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

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+/*
+ * 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();
+  }
+};