Mercurial > hg > graal-jvmci-8
view src/share/vm/gc_implementation/shared/gcUtil.cpp @ 807:d44bdab1c03d
6843694: G1: assert(index < _vs.committed_size(),"bad index"), g1BlockOffsetTable.inline.hpp:55
Summary: For heaps larger than 32Gb, the number of heap regions overflows the data type used to hold the region index in the SparsePRT structure. Changed the region indexes, card indexes, and RSet hash table buckets to ints and added some size overflow guarantees.
Reviewed-by: ysr, tonyp
author | johnc |
---|---|
date | Thu, 11 Jun 2009 17:19:33 -0700 |
parents | a61af66fc99e |
children | e018e6884bd8 |
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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); }