kAdjust, unit, kFractionalDigits) << " Max: " 201 << FormatDuration(Max() * kAdjust, unit, kFractionalDigits) << "\n"; 202 } 203 204 template <class Value> 205 inline void Histogram<Value>::PrintMemoryUse(std::ostream &os) const { 206 os << Name(); 207 if (sample_size_ != 0u) { 208 os << ": Avg: " << PrettySize(Mean()) << " Max: " 209 << PrettySize(Max()) << " Min: " << PrettySize(Min()) << "\n"; 210 } else { 211 os << ": <no data>\n"; 212 } 213 } 214 215 template <class Value> 216 inline void Histogram<Value>::CreateHistogram(CumulativeData* out_data) const { 217 DCHECK_GT(sample_size_, 0ull); 218 out_data->freq_.clear(); 219 out_data->perc_.clear(); 220 uint64_t accumulated = 0; 221 out_data->freq_.push_back(accumulated); 222 out_data->perc_.push_back(0.0); 223 for (size_t idx = 0; idx < frequency_.size(); idx++) { 224 accumulated += frequency_[idx]; 225 out_data->freq_.push_back(accumulated); 226 out_data->perc_.push_back(static_cast<double>(accumulated) / static_cast<double>(sample_size_)); 227 } 228 DCHECK_EQ(out_data->freq_.back(), sample_size_); 229 DCHECK_LE(std::abs(out_data->perc_.back() - 1.0), 0.001); 230 } 231 232 #pragma clang diagnostic push 233 #pragma clang diagnostic ignored "-Wfloat-equal" 234 235 template <class Value> 236 inline double Histogram<Value>::Percentile(double per, const CumulativeData& data) const { 237 DCHECK_GT(data.perc_.size(), 0ull); 238 size_t upper_idx = 0, lower_idx = 0; 239 for (size_t idx = 0; idx < data.perc_.size(); idx++) { 240 if (per <= data.perc_[idx]) { 241 upper_idx = idx; 242 break; 243 } 244 245 if (per >= data.perc_[idx] && idx != 0 && data.perc_[idx] != data.perc_[idx - 1]) { 246 lower_idx = idx; 247 } 248 } 249 250 const double lower_perc = data.perc_[lower_idx]; 251 const double lower_value = static_cast<double>(GetRange(lower_idx)); 252 if (per == lower_perc) { 253 return lower_value; 254 } 255 256 const double upper_perc = data.perc_[upper_idx]; 257 const double upper_value = static_cast<double>(GetRange(upper_idx)); 258 if (per == upper_perc) { 259 return upper_value; 260 } 261 DCHECK_GT(upper_perc, lower_perc); 262 263 double value = lower_value + (upper_value - lower_value) * 264 (per - lower_perc) / (upper_perc - lower_perc); 265 266 if (value < min_value_added_) { 267 value = min_value_added_; 268 } else if (value > max_value_added_) { 269 value = max_value_added_; 270 } 271 272 return value; 273 } 274 275 #pragma clang diagnostic pop 276 277 } // namespace art 278 #endif // ART_RUNTIME_BASE_HISTOGRAM_INL_H_ 279
