jdk8-mips64-public/hotspot: src/share/vm/utilities/numberSeq.cpp@c96030fff130 (annotated)

src/share/vm/utilities/numberSeq.cpp@c96030fff130 (annotated)

src/share/vm/utilities/numberSeq.cpp

Thu, 20 Nov 2008 16:56:09 -0800

author: ysr
date: Thu, 20 Nov 2008 16:56:09 -0800
changeset 888: c96030fff130
parent 777: 37f87013dfd8
child 1521: 89f1b9ae8991
permissions: -rw-r--r--

6684579: SoftReference processing can be made more efficient
Summary: For current soft-ref clearing policies, we can decide at marking time if a soft-reference will definitely not be cleared, postponing the decision of whether it will definitely be cleared to the final reference processing phase. This can be especially beneficial in the case of concurrent collectors where the marking is usually concurrent but reference processing is usually not.
Reviewed-by: jmasa

 /*
  * Copyright 2001-2007 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/_numberSeq.cpp.incl"
 AbsSeq::AbsSeq(double alpha) :
   _num(0), _sum(0.0), _sum_of_squares(0.0),
   _davg(0.0), _dvariance(0.0), _alpha(alpha) {
 }
 void AbsSeq::add(double val) {
   if (_num == 0) {
     // if the sequence is empty, the davg is the same as the value
     _davg = val;
     // and the variance is 0
     _dvariance = 0.0;
   } else {
     // otherwise, calculate both
     _davg = (1.0 - _alpha) * val + _alpha * _davg;
     double diff = val - _davg;
     _dvariance = (1.0 - _alpha) * diff * diff + _alpha * _dvariance;
   }
 }
 double AbsSeq::avg() const {
   if (_num == 0)
     return 0.0;
   else
     return _sum / total();
 }
 double AbsSeq::variance() const {
   if (_num <= 1)
     return 0.0;
   double x_bar = avg();
   double result = _sum_of_squares / total() - x_bar * x_bar;
   if (result < 0.0) {
     // due to loss-of-precision errors, the variance might be negative
     // by a small bit
     //    guarantee(-0.1 < result && result < 0.0,
     //        "if variance is negative, it should be very small");
     result = 0.0;
   }
   return result;
 }
 double AbsSeq::sd() const {
   double var = variance();
   guarantee( var >= 0.0, "variance should not be negative" );
   return sqrt(var);
 }
 double AbsSeq::davg() const {
   return _davg;
 }
 double AbsSeq::dvariance() const {
   if (_num <= 1)
     return 0.0;
   double result = _dvariance;
   if (result < 0.0) {
     // due to loss-of-precision errors, the variance might be negative
     // by a small bit
     guarantee(-0.1 < result && result < 0.0,
                "if variance is negative, it should be very small");
     result = 0.0;
   }
   return result;
 }
 double AbsSeq::dsd() const {
   double var = dvariance();
   guarantee( var >= 0.0, "variance should not be negative" );
   return sqrt(var);
 }
 NumberSeq::NumberSeq(double alpha) :
   AbsSeq(alpha), _maximum(0.0), _last(0.0) {
 }
 bool NumberSeq::check_nums(NumberSeq *total, int n, NumberSeq **parts) {
   for (int i = 0; i < n; ++i) {
     if (parts[i] != NULL && total->num() != parts[i]->num())
       return false;
   }
   return true;
 }
 NumberSeq::NumberSeq(NumberSeq *total, int n, NumberSeq **parts) {
   guarantee(check_nums(total, n, parts), "all seq lengths should match");
   double sum = total->sum();
   for (int i = 0; i < n; ++i) {
     if (parts[i] != NULL)
       sum -= parts[i]->sum();
   }
   _num = total->num();
   _sum = sum;
   // we do not calculate these...
   _sum_of_squares = -1.0;
   _maximum = -1.0;
   _davg = -1.0;
   _dvariance = -1.0;
 }
 void NumberSeq::add(double val) {
   AbsSeq::add(val);
   _last = val;
   if (_num == 0) {
     _maximum = val;
   } else {
     if (val > _maximum)
       _maximum = val;
   }
   _sum += val;
   _sum_of_squares += val * val;
   ++_num;
 }
 TruncatedSeq::TruncatedSeq(int length, double alpha):
   AbsSeq(alpha), _length(length), _next(0) {
   _sequence = NEW_C_HEAP_ARRAY(double, _length);
   for (int i = 0; i < _length; ++i)
     _sequence[i] = 0.0;
 }
 void TruncatedSeq::add(double val) {
   AbsSeq::add(val);
   // get the oldest value in the sequence...
   double old_val = _sequence[_next];
   // ...remove it from the sum and sum of squares
   _sum -= old_val;
   _sum_of_squares -= old_val * old_val;
   // ...and update them with the new value
   _sum += val;
   _sum_of_squares += val * val;
   // now replace the old value with the new one
   _sequence[_next] = val;
   _next = (_next + 1) % _length;
   // only increase it if the buffer is not full
   if (_num < _length)
     ++_num;
   guarantee( variance() > -1.0, "variance should be >= 0" );
 }
 // can't easily keep track of this incrementally...
 double TruncatedSeq::maximum() const {
   if (_num == 0)
     return 0.0;
   double ret = _sequence[0];
   for (int i = 1; i < _num; ++i) {
     double val = _sequence[i];
     if (val > ret)
       ret = val;
   }
   return ret;
 }
 double TruncatedSeq::last() const {
   if (_num == 0)
     return 0.0;
   unsigned last_index = (_next + _length - 1) % _length;
   return _sequence[last_index];
 }
 double TruncatedSeq::oldest() const {
   if (_num == 0)
     return 0.0;
   else if (_num < _length)
     // index 0 always oldest value until the array is full
     return _sequence[0];
   else {
     // since the array is full, _next is over the oldest value
     return _sequence[_next];
   }
 }
 double TruncatedSeq::predict_next() const {
   if (_num == 0)
     return 0.0;
   double num           = (double) _num;
   double x_squared_sum = 0.0;
   double x_sum         = 0.0;
   double y_sum         = 0.0;
   double xy_sum        = 0.0;
   double x_avg         = 0.0;
   double y_avg         = 0.0;
   int first = (_next + _length - _num) % _length;
   for (int i = 0; i < _num; ++i) {
     double x = (double) i;
     double y =  _sequence[(first + i) % _length];
     x_squared_sum += x * x;
     x_sum         += x;
     y_sum         += y;
     xy_sum        += x * y;
   }
   x_avg = x_sum / num;
   y_avg = y_sum / num;
   double Sxx = x_squared_sum - x_sum * x_sum / num;
   double Sxy = xy_sum - x_sum * y_sum / num;
   double b1 = Sxy / Sxx;
   double b0 = y_avg - b1 * x_avg;
   return b0 + b1 * num;
 }

Mercurial > jdk8-mips64-public > hotspot / annotate

src/share/vm/utilities/numberSeq.cpp@c96030fff130 (annotated)

src/share/vm/utilities/numberSeq.cpp