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src/share/vm/gc_implementation/shared/gcUtil.cpp@ba764ed4b6f2 (annotated)

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

Sun, 13 Apr 2008 17:43:42 -0400

author

coleenp

date

Sun, 13 Apr 2008 17:43:42 -0400

changeset 548

ba764ed4b6f2

parent 435

a61af66fc99e

child 1580

e018e6884bd8

permissions

-rw-r--r--

6420645: Create a vm that uses compressed oops for up to 32gb heapsizes
Summary: Compressed oops in instances, arrays, and headers. Code contributors are coleenp, phh, never, swamyv
Reviewed-by: jmasa, kamg, acorn, tbell, kvn, rasbold

duke@435	1	/*
duke@435	2	* Copyright 2002-2005 Sun Microsystems, Inc. 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	*
duke@435	19	* Please contact Sun Microsystems, Inc., 4150 Network Circle, Santa Clara,
duke@435	20	* CA 95054 USA or visit www.sun.com if you need additional information or
duke@435	21	* have any questions.
duke@435	22	*
duke@435	23	*/
duke@435	24
duke@435	25	# include "incls/_precompiled.incl"
duke@435	26	# include "incls/_gcUtil.cpp.incl"
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
duke@435	34	// used to be 1, the second to be 1/2, etc until we have 100/weight
duke@435	35	// samples.
duke@435	36	unsigned count_weight = 100/count();
duke@435	37	unsigned adaptive_weight = (MAX2(weight(), count_weight));
duke@435	38
duke@435	39	float new_avg = exp_avg(average, new_sample, adaptive_weight);
duke@435	40
duke@435	41	return new_avg;
duke@435	42	}
duke@435	43
duke@435	44	void AdaptiveWeightedAverage::sample(float new_sample) {
duke@435	45	increment_count();
duke@435	46	assert(count() != 0,
duke@435	47	"Wraparound -- history would be incorrectly discarded");
duke@435	48
duke@435	49	// Compute the new weighted average
duke@435	50	float new_avg = compute_adaptive_average(new_sample, average());
duke@435	51	set_average(new_avg);
duke@435	52	_last_sample = new_sample;
duke@435	53	}
duke@435	54
duke@435	55	void AdaptivePaddedAverage::sample(float new_sample) {
duke@435	56	// Compute our parent classes sample information
duke@435	57	AdaptiveWeightedAverage::sample(new_sample);
duke@435	58
duke@435	59	// Now compute the deviation and the new padded sample
duke@435	60	float new_avg = average();
duke@435	61	float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),
duke@435	62	deviation());
duke@435	63	set_deviation(new_dev);
duke@435	64	set_padded_average(new_avg + padding() * new_dev);
duke@435	65	_last_sample = new_sample;
duke@435	66	}
duke@435	67
duke@435	68	void AdaptivePaddedNoZeroDevAverage::sample(float new_sample) {
duke@435	69	// Compute our parent classes sample information
duke@435	70	AdaptiveWeightedAverage::sample(new_sample);
duke@435	71
duke@435	72	float new_avg = average();
duke@435	73	if (new_sample != 0) {
duke@435	74	// We only create a new deviation if the sample is non-zero
duke@435	75	float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),
duke@435	76	deviation());
duke@435	77
duke@435	78	set_deviation(new_dev);
duke@435	79	}
duke@435	80	set_padded_average(new_avg + padding() * deviation());
duke@435	81	_last_sample = new_sample;
duke@435	82	}
duke@435	83
duke@435	84	LinearLeastSquareFit::LinearLeastSquareFit(unsigned weight) :
duke@435	85	_sum_x(0), _sum_y(0), _sum_xy(0),
duke@435	86	_mean_x(weight), _mean_y(weight) {}
duke@435	87
duke@435	88	void LinearLeastSquareFit::update(double x, double y) {
duke@435	89	_sum_x = _sum_x + x;
duke@435	90	_sum_x_squared = _sum_x_squared + x * x;
duke@435	91	_sum_y = _sum_y + y;
duke@435	92	_sum_xy = _sum_xy + x * y;
duke@435	93	_mean_x.sample(x);
duke@435	94	_mean_y.sample(y);
duke@435	95	assert(_mean_x.count() == _mean_y.count(), "Incorrect count");
duke@435	96	if ( _mean_x.count() > 1 ) {
duke@435	97	double slope_denominator;
duke@435	98	slope_denominator = (_mean_x.count() * _sum_x_squared - _sum_x * _sum_x);
duke@435	99	// Some tolerance should be injected here. A denominator that is
duke@435	100	// nearly 0 should be avoided.
duke@435	101
duke@435	102	if (slope_denominator != 0.0) {
duke@435	103	double slope_numerator;
duke@435	104	slope_numerator = (_mean_x.count() * _sum_xy - _sum_x * _sum_y);
duke@435	105	_slope = slope_numerator / slope_denominator;
duke@435	106
duke@435	107	// The _mean_y and _mean_x are decaying averages and can
duke@435	108	// be used to discount earlier data. If they are used,
duke@435	109	// first consider whether all the quantities should be
duke@435	110	// kept as decaying averages.
duke@435	111	// _intercept = _mean_y.average() - _slope * _mean_x.average();
duke@435	112	_intercept = (_sum_y - _slope * _sum_x) / ((double) _mean_x.count());
duke@435	113	}
duke@435	114	}
duke@435	115	}
duke@435	116
duke@435	117	double LinearLeastSquareFit::y(double x) {
duke@435	118	double new_y;
duke@435	119
duke@435	120	if ( _mean_x.count() > 1 ) {
duke@435	121	new_y = (_intercept + _slope * x);
duke@435	122	return new_y;
duke@435	123	} else {
duke@435	124	return _mean_y.average();
duke@435	125	}
duke@435	126	}
duke@435	127
duke@435	128	// Both decrement_will_decrease() and increment_will_decrease() return
duke@435	129	// true for a slope of 0. That is because a change is necessary before
duke@435	130	// a slope can be calculated and a 0 slope will, in general, indicate
duke@435	131	// that no calculation of the slope has yet been done. Returning true
duke@435	132	// for a slope equal to 0 reflects the intuitive expectation of the
duke@435	133	// dependence on the slope. Don't use the complement of these functions
duke@435	134	// since that untuitive expectation is not built into the complement.
duke@435	135	bool LinearLeastSquareFit::decrement_will_decrease() {
duke@435	136	return (_slope >= 0.00);
duke@435	137	}
duke@435	138
duke@435	139	bool LinearLeastSquareFit::increment_will_decrease() {
duke@435	140	return (_slope <= 0.00);
duke@435	141	}