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src/share/vm/gc_implementation/shared/gcUtil.cpp@0982ec23da03 (annotated)

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

Thu, 19 Jun 2014 13:31:14 +0200

author

brutisso

date

Thu, 19 Jun 2014 13:31:14 +0200

changeset 6904

0982ec23da03

parent 4153

b9a9ed0f8eeb

child 6876

710a3c8b516e

permissions

-rw-r--r--

8043607: Add a GC id as a log decoration similar to PrintGCTimeStamps
Reviewed-by: jwilhelm, ehelin, tschatzl

duke@435	1	/*
mikael@4153	2	* Copyright (c) 2002, 2012, Oracle and/or its affiliates. All rights reserved.
duke@435	3	* DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
duke@435	4	*
duke@435	5	* This code is free software; you can redistribute it and/or modify it
duke@435	6	* under the terms of the GNU General Public License version 2 only, as
duke@435	7	* published by the Free Software Foundation.
duke@435	8	*
duke@435	9	* This code is distributed in the hope that it will be useful, but WITHOUT
duke@435	10	* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
duke@435	11	* FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
duke@435	12	* version 2 for more details (a copy is included in the LICENSE file that
duke@435	13	* accompanied this code).
duke@435	14	*
duke@435	15	* You should have received a copy of the GNU General Public License version
duke@435	16	* 2 along with this work; if not, write to the Free Software Foundation,
duke@435	17	* Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
duke@435	18	*
trims@1907	19	* Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
trims@1907	20	* or visit www.oracle.com if you need additional information or have any
trims@1907	21	* questions.
duke@435	22	*
duke@435	23	*/
duke@435	24
stefank@2314	25	#include "precompiled.hpp"
stefank@2314	26	#include "gc_implementation/shared/gcUtil.hpp"
duke@435	27
duke@435	28	// Catch-all file for utility classes
duke@435	29
duke@435	30	float AdaptiveWeightedAverage::compute_adaptive_average(float new_sample,
duke@435	31	float average) {
duke@435	32	// We smooth the samples by not using weight() directly until we've
duke@435	33	// had enough data to make it meaningful. We'd like the first weight
mikael@3763	34	// used to be 1, the second to be 1/2, etc until we have
mikael@3763	35	// OLD_THRESHOLD/weight samples.
mikael@3763	36	unsigned count_weight = 0;
mikael@3763	37
mikael@3763	38	// Avoid division by zero if the counter wraps (7158457)
mikael@3763	39	if (!is_old()) {
mikael@3763	40	count_weight = OLD_THRESHOLD/count();
mikael@3763	41	}
mikael@3763	42
duke@435	43	unsigned adaptive_weight = (MAX2(weight(), count_weight));
duke@435	44
duke@435	45	float new_avg = exp_avg(average, new_sample, adaptive_weight);
duke@435	46
duke@435	47	return new_avg;
duke@435	48	}
duke@435	49
duke@435	50	void AdaptiveWeightedAverage::sample(float new_sample) {
duke@435	51	increment_count();
duke@435	52
duke@435	53	// Compute the new weighted average
duke@435	54	float new_avg = compute_adaptive_average(new_sample, average());
duke@435	55	set_average(new_avg);
duke@435	56	_last_sample = new_sample;
duke@435	57	}
duke@435	58
ysr@1580	59	void AdaptiveWeightedAverage::print() const {
ysr@1580	60	print_on(tty);
ysr@1580	61	}
ysr@1580	62
ysr@1580	63	void AdaptiveWeightedAverage::print_on(outputStream* st) const {
ysr@1580	64	guarantee(false, "NYI");
ysr@1580	65	}
ysr@1580	66
ysr@1580	67	void AdaptivePaddedAverage::print() const {
ysr@1580	68	print_on(tty);
ysr@1580	69	}
ysr@1580	70
ysr@1580	71	void AdaptivePaddedAverage::print_on(outputStream* st) const {
ysr@1580	72	guarantee(false, "NYI");
ysr@1580	73	}
ysr@1580	74
ysr@1580	75	void AdaptivePaddedNoZeroDevAverage::print() const {
ysr@1580	76	print_on(tty);
ysr@1580	77	}
ysr@1580	78
ysr@1580	79	void AdaptivePaddedNoZeroDevAverage::print_on(outputStream* st) const {
ysr@1580	80	guarantee(false, "NYI");
ysr@1580	81	}
ysr@1580	82
duke@435	83	void AdaptivePaddedAverage::sample(float new_sample) {
ysr@1580	84	// Compute new adaptive weighted average based on new sample.
duke@435	85	AdaptiveWeightedAverage::sample(new_sample);
duke@435	86
ysr@1580	87	// Now update the deviation and the padded average.
duke@435	88	float new_avg = average();
duke@435	89	float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),
duke@435	90	deviation());
duke@435	91	set_deviation(new_dev);
duke@435	92	set_padded_average(new_avg + padding() * new_dev);
duke@435	93	_last_sample = new_sample;
duke@435	94	}
duke@435	95
duke@435	96	void AdaptivePaddedNoZeroDevAverage::sample(float new_sample) {
duke@435	97	// Compute our parent classes sample information
duke@435	98	AdaptiveWeightedAverage::sample(new_sample);
duke@435	99
duke@435	100	float new_avg = average();
duke@435	101	if (new_sample != 0) {
duke@435	102	// We only create a new deviation if the sample is non-zero
duke@435	103	float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),
duke@435	104	deviation());
duke@435	105
duke@435	106	set_deviation(new_dev);
duke@435	107	}
duke@435	108	set_padded_average(new_avg + padding() * deviation());
duke@435	109	_last_sample = new_sample;
duke@435	110	}
duke@435	111
duke@435	112	LinearLeastSquareFit::LinearLeastSquareFit(unsigned weight) :
phh@2505	113	_sum_x(0), _sum_x_squared(0), _sum_y(0), _sum_xy(0),
phh@2505	114	_intercept(0), _slope(0), _mean_x(weight), _mean_y(weight) {}
duke@435	115
duke@435	116	void LinearLeastSquareFit::update(double x, double y) {
duke@435	117	_sum_x = _sum_x + x;
duke@435	118	_sum_x_squared = _sum_x_squared + x * x;
duke@435	119	_sum_y = _sum_y + y;
duke@435	120	_sum_xy = _sum_xy + x * y;
duke@435	121	_mean_x.sample(x);
duke@435	122	_mean_y.sample(y);
duke@435	123	assert(_mean_x.count() == _mean_y.count(), "Incorrect count");
duke@435	124	if ( _mean_x.count() > 1 ) {
duke@435	125	double slope_denominator;
duke@435	126	slope_denominator = (_mean_x.count() * _sum_x_squared - _sum_x * _sum_x);
duke@435	127	// Some tolerance should be injected here. A denominator that is
duke@435	128	// nearly 0 should be avoided.
duke@435	129
duke@435	130	if (slope_denominator != 0.0) {
duke@435	131	double slope_numerator;
duke@435	132	slope_numerator = (_mean_x.count() * _sum_xy - _sum_x * _sum_y);
duke@435	133	_slope = slope_numerator / slope_denominator;
duke@435	134
duke@435	135	// The _mean_y and _mean_x are decaying averages and can
duke@435	136	// be used to discount earlier data. If they are used,
duke@435	137	// first consider whether all the quantities should be
duke@435	138	// kept as decaying averages.
duke@435	139	// _intercept = _mean_y.average() - _slope * _mean_x.average();
duke@435	140	_intercept = (_sum_y - _slope * _sum_x) / ((double) _mean_x.count());
duke@435	141	}
duke@435	142	}
duke@435	143	}
duke@435	144
duke@435	145	double LinearLeastSquareFit::y(double x) {
duke@435	146	double new_y;
duke@435	147
duke@435	148	if ( _mean_x.count() > 1 ) {
duke@435	149	new_y = (_intercept + _slope * x);
duke@435	150	return new_y;
duke@435	151	} else {
duke@435	152	return _mean_y.average();
duke@435	153	}
duke@435	154	}
duke@435	155
duke@435	156	// Both decrement_will_decrease() and increment_will_decrease() return
duke@435	157	// true for a slope of 0. That is because a change is necessary before
duke@435	158	// a slope can be calculated and a 0 slope will, in general, indicate
duke@435	159	// that no calculation of the slope has yet been done. Returning true
duke@435	160	// for a slope equal to 0 reflects the intuitive expectation of the
duke@435	161	// dependence on the slope. Don't use the complement of these functions
duke@435	162	// since that untuitive expectation is not built into the complement.
duke@435	163	bool LinearLeastSquareFit::decrement_will_decrease() {
duke@435	164	return (_slope >= 0.00);
duke@435	165	}
duke@435	166
duke@435	167	bool LinearLeastSquareFit::increment_will_decrease() {
duke@435	168	return (_slope <= 0.00);
duke@435	169	}