--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/hotspot/src/share/vm/gc_implementation/shared/gcUtil.hpp Sat Dec 01 00:00:00 2007 +0000
@@ -0,0 +1,176 @@
+/*
+ * 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();
+ }
+};