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src/share/vm/gc_implementation/shared/gcUtil.hpp@7a102acc9f17 (annotated)

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

Fri, 25 Sep 2009 12:17:06 -0700

author

trims

date

Fri, 25 Sep 2009 12:17:06 -0700

changeset 1417

7a102acc9f17

parent 772

9ee9cf798b59

child 1580

e018e6884bd8

permissions

-rw-r--r--

Merge

duke@435	1	/*
xdono@772	2	* Copyright 2002-2008 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	// Catch-all file for utility classes
duke@435	26
duke@435	27	// A weighted average maintains a running, weighted average
duke@435	28	// of some float value (templates would be handy here if we
duke@435	29	// need different types).
duke@435	30	//
duke@435	31	// The average is adaptive in that we smooth it for the
duke@435	32	// initial samples; we don't use the weight until we have
duke@435	33	// enough samples for it to be meaningful.
duke@435	34	//
duke@435	35	// This serves as our best estimate of a future unknown.
duke@435	36	//
duke@435	37	class AdaptiveWeightedAverage : public CHeapObj {
duke@435	38	private:
duke@435	39	float _average; // The last computed average
duke@435	40	unsigned _sample_count; // How often we've sampled this average
duke@435	41	unsigned _weight; // The weight used to smooth the averages
duke@435	42	// A higher weight favors the most
duke@435	43	// recent data.
duke@435	44
duke@435	45	protected:
duke@435	46	float _last_sample; // The last value sampled.
duke@435	47
duke@435	48	void increment_count() { _sample_count++; }
duke@435	49	void set_average(float avg) { _average = avg; }
duke@435	50
duke@435	51	// Helper function, computes an adaptive weighted average
duke@435	52	// given a sample and the last average
duke@435	53	float compute_adaptive_average(float new_sample, float average);
duke@435	54
duke@435	55	public:
duke@435	56	// Input weight must be between 0 and 100
duke@435	57	AdaptiveWeightedAverage(unsigned weight) :
duke@435	58	_average(0.0), _sample_count(0), _weight(weight), _last_sample(0.0) {
duke@435	59	}
duke@435	60
iveresov@703	61	void clear() {
iveresov@703	62	_average = 0;
iveresov@703	63	_sample_count = 0;
iveresov@703	64	_last_sample = 0;
iveresov@703	65	}
iveresov@703	66
duke@435	67	// Accessors
duke@435	68	float average() const { return _average; }
duke@435	69	unsigned weight() const { return _weight; }
duke@435	70	unsigned count() const { return _sample_count; }
duke@435	71	float last_sample() const { return _last_sample; }
duke@435	72
duke@435	73	// Update data with a new sample.
duke@435	74	void sample(float new_sample);
duke@435	75
duke@435	76	static inline float exp_avg(float avg, float sample,
duke@435	77	unsigned int weight) {
duke@435	78	assert(0 <= weight && weight <= 100, "weight must be a percent");
duke@435	79	return (100.0F - weight) * avg / 100.0F + weight * sample / 100.0F;
duke@435	80	}
duke@435	81	static inline size_t exp_avg(size_t avg, size_t sample,
duke@435	82	unsigned int weight) {
duke@435	83	// Convert to float and back to avoid integer overflow.
duke@435	84	return (size_t)exp_avg((float)avg, (float)sample, weight);
duke@435	85	}
duke@435	86	};
duke@435	87
duke@435	88
duke@435	89	// A weighted average that includes a deviation from the average,
duke@435	90	// some multiple of which is added to the average.
duke@435	91	//
duke@435	92	// This serves as our best estimate of an upper bound on a future
duke@435	93	// unknown.
duke@435	94	class AdaptivePaddedAverage : public AdaptiveWeightedAverage {
duke@435	95	private:
duke@435	96	float _padded_avg; // The last computed padded average
duke@435	97	float _deviation; // Running deviation from the average
duke@435	98	unsigned _padding; // A multiple which, added to the average,
duke@435	99	// gives us an upper bound guess.
duke@435	100
duke@435	101	protected:
duke@435	102	void set_padded_average(float avg) { _padded_avg = avg; }
duke@435	103	void set_deviation(float dev) { _deviation = dev; }
duke@435	104
duke@435	105	public:
duke@435	106	AdaptivePaddedAverage() :
duke@435	107	AdaptiveWeightedAverage(0),
duke@435	108	_padded_avg(0.0), _deviation(0.0), _padding(0) {}
duke@435	109
duke@435	110	AdaptivePaddedAverage(unsigned weight, unsigned padding) :
duke@435	111	AdaptiveWeightedAverage(weight),
duke@435	112	_padded_avg(0.0), _deviation(0.0), _padding(padding) {}
duke@435	113
duke@435	114	// Placement support
duke@435	115	void* operator new(size_t ignored, void* p) { return p; }
duke@435	116	// Allocator
duke@435	117	void* operator new(size_t size) { return CHeapObj::operator new(size); }
duke@435	118
duke@435	119	// Accessor
duke@435	120	float padded_average() const { return _padded_avg; }
duke@435	121	float deviation() const { return _deviation; }
duke@435	122	unsigned padding() const { return _padding; }
duke@435	123
iveresov@703	124	void clear() {
iveresov@703	125	AdaptiveWeightedAverage::clear();
iveresov@703	126	_padded_avg = 0;
iveresov@703	127	_deviation = 0;
iveresov@703	128	}
iveresov@703	129
duke@435	130	// Override
duke@435	131	void sample(float new_sample);
duke@435	132	};
duke@435	133
duke@435	134	// A weighted average that includes a deviation from the average,
duke@435	135	// some multiple of which is added to the average.
duke@435	136	//
duke@435	137	// This serves as our best estimate of an upper bound on a future
duke@435	138	// unknown.
duke@435	139	// A special sort of padded average: it doesn't update deviations
duke@435	140	// if the sample is zero. The average is allowed to change. We're
duke@435	141	// preventing the zero samples from drastically changing our padded
duke@435	142	// average.
duke@435	143	class AdaptivePaddedNoZeroDevAverage : public AdaptivePaddedAverage {
duke@435	144	public:
duke@435	145	AdaptivePaddedNoZeroDevAverage(unsigned weight, unsigned padding) :
duke@435	146	AdaptivePaddedAverage(weight, padding) {}
duke@435	147	// Override
duke@435	148	void sample(float new_sample);
duke@435	149	};
duke@435	150	// Use a least squares fit to a set of data to generate a linear
duke@435	151	// equation.
duke@435	152	// y = intercept + slope * x
duke@435	153
duke@435	154	class LinearLeastSquareFit : public CHeapObj {
duke@435	155	double _sum_x; // sum of all independent data points x
duke@435	156	double _sum_x_squared; // sum of all independent data points x**2
duke@435	157	double _sum_y; // sum of all dependent data points y
duke@435	158	double _sum_xy; // sum of all x * y.
duke@435	159	double _intercept; // constant term
duke@435	160	double _slope; // slope
duke@435	161	// The weighted averages are not currently used but perhaps should
duke@435	162	// be used to get decaying averages.
duke@435	163	AdaptiveWeightedAverage _mean_x; // weighted mean of independent variable
duke@435	164	AdaptiveWeightedAverage _mean_y; // weighted mean of dependent variable
duke@435	165
duke@435	166	public:
duke@435	167	LinearLeastSquareFit(unsigned weight);
duke@435	168	void update(double x, double y);
duke@435	169	double y(double x);
duke@435	170	double slope() { return _slope; }
duke@435	171	// Methods to decide if a change in the dependent variable will
duke@435	172	// achive a desired goal. Note that these methods are not
duke@435	173	// complementary and both are needed.
duke@435	174	bool decrement_will_decrease();
duke@435	175	bool increment_will_decrease();
duke@435	176	};
duke@435	177
duke@435	178	class GCPauseTimer : StackObj {
duke@435	179	elapsedTimer* _timer;
duke@435	180	public:
duke@435	181	GCPauseTimer(elapsedTimer* timer) {
duke@435	182	_timer = timer;
duke@435	183	_timer->stop();
duke@435	184	}
duke@435	185	~GCPauseTimer() {
duke@435	186	_timer->start();
duke@435	187	}
duke@435	188	};