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

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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.
+ *
+ */
+
+# include "incls/_precompiled.incl"
+# include "incls/_gcUtil.cpp.incl"
+
+// Catch-all file for utility classes
+
+float AdaptiveWeightedAverage::compute_adaptive_average(float new_sample,
+                                                        float average) {
+  // We smooth the samples by not using weight() directly until we've
+  // had enough data to make it meaningful. We'd like the first weight
+  // used to be 1, the second to be 1/2, etc until we have 100/weight
+  // samples.
+  unsigned count_weight = 100/count();
+  unsigned adaptive_weight = (MAX2(weight(), count_weight));
+
+  float new_avg = exp_avg(average, new_sample, adaptive_weight);
+
+  return new_avg;
+}
+
+void AdaptiveWeightedAverage::sample(float new_sample) {
+  increment_count();
+  assert(count() != 0,
+         "Wraparound -- history would be incorrectly discarded");
+
+  // Compute the new weighted average
+  float new_avg = compute_adaptive_average(new_sample, average());
+  set_average(new_avg);
+  _last_sample = new_sample;
+}
+
+void AdaptivePaddedAverage::sample(float new_sample) {
+  // Compute our parent classes sample information
+  AdaptiveWeightedAverage::sample(new_sample);
+
+  // Now compute the deviation and the new padded sample
+  float new_avg = average();
+  float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),
+                                           deviation());
+  set_deviation(new_dev);
+  set_padded_average(new_avg + padding() * new_dev);
+  _last_sample = new_sample;
+}
+
+void AdaptivePaddedNoZeroDevAverage::sample(float new_sample) {
+  // Compute our parent classes sample information
+  AdaptiveWeightedAverage::sample(new_sample);
+
+  float new_avg = average();
+  if (new_sample != 0) {
+    // We only create a new deviation if the sample is non-zero
+    float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),
+                                             deviation());
+
+    set_deviation(new_dev);
+  }
+  set_padded_average(new_avg + padding() * deviation());
+  _last_sample = new_sample;
+}
+
+LinearLeastSquareFit::LinearLeastSquareFit(unsigned weight) :
+  _sum_x(0), _sum_y(0), _sum_xy(0),
+  _mean_x(weight), _mean_y(weight) {}
+
+void LinearLeastSquareFit::update(double x, double y) {
+  _sum_x = _sum_x + x;
+  _sum_x_squared = _sum_x_squared + x * x;
+  _sum_y = _sum_y + y;
+  _sum_xy = _sum_xy + x * y;
+  _mean_x.sample(x);
+  _mean_y.sample(y);
+  assert(_mean_x.count() == _mean_y.count(), "Incorrect count");
+  if ( _mean_x.count() > 1 ) {
+    double slope_denominator;
+    slope_denominator = (_mean_x.count() * _sum_x_squared - _sum_x * _sum_x);
+    // Some tolerance should be injected here.  A denominator that is
+    // nearly 0 should be avoided.
+
+    if (slope_denominator != 0.0) {
+      double slope_numerator;
+      slope_numerator = (_mean_x.count() * _sum_xy - _sum_x * _sum_y);
+      _slope = slope_numerator / slope_denominator;
+
+      // The _mean_y and _mean_x are decaying averages and can
+      // be used to discount earlier data.  If they are used,
+      // first consider whether all the quantities should be
+      // kept as decaying averages.
+      // _intercept = _mean_y.average() - _slope * _mean_x.average();
+      _intercept = (_sum_y - _slope * _sum_x) / ((double) _mean_x.count());
+    }
+  }
+}
+
+double LinearLeastSquareFit::y(double x) {
+  double new_y;
+
+  if ( _mean_x.count() > 1 ) {
+    new_y = (_intercept + _slope * x);
+    return new_y;
+  } else {
+    return _mean_y.average();
+  }
+}
+
+// Both decrement_will_decrease() and increment_will_decrease() return
+// true for a slope of 0.  That is because a change is necessary before
+// a slope can be calculated and a 0 slope will, in general, indicate
+// that no calculation of the slope has yet been done.  Returning true
+// for a slope equal to 0 reflects the intuitive expectation of the
+// dependence on the slope.  Don't use the complement of these functions
+// since that untuitive expectation is not built into the complement.
+bool LinearLeastSquareFit::decrement_will_decrease() {
+  return (_slope >= 0.00);
+}
+
+bool LinearLeastSquareFit::increment_will_decrease() {
+  return (_slope <= 0.00);
+}