1 /*
   2  * Copyright (c) 2002, 2012, Oracle and/or its affiliates. All rights reserved.
   3  * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
   4  *
   5  * This code is free software; you can redistribute it and/or modify it
   6  * under the terms of the GNU General Public License version 2 only, as
   7  * published by the Free Software Foundation.
   8  *
   9  * This code is distributed in the hope that it will be useful, but WITHOUT
  10  * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
  11  * FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License
  12  * version 2 for more details (a copy is included in the LICENSE file that
  13  * accompanied this code).
  14  *
  15  * You should have received a copy of the GNU General Public License version
  16  * 2 along with this work; if not, write to the Free Software Foundation,
  17  * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
  18  *
  19  * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
  20  * or visit www.oracle.com if you need additional information or have any
  21  * questions.
  22  *
  23  */
  24 
  25 #include "precompiled.hpp"
  26 #include "gc_implementation/shared/gcUtil.hpp"
  27 
  28 // Catch-all file for utility classes
  29 
  30 float AdaptiveWeightedAverage::compute_adaptive_average(float new_sample,
  31                                                         float average) {
  32   // We smooth the samples by not using weight() directly until we've
  33   // had enough data to make it meaningful. We'd like the first weight
  34   // used to be 1, the second to be 1/2, etc until we have
  35   // OLD_THRESHOLD/weight samples.
  36   unsigned count_weight = 0;
  37 
  38   // Avoid division by zero if the counter wraps (7158457)
  39   if (!is_old()) {
  40     count_weight = OLD_THRESHOLD/count();
  41   }
  42 
  43   unsigned adaptive_weight = (MAX2(weight(), count_weight));
  44 
  45   float new_avg = exp_avg(average, new_sample, adaptive_weight);
  46 
  47   return new_avg;
  48 }
  49 
  50 void AdaptiveWeightedAverage::sample(float new_sample) {
  51   increment_count();
  52 
  53   // Compute the new weighted average
  54   float new_avg = compute_adaptive_average(new_sample, average());
  55   set_average(new_avg);
  56   _last_sample = new_sample;
  57 }
  58 
  59 void AdaptiveWeightedAverage::print() const {
  60   print_on(tty);
  61 }
  62 
  63 void AdaptiveWeightedAverage::print_on(outputStream* st) const {
  64   guarantee(false, "NYI");
  65 }
  66 
  67 void AdaptivePaddedAverage::print() const {
  68   print_on(tty);
  69 }
  70 
  71 void AdaptivePaddedAverage::print_on(outputStream* st) const {
  72   guarantee(false, "NYI");
  73 }
  74 
  75 void AdaptivePaddedNoZeroDevAverage::print() const {
  76   print_on(tty);
  77 }
  78 
  79 void AdaptivePaddedNoZeroDevAverage::print_on(outputStream* st) const {
  80   guarantee(false, "NYI");
  81 }
  82 
  83 void AdaptivePaddedAverage::sample(float new_sample) {
  84   // Compute new adaptive weighted average based on new sample.
  85   AdaptiveWeightedAverage::sample(new_sample);
  86 
  87   // Now update the deviation and the padded average.
  88   float new_avg = average();
  89   float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),
  90                                            deviation());
  91   set_deviation(new_dev);
  92   set_padded_average(new_avg + padding() * new_dev);
  93   _last_sample = new_sample;
  94 }
  95 
  96 void AdaptivePaddedNoZeroDevAverage::sample(float new_sample) {
  97   // Compute our parent classes sample information
  98   AdaptiveWeightedAverage::sample(new_sample);
  99 
 100   float new_avg = average();
 101   if (new_sample != 0) {
 102     // We only create a new deviation if the sample is non-zero
 103     float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),
 104                                              deviation());
 105 
 106     set_deviation(new_dev);
 107   }
 108   set_padded_average(new_avg + padding() * deviation());
 109   _last_sample = new_sample;
 110 }
 111 
 112 LinearLeastSquareFit::LinearLeastSquareFit(unsigned weight) :
 113   _sum_x(0), _sum_x_squared(0), _sum_y(0), _sum_xy(0),
 114   _intercept(0), _slope(0), _mean_x(weight), _mean_y(weight) {}
 115 
 116 void LinearLeastSquareFit::update(double x, double y) {
 117   _sum_x = _sum_x + x;
 118   _sum_x_squared = _sum_x_squared + x * x;
 119   _sum_y = _sum_y + y;
 120   _sum_xy = _sum_xy + x * y;
 121   _mean_x.sample(x);
 122   _mean_y.sample(y);
 123   assert(_mean_x.count() == _mean_y.count(), "Incorrect count");
 124   if ( _mean_x.count() > 1 ) {
 125     double slope_denominator;
 126     slope_denominator = (_mean_x.count() * _sum_x_squared - _sum_x * _sum_x);
 127     // Some tolerance should be injected here.  A denominator that is
 128     // nearly 0 should be avoided.
 129 
 130     if (slope_denominator != 0.0) {
 131       double slope_numerator;
 132       slope_numerator = (_mean_x.count() * _sum_xy - _sum_x * _sum_y);
 133       _slope = slope_numerator / slope_denominator;
 134 
 135       // The _mean_y and _mean_x are decaying averages and can
 136       // be used to discount earlier data.  If they are used,
 137       // first consider whether all the quantities should be
 138       // kept as decaying averages.
 139       // _intercept = _mean_y.average() - _slope * _mean_x.average();
 140       _intercept = (_sum_y - _slope * _sum_x) / ((double) _mean_x.count());
 141     }
 142   }
 143 }
 144 
 145 double LinearLeastSquareFit::y(double x) {
 146   double new_y;
 147 
 148   if ( _mean_x.count() > 1 ) {
 149     new_y = (_intercept + _slope * x);
 150     return new_y;
 151   } else {
 152     return _mean_y.average();
 153   }
 154 }
 155 
 156 // Both decrement_will_decrease() and increment_will_decrease() return
 157 // true for a slope of 0.  That is because a change is necessary before
 158 // a slope can be calculated and a 0 slope will, in general, indicate
 159 // that no calculation of the slope has yet been done.  Returning true
 160 // for a slope equal to 0 reflects the intuitive expectation of the
 161 // dependence on the slope.  Don't use the complement of these functions
 162 // since that intuitive expectation is not built into the complement.
 163 bool LinearLeastSquareFit::decrement_will_decrease() {
 164   return (_slope >= 0.00);
 165 }
 166 
 167 bool LinearLeastSquareFit::increment_will_decrease() {
 168   return (_slope <= 0.00);
 169 }