Mercurial > jdk8-mips64-public > hotspot / file revision

 /*

  * Copyright (c) 2002, 2012, 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.

  *

  */

 #include "precompiled.hpp"

 #include "gc_implementation/shared/gcUtil.hpp"

 // 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

   // OLD_THRESHOLD/weight samples.

   unsigned count_weight = 0;

   // Avoid division by zero if the counter wraps (7158457)

   if (!is_old()) {

     count_weight = OLD_THRESHOLD/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();

   // Compute the new weighted average

   float new_avg = compute_adaptive_average(new_sample, average());

   set_average(new_avg);

   _last_sample = new_sample;

 }

 void AdaptiveWeightedAverage::print() const {

   print_on(tty);

 }

 void AdaptiveWeightedAverage::print_on(outputStream* st) const {

   guarantee(false, "NYI");

 }

 void AdaptivePaddedAverage::print() const {

   print_on(tty);

 }

 void AdaptivePaddedAverage::print_on(outputStream* st) const {

   guarantee(false, "NYI");

 }

 void AdaptivePaddedNoZeroDevAverage::print() const {

   print_on(tty);

 }

 void AdaptivePaddedNoZeroDevAverage::print_on(outputStream* st) const {

   guarantee(false, "NYI");

 }

 void AdaptivePaddedAverage::sample(float new_sample) {

   // Compute new adaptive weighted average based on new sample.

   AdaptiveWeightedAverage::sample(new_sample);

   // Now update the deviation and the padded average.

   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_x_squared(0), _sum_y(0), _sum_xy(0),

   _intercept(0), _slope(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);

 }