1 /*
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   3  * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
   4  *
   5  * This code is free software; you can redistribute it and/or modify it
   6  * under the terms of the GNU General Public License version 2 only, as
   7  * published by the Free Software Foundation.
   8  *
   9  * This code is distributed in the hope that it will be useful, but WITHOUT
  10  * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
  11  * FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License
  12  * version 2 for more details (a copy is included in the LICENSE file that
  13  * accompanied this code).
  14  *
  15  * You should have received a copy of the GNU General Public License version
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  17  * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
  18  *
  19  * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
  20  * or visit www.oracle.com if you need additional information or have any
  21  * questions.
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  23  */
  24 
  25 // Catch-all file for utility classes
  26 
  27 // A weighted average maintains a running, weighted average
  28 // of some float value (templates would be handy here if we
  29 // need different types).
  30 //
  31 // The average is adaptive in that we smooth it for the
  32 // initial samples; we don't use the weight until we have
  33 // enough samples for it to be meaningful.
  34 //
  35 // This serves as our best estimate of a future unknown.
  36 //
  37 class AdaptiveWeightedAverage : public CHeapObj {
  38  private:
  39   float            _average;        // The last computed average
  40   unsigned         _sample_count;   // How often we've sampled this average
  41   unsigned         _weight;         // The weight used to smooth the averages
  42                                     //   A higher weight favors the most
  43                                     //   recent data.
  44 
  45  protected:
  46   float            _last_sample;    // The last value sampled.
  47 
  48   void  increment_count()       { _sample_count++;       }
  49   void  set_average(float avg)  { _average = avg;        }
  50 
  51   // Helper function, computes an adaptive weighted average
  52   // given a sample and the last average
  53   float compute_adaptive_average(float new_sample, float average);
  54 
  55  public:
  56   // Input weight must be between 0 and 100
  57   AdaptiveWeightedAverage(unsigned weight, float avg = 0.0) :
  58     _average(avg), _sample_count(0), _weight(weight), _last_sample(0.0) {
  59   }
  60 
  61   void clear() {
  62     _average = 0;
  63     _sample_count = 0;
  64     _last_sample = 0;
  65   }
  66 
  67   // Useful for modifying static structures after startup.
  68   void  modify(size_t avg, unsigned wt, bool force = false)  {
  69     assert(force, "Are you sure you want to call this?");
  70     _average = (float)avg;
  71     _weight  = wt;
  72   }
  73 
  74   // Accessors
  75   float    average() const       { return _average;       }
  76   unsigned weight()  const       { return _weight;        }
  77   unsigned count()   const       { return _sample_count;  }
  78   float    last_sample() const   { return _last_sample; }
  79 
  80   // Update data with a new sample.
  81   void sample(float new_sample);
  82 
  83   static inline float exp_avg(float avg, float sample,
  84                                unsigned int weight) {
  85     assert(0 <= weight && weight <= 100, "weight must be a percent");
  86     return (100.0F - weight) * avg / 100.0F + weight * sample / 100.0F;
  87   }
  88   static inline size_t exp_avg(size_t avg, size_t sample,
  89                                unsigned int weight) {
  90     // Convert to float and back to avoid integer overflow.
  91     return (size_t)exp_avg((float)avg, (float)sample, weight);
  92   }
  93 
  94   // Printing
  95   void print_on(outputStream* st) const;
  96   void print() const;
  97 };
  98 
  99 
 100 // A weighted average that includes a deviation from the average,
 101 // some multiple of which is added to the average.
 102 //
 103 // This serves as our best estimate of an upper bound on a future
 104 // unknown.
 105 class AdaptivePaddedAverage : public AdaptiveWeightedAverage {
 106  private:
 107   float          _padded_avg;     // The last computed padded average
 108   float          _deviation;      // Running deviation from the average
 109   unsigned       _padding;        // A multiple which, added to the average,
 110                                   // gives us an upper bound guess.
 111 
 112  protected:
 113   void set_padded_average(float avg)  { _padded_avg = avg;  }
 114   void set_deviation(float dev)       { _deviation  = dev;  }
 115 
 116  public:
 117   AdaptivePaddedAverage() :
 118     AdaptiveWeightedAverage(0),
 119     _padded_avg(0.0), _deviation(0.0), _padding(0) {}
 120 
 121   AdaptivePaddedAverage(unsigned weight, unsigned padding) :
 122     AdaptiveWeightedAverage(weight),
 123     _padded_avg(0.0), _deviation(0.0), _padding(padding) {}
 124 
 125   // Placement support
 126   void* operator new(size_t ignored, void* p) { return p; }
 127   // Allocator
 128   void* operator new(size_t size) { return CHeapObj::operator new(size); }
 129 
 130   // Accessor
 131   float padded_average() const         { return _padded_avg; }
 132   float deviation()      const         { return _deviation;  }
 133   unsigned padding()     const         { return _padding;    }
 134 
 135   void clear() {
 136     AdaptiveWeightedAverage::clear();
 137     _padded_avg = 0;
 138     _deviation = 0;
 139   }
 140 
 141   // Override
 142   void  sample(float new_sample);
 143 
 144   // Printing
 145   void print_on(outputStream* st) const;
 146   void print() const;
 147 };
 148 
 149 // A weighted average that includes a deviation from the average,
 150 // some multiple of which is added to the average.
 151 //
 152 // This serves as our best estimate of an upper bound on a future
 153 // unknown.
 154 // A special sort of padded average:  it doesn't update deviations
 155 // if the sample is zero. The average is allowed to change. We're
 156 // preventing the zero samples from drastically changing our padded
 157 // average.
 158 class AdaptivePaddedNoZeroDevAverage : public AdaptivePaddedAverage {
 159 public:
 160   AdaptivePaddedNoZeroDevAverage(unsigned weight, unsigned padding) :
 161     AdaptivePaddedAverage(weight, padding)  {}
 162   // Override
 163   void  sample(float new_sample);
 164 
 165   // Printing
 166   void print_on(outputStream* st) const;
 167   void print() const;
 168 };
 169 
 170 // Use a least squares fit to a set of data to generate a linear
 171 // equation.
 172 //              y = intercept + slope * x
 173 
 174 class LinearLeastSquareFit : public CHeapObj {
 175   double _sum_x;        // sum of all independent data points x
 176   double _sum_x_squared; // sum of all independent data points x**2
 177   double _sum_y;        // sum of all dependent data points y
 178   double _sum_xy;       // sum of all x * y.
 179   double _intercept;     // constant term
 180   double _slope;        // slope
 181   // The weighted averages are not currently used but perhaps should
 182   // be used to get decaying averages.
 183   AdaptiveWeightedAverage _mean_x; // weighted mean of independent variable
 184   AdaptiveWeightedAverage _mean_y; // weighted mean of dependent variable
 185 
 186  public:
 187   LinearLeastSquareFit(unsigned weight);
 188   void update(double x, double y);
 189   double y(double x);
 190   double slope() { return _slope; }
 191   // Methods to decide if a change in the dependent variable will
 192   // achive a desired goal.  Note that these methods are not
 193   // complementary and both are needed.
 194   bool decrement_will_decrease();
 195   bool increment_will_decrease();
 196 };
 197 
 198 class GCPauseTimer : StackObj {
 199   elapsedTimer* _timer;
 200  public:
 201   GCPauseTimer(elapsedTimer* timer) {
 202     _timer = timer;
 203     _timer->stop();
 204   }
 205   ~GCPauseTimer() {
 206     _timer->start();
 207   }
 208 };