Mercurial > jdk8-mips64-public > hotspot / file revision

 /*

  * Copyright 2001-2008 Sun Microsystems, Inc.  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 Sun Microsystems, Inc., 4150 Network Circle, Santa Clara,

  * CA 95054 USA or visit www.sun.com if you need additional information or

  * have any questions.

  *

  */

 class AllocationStats VALUE_OBJ_CLASS_SPEC {

   // A duration threshold (in ms) used to filter

   // possibly unreliable samples.

   static float _threshold;

   // We measure the demand between the end of the previous sweep and

   // beginning of this sweep:

   //   Count(end_last_sweep) - Count(start_this_sweep)

   //     + splitBirths(between) - splitDeaths(between)

   // The above number divided by the time since the start [END???] of the

   // previous sweep gives us a time rate of demand for blocks

   // of this size. We compute a padded average of this rate as

   // our current estimate for the time rate of demand for blocks

   // of this size. Similarly, we keep a padded average for the time

   // between sweeps. Our current estimate for demand for blocks of

   // this size is then simply computed as the product of these two

   // estimates.

   AdaptivePaddedAverage _demand_rate_estimate;

   ssize_t     _desired;          // Estimate computed as described above

   ssize_t     _coalDesired;     // desired +/- small-percent for tuning coalescing

   ssize_t     _surplus;         // count - (desired +/- small-percent),

                                 // used to tune splitting in best fit

   ssize_t     _bfrSurp;         // surplus at start of current sweep

   ssize_t     _prevSweep;       // count from end of previous sweep

   ssize_t     _beforeSweep;     // count from before current sweep

   ssize_t     _coalBirths;      // additional chunks from coalescing

   ssize_t     _coalDeaths;      // loss from coalescing

   ssize_t     _splitBirths;     // additional chunks from splitting

   ssize_t     _splitDeaths;     // loss from splitting

   size_t     _returnedBytes;    // number of bytes returned to list.

  public:

   void initialize() {

     AdaptivePaddedAverage* dummy =

       new (&_demand_rate_estimate) AdaptivePaddedAverage(CMS_FLSWeight,

                                                          CMS_FLSPadding);

     _desired = 0;

     _coalDesired = 0;

     _surplus = 0;

     _bfrSurp = 0;

     _prevSweep = 0;

     _beforeSweep = 0;

     _coalBirths = 0;

     _coalDeaths = 0;

     _splitBirths = 0;

     _splitDeaths = 0;

     _returnedBytes = 0;

   }

   AllocationStats() {

     initialize();

   }

   // The rate estimate is in blocks per second.

   void compute_desired(size_t count,

                        float inter_sweep_current,

                        float inter_sweep_estimate) {

     // If the latest inter-sweep time is below our granularity

     // of measurement, we may call in here with

     // inter_sweep_current == 0. However, even for suitably small

     // but non-zero inter-sweep durations, we may not trust the accuracy

     // of accumulated data, since it has not been "integrated"

     // (read "low-pass-filtered") long enough, and would be

     // vulnerable to noisy glitches. In such cases, we

     // ignore the current sample and use currently available

     // historical estimates.

     if (inter_sweep_current > _threshold) {

       ssize_t demand = prevSweep() - count + splitBirths() - splitDeaths();

       float rate = ((float)demand)/inter_sweep_current;

       _demand_rate_estimate.sample(rate);

       _desired = (ssize_t)(_demand_rate_estimate.padded_average()

                            *inter_sweep_estimate);

     }

   }

   ssize_t desired() const { return _desired; }

   void set_desired(ssize_t v) { _desired = v; }

   ssize_t coalDesired() const { return _coalDesired; }

   void set_coalDesired(ssize_t v) { _coalDesired = v; }

   ssize_t surplus() const { return _surplus; }

   void set_surplus(ssize_t v) { _surplus = v; }

   void increment_surplus() { _surplus++; }

   void decrement_surplus() { _surplus--; }

   ssize_t bfrSurp() const { return _bfrSurp; }

   void set_bfrSurp(ssize_t v) { _bfrSurp = v; }

   ssize_t prevSweep() const { return _prevSweep; }

   void set_prevSweep(ssize_t v) { _prevSweep = v; }

   ssize_t beforeSweep() const { return _beforeSweep; }

   void set_beforeSweep(ssize_t v) { _beforeSweep = v; }

   ssize_t coalBirths() const { return _coalBirths; }

   void set_coalBirths(ssize_t v) { _coalBirths = v; }

   void increment_coalBirths() { _coalBirths++; }

   ssize_t coalDeaths() const { return _coalDeaths; }

   void set_coalDeaths(ssize_t v) { _coalDeaths = v; }

   void increment_coalDeaths() { _coalDeaths++; }

   ssize_t splitBirths() const { return _splitBirths; }

   void set_splitBirths(ssize_t v) { _splitBirths = v; }

   void increment_splitBirths() { _splitBirths++; }

   ssize_t splitDeaths() const { return _splitDeaths; }

   void set_splitDeaths(ssize_t v) { _splitDeaths = v; }

   void increment_splitDeaths() { _splitDeaths++; }

   NOT_PRODUCT(

     size_t returnedBytes() const { return _returnedBytes; }

     void set_returnedBytes(size_t v) { _returnedBytes = v; }

   )

 };