jdk8-mips64-public/hotspot: src/share/vm/gc_implementation/shared/gcUtil.hpp@b2ef234911c9 (annotated)

src/share/vm/gc_implementation/shared/gcUtil.hpp@b2ef234911c9 (annotated)

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

Thu, 20 Sep 2012 09:52:56 -0700

author: johnc
date: Thu, 20 Sep 2012 09:52:56 -0700
changeset 4067: b2ef234911c9
parent 3900: d2a62e0f25eb
child 4153: b9a9ed0f8eeb
permissions: -rw-r--r--

7190666: G1: assert(_unused == 0) failed: Inconsistency in PLAB stats
Summary: Reset the fields in ParGCAllocBuffer, that are used for accumulating values for the ResizePLAB sensors in PLABStats, to zero after flushing the values to the PLABStats fields. Flush PLABStats values only when retiring the final allocation buffers prior to disposing of a G1ParScanThreadState object, rather than when retiring every allocation buffer.
Reviewed-by: jwilhelm, jmasa, ysr

 /*
  * Copyright (c) 2002, 2010, 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@b2ef234911c9 (annotated)

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