--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/src/hotspot/share/gc/shared/gcUtil.cpp Tue Sep 12 19:03:39 2017 +0200
@@ -0,0 +1,169 @@
+/*
+ * Copyright (c) 2002, 2015, 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 "gc/shared/gcUtil.hpp"
+
+// 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
+ // OLD_THRESHOLD/weight samples.
+ unsigned count_weight = 0;
+
+ // Avoid division by zero if the counter wraps (7158457)
+ if (!is_old()) {
+ count_weight = OLD_THRESHOLD/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();
+
+ // Compute the new weighted average
+ float new_avg = compute_adaptive_average(new_sample, average());
+ set_average(new_avg);
+ _last_sample = new_sample;
+}
+
+void AdaptiveWeightedAverage::print() const {
+ print_on(tty);
+}
+
+void AdaptiveWeightedAverage::print_on(outputStream* st) const {
+ guarantee(false, "NYI");
+}
+
+void AdaptivePaddedAverage::print() const {
+ print_on(tty);
+}
+
+void AdaptivePaddedAverage::print_on(outputStream* st) const {
+ guarantee(false, "NYI");
+}
+
+void AdaptivePaddedNoZeroDevAverage::print() const {
+ print_on(tty);
+}
+
+void AdaptivePaddedNoZeroDevAverage::print_on(outputStream* st) const {
+ guarantee(false, "NYI");
+}
+
+void AdaptivePaddedAverage::sample(float new_sample) {
+ // Compute new adaptive weighted average based on new sample.
+ AdaptiveWeightedAverage::sample(new_sample);
+
+ // Now update the deviation and the padded average.
+ 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_x_squared(0), _sum_y(0), _sum_xy(0),
+ _intercept(0), _slope(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 intuitive 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);
+}