Mercurial > jdk8-mips64-public > hotspot / file revision

 /*

  * Copyright 2002-2005 Sun Microsystems, Inc.  All Rights Reserved.

  * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.

  *

  * This code is free software; you can redistribute it and/or modify it

  * under the terms of the GNU General Public License version 2 only, as

  * published by the Free Software Foundation.

  *

  * This code is distributed in the hope that it will be useful, but WITHOUT

  * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or

  * FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License

  * version 2 for more details (a copy is included in the LICENSE file that

  * accompanied this code).

  *

  * You should have received a copy of the GNU General Public License version

  * 2 along with this work; if not, write to the Free Software Foundation,

  * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.

  *

  * Please contact Sun Microsystems, Inc., 4150 Network Circle, Santa Clara,

  * CA 95054 USA or visit www.sun.com if you need additional information or

  * have any questions.

  *

  */

 // Catch-all file for utility classes

 // A weighted average maintains a running, weighted average

 // of some float value (templates would be handy here if we

 // need different types).

 //

 // The average is adaptive in that we smooth it for the

 // initial samples; we don't use the weight until we have

 // enough samples for it to be meaningful.

 //

 // This serves as our best estimate of a future unknown.

 //

 class AdaptiveWeightedAverage : public CHeapObj {

  private:

   float            _average;        // The last computed average

   unsigned         _sample_count;   // How often we've sampled this average

   unsigned         _weight;         // The weight used to smooth the averages

                                     //   A higher weight favors the most

                                     //   recent data.

  protected:

   float            _last_sample;    // The last value sampled.

   void  increment_count()       { _sample_count++;       }

   void  set_average(float avg)  { _average = avg;        }

   // Helper function, computes an adaptive weighted average

   // given a sample and the last average

   float compute_adaptive_average(float new_sample, float average);

  public:

   // Input weight must be between 0 and 100

   AdaptiveWeightedAverage(unsigned weight) :

     _average(0.0), _sample_count(0), _weight(weight), _last_sample(0.0) {

   }

   // Accessors

   float    average() const       { return _average;       }

   unsigned weight()  const       { return _weight;        }

   unsigned count()   const       { return _sample_count;  }

   float    last_sample() const   { return _last_sample; }

   // Update data with a new sample.

   void sample(float new_sample);

   static inline float exp_avg(float avg, float sample,

                                unsigned int weight) {

     assert(0 <= weight && weight <= 100, "weight must be a percent");

     return (100.0F - weight) * avg / 100.0F + weight * sample / 100.0F;

   }

   static inline size_t exp_avg(size_t avg, size_t sample,

                                unsigned int weight) {

     // Convert to float and back to avoid integer overflow.

     return (size_t)exp_avg((float)avg, (float)sample, weight);

   }

 };

 // A weighted average that includes a deviation from the average,

 // some multiple of which is added to the average.

 //

 // This serves as our best estimate of an upper bound on a future

 // unknown.

 class AdaptivePaddedAverage : public AdaptiveWeightedAverage {

  private:

   float          _padded_avg;     // The last computed padded average

   float          _deviation;      // Running deviation from the average

   unsigned       _padding;        // A multiple which, added to the average,

                                   // gives us an upper bound guess.

  protected:

   void set_padded_average(float avg)  { _padded_avg = avg;  }

   void set_deviation(float dev)       { _deviation  = dev;  }

  public:

   AdaptivePaddedAverage() :

     AdaptiveWeightedAverage(0),

     _padded_avg(0.0), _deviation(0.0), _padding(0) {}

   AdaptivePaddedAverage(unsigned weight, unsigned padding) :

     AdaptiveWeightedAverage(weight),

     _padded_avg(0.0), _deviation(0.0), _padding(padding) {}

   // Placement support

   void* operator new(size_t ignored, void* p) { return p; }

   // Allocator

   void* operator new(size_t size) { return CHeapObj::operator new(size); }

   // Accessor

   float padded_average() const         { return _padded_avg; }

   float deviation()      const         { return _deviation;  }

   unsigned padding()     const         { return _padding;    }

   // Override

   void  sample(float new_sample);

 };

 // A weighted average that includes a deviation from the average,

 // some multiple of which is added to the average.

 //

 // This serves as our best estimate of an upper bound on a future

 // unknown.

 // A special sort of padded average:  it doesn't update deviations

 // if the sample is zero. The average is allowed to change. We're

 // preventing the zero samples from drastically changing our padded

 // average.

 class AdaptivePaddedNoZeroDevAverage : public AdaptivePaddedAverage {

 public:

   AdaptivePaddedNoZeroDevAverage(unsigned weight, unsigned padding) :

     AdaptivePaddedAverage(weight, padding)  {}

   // Override

   void  sample(float new_sample);

 };

 // Use a least squares fit to a set of data to generate a linear

 // equation.

 //              y = intercept + slope * x

 class LinearLeastSquareFit : public CHeapObj {

   double _sum_x;        // sum of all independent data points x

   double _sum_x_squared; // sum of all independent data points x**2

   double _sum_y;        // sum of all dependent data points y

   double _sum_xy;       // sum of all x * y.

   double _intercept;     // constant term

   double _slope;        // slope

   // The weighted averages are not currently used but perhaps should

   // be used to get decaying averages.

   AdaptiveWeightedAverage _mean_x; // weighted mean of independent variable

   AdaptiveWeightedAverage _mean_y; // weighted mean of dependent variable

  public:

   LinearLeastSquareFit(unsigned weight);

   void update(double x, double y);

   double y(double x);

   double slope() { return _slope; }

   // Methods to decide if a change in the dependent variable will

   // achive a desired goal.  Note that these methods are not

   // complementary and both are needed.

   bool decrement_will_decrease();

   bool increment_will_decrease();

 };

 class GCPauseTimer : StackObj {

   elapsedTimer* _timer;

  public:

   GCPauseTimer(elapsedTimer* timer) {

     _timer = timer;

     _timer->stop();

   }

   ~GCPauseTimer() {

     _timer->start();

   }

 };