jdk8-mips64-public/hotspot: src/share/vm/gc_implementation/shared/gcUtil.hpp@db9981fd3124 (annotated)

src/share/vm/gc_implementation/shared/gcUtil.hpp@db9981fd3124 (annotated)

src/share/vm/gc_implementation/shared/gcUtil.hpp

Wed, 23 Jan 2013 13:02:39 -0500

author: jprovino
date: Wed, 23 Jan 2013 13:02:39 -0500
changeset 4542: db9981fd3124
parent 4153: b9a9ed0f8eeb
child 5614: 9758d9f36299
permissions: -rw-r--r--

8005915: Unify SERIALGC and INCLUDE_ALTERNATE_GCS
Summary: Rename INCLUDE_ALTERNATE_GCS to INCLUDE_ALL_GCS and replace SERIALGC with INCLUDE_ALL_GCS.
Reviewed-by: coleenp, stefank

 /*
  * Copyright (c) 2002, 2012, Oracle and/or its affiliates. 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 Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
  * or visit www.oracle.com if you need additional information or have any
  * questions.
  *
  */
 #ifndef SHARE_VM_GC_IMPLEMENTATION_SHARED_GCUTIL_HPP
 #define SHARE_VM_GC_IMPLEMENTATION_SHARED_GCUTIL_HPP
 #include "memory/allocation.hpp"
 #include "runtime/timer.hpp"
 #include "utilities/debug.hpp"
 #include "utilities/globalDefinitions.hpp"
 #include "utilities/ostream.hpp"
 // 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<mtGC> {
  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.
   bool             _is_old;         // Has enough historical data
   const static unsigned OLD_THRESHOLD = 100;
  protected:
   float            _last_sample;    // The last value sampled.
   void  increment_count() {
     _sample_count++;
     if (!_is_old && _sample_count > OLD_THRESHOLD) {
       _is_old = true;
     }
   }
   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, float avg = 0.0) :
     _average(avg), _sample_count(0), _weight(weight), _last_sample(0.0),
     _is_old(false) {
   }
   void clear() {
     _average = 0;
     _sample_count = 0;
     _last_sample = 0;
     _is_old = false;
   }
   // Useful for modifying static structures after startup.
   void  modify(size_t avg, unsigned wt, bool force = false)  {
     assert(force, "Are you sure you want to call this?");
     _average = (float)avg;
     _weight  = wt;
   }
   // Accessors
   float    average() const       { return _average;       }
   unsigned weight()  const       { return _weight;        }
   unsigned count()   const       { return _sample_count;  }
   float    last_sample() const   { return _last_sample;   }
   bool     is_old()  const       { return _is_old;        }
   // 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);
   }
   // Printing
   void print_on(outputStream* st) const;
   void print() const;
 };
 // 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<mtGC>::operator new(size); }
   // Accessor
   float padded_average() const         { return _padded_avg; }
   float deviation()      const         { return _deviation;  }
   unsigned padding()     const         { return _padding;    }
   void clear() {
     AdaptiveWeightedAverage::clear();
     _padded_avg = 0;
     _deviation = 0;
   }
   // Override
   void  sample(float new_sample);
   // Printing
   void print_on(outputStream* st) const;
   void print() const;
 };
 // 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);
   // Printing
   void print_on(outputStream* st) const;
   void print() const;
 };
 // Use a least squares fit to a set of data to generate a linear
 // equation.
 //              y = intercept + slope * x
 class LinearLeastSquareFit : public CHeapObj<mtGC> {
   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();
   }
 };
 #endif // SHARE_VM_GC_IMPLEMENTATION_SHARED_GCUTIL_HPP

Mercurial > jdk8-mips64-public > hotspot / annotate

src/share/vm/gc_implementation/shared/gcUtil.hpp@db9981fd3124 (annotated)

src/share/vm/gc_implementation/shared/gcUtil.hpp