Mercurial > hg > graal-compiler
view src/share/vm/utilities/numberSeq.cpp @ 2149:7e37af9d69ef
7011379: G1: overly long concurrent marking cycles
Summary: This changeset introduces filtering of SATB buffers at the point when they are about to be enqueued. If this filtering clears enough entries on each buffer, the buffer can then be re-used and not enqueued. This cuts down the number of SATB buffers that need to be processed by the concurrent marking threads.
Reviewed-by: johnc, ysr
author | tonyp |
---|---|
date | Wed, 19 Jan 2011 09:35:17 -0500 |
parents | f95d63e2154a |
children | 2c0751569716 |
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/* * Copyright (c) 2001, 2010, Oracle and/or its affiliates. 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 Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA * or visit www.oracle.com if you need additional information or have any * questions. * */ #include "precompiled.hpp" #include "memory/allocation.inline.hpp" #include "utilities/debug.hpp" #include "utilities/globalDefinitions.hpp" #include "utilities/numberSeq.hpp" 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; } // Printing/Debugging Support void AbsSeq::dump() { dump_on(gclog_or_tty); } void AbsSeq::dump_on(outputStream* s) { s->print_cr("\t _num = %d, _sum = %7.3f, _sum_of_squares = %7.3f", _num, _sum, _sum_of_squares); s->print_cr("\t _davg = %7.3f, _dvariance = %7.3f, _alpha = %7.3f", _davg, _dvariance, _alpha); } void NumberSeq::dump_on(outputStream* s) { AbsSeq::dump_on(s); s->print_cr("\t\t _last = %7.3f, _maximum = %7.3f"); } void TruncatedSeq::dump_on(outputStream* s) { AbsSeq::dump_on(s); s->print_cr("\t\t _length = %d, _next = %d", _length, _next); for (int i = 0; i < _length; i++) { if (i%5 == 0) { s->cr(); s->print("\t"); } s->print("\t[%d]=%7.3f", i, _sequence[i]); } s->print_cr(""); }