/*
 * 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.  Oracle designates this
 * particular file as subject to the "Classpath" exception as provided
 * by Oracle in the LICENSE file that accompanied this code.
 *
 * 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
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/*
 * This file is available under and governed by the GNU General Public
 * License version 2 only, as published by the Free Software Foundation.
 * However, the following notice accompanied the original version of this
 * file:
 *
 * Written by Doug Lea with assistance from members of JCP JSR-166
 * Expert Group and released to the public domain, as explained at
 * http://creativecommons.org/publicdomain/zero/1.0/
 */

package java.util.concurrent;

import org.naniteproject.annotation.NaniteNotImplemented;

import java.util.Collection;
import java.util.concurrent.RejectedExecutionException;

/**
 * A thread managed by a {@link ForkJoinPool}, which executes
 * {@link ForkJoinTask}s.
 * This class is subclassable solely for the sake of adding
 * functionality -- there are no overridable methods dealing with
 * scheduling or execution.  However, you can override initialization
 * and termination methods surrounding the main task processing loop.
 * If you do create such a subclass, you will also need to supply a
 * custom {@link ForkJoinPool.ForkJoinWorkerThreadFactory} to use it
 * in a {@code ForkJoinPool}.
 *
 * @since 1.7
 * @author Doug Lea
 */
/* NANITE: Unsafe API is not supported */
@NaniteNotImplemented("Unsafe API is not supported")
public class ForkJoinWorkerThread extends Thread {
    /*
     * Overview:
     *
     * ForkJoinWorkerThreads are managed by ForkJoinPools and perform
     * ForkJoinTasks. This class includes bookkeeping in support of
     * worker activation, suspension, and lifecycle control described
     * in more detail in the internal documentation of class
     * ForkJoinPool. And as described further below, this class also
     * includes special-cased support for some ForkJoinTask
     * methods. But the main mechanics involve work-stealing:
     *
     * Work-stealing queues are special forms of Deques that support
     * only three of the four possible end-operations -- push, pop,
     * and deq (aka steal), under the further constraints that push
     * and pop are called only from the owning thread, while deq may
     * be called from other threads.  (If you are unfamiliar with
     * them, you probably want to read Herlihy and Shavit's book "The
     * Art of Multiprocessor programming", chapter 16 describing these
     * in more detail before proceeding.)  The main work-stealing
     * queue design is roughly similar to those in the papers "Dynamic
     * Circular Work-Stealing Deque" by Chase and Lev, SPAA 2005
     * (http://research.sun.com/scalable/pubs/index.html) and
     * "Idempotent work stealing" by Michael, Saraswat, and Vechev,
     * PPoPP 2009 (http://portal.acm.org/citation.cfm?id=1504186).
     * The main differences ultimately stem from gc requirements that
     * we null out taken slots as soon as we can, to maintain as small
     * a footprint as possible even in programs generating huge
     * numbers of tasks. To accomplish this, we shift the CAS
     * arbitrating pop vs deq (steal) from being on the indices
     * ("queueBase" and "queueTop") to the slots themselves (mainly
     * via method "casSlotNull()"). So, both a successful pop and deq
     * mainly entail a CAS of a slot from non-null to null.  Because
     * we rely on CASes of references, we do not need tag bits on
     * queueBase or queueTop.  They are simple ints as used in any
     * circular array-based queue (see for example ArrayDeque).
     * Updates to the indices must still be ordered in a way that
     * guarantees that queueTop == queueBase means the queue is empty,
     * but otherwise may err on the side of possibly making the queue
     * appear nonempty when a push, pop, or deq have not fully
     * committed. Note that this means that the deq operation,
     * considered individually, is not wait-free. One thief cannot
     * successfully continue until another in-progress one (or, if
     * previously empty, a push) completes.  However, in the
     * aggregate, we ensure at least probabilistic non-blockingness.
     * If an attempted steal fails, a thief always chooses a different
     * random victim target to try next. So, in order for one thief to
     * progress, it suffices for any in-progress deq or new push on
     * any empty queue to complete.
     *
     * This approach also enables support for "async mode" where local
     * task processing is in FIFO, not LIFO order; simply by using a
     * version of deq rather than pop when locallyFifo is true (as set
     * by the ForkJoinPool).  This allows use in message-passing
     * frameworks in which tasks are never joined.  However neither
     * mode considers affinities, loads, cache localities, etc, so
     * rarely provide the best possible performance on a given
     * machine, but portably provide good throughput by averaging over
     * these factors.  (Further, even if we did try to use such
     * information, we do not usually have a basis for exploiting
     * it. For example, some sets of tasks profit from cache
     * affinities, but others are harmed by cache pollution effects.)
     *
     * When a worker would otherwise be blocked waiting to join a
     * task, it first tries a form of linear helping: Each worker
     * records (in field currentSteal) the most recent task it stole
     * from some other worker. Plus, it records (in field currentJoin)
     * the task it is currently actively joining. Method joinTask uses
     * these markers to try to find a worker to help (i.e., steal back
     * a task from and execute it) that could hasten completion of the
     * actively joined task. In essence, the joiner executes a task
     * that would be on its own local deque had the to-be-joined task
     * not been stolen. This may be seen as a conservative variant of
     * the approach in Wagner & Calder "Leapfrogging: a portable
     * technique for implementing efficient futures" SIGPLAN Notices,
     * 1993 (http://portal.acm.org/citation.cfm?id=155354). It differs
     * in that: (1) We only maintain dependency links across workers
     * upon steals, rather than use per-task bookkeeping.  This may
     * require a linear scan of workers array to locate stealers, but
     * usually doesn't because stealers leave hints (that may become
     * stale/wrong) of where to locate them. This isolates cost to
     * when it is needed, rather than adding to per-task overhead.
     * (2) It is "shallow", ignoring nesting and potentially cyclic
     * mutual steals.  (3) It is intentionally racy: field currentJoin
     * is updated only while actively joining, which means that we
     * miss links in the chain during long-lived tasks, GC stalls etc
     * (which is OK since blocking in such cases is usually a good
     * idea).  (4) We bound the number of attempts to find work (see
     * MAX_HELP) and fall back to suspending the worker and if
     * necessary replacing it with another.
     *
     * Efficient implementation of these algorithms currently relies
     * on an uncomfortable amount of "Unsafe" mechanics. To maintain
     * correct orderings, reads and writes of variable queueBase
     * require volatile ordering.  Variable queueTop need not be
     * volatile because non-local reads always follow those of
     * queueBase.  Similarly, because they are protected by volatile
     * queueBase reads, reads of the queue array and its slots by
     * other threads do not need volatile load semantics, but writes
     * (in push) require store order and CASes (in pop and deq)
     * require (volatile) CAS semantics.  (Michael, Saraswat, and
     * Vechev's algorithm has similar properties, but without support
     * for nulling slots.)  Since these combinations aren't supported
     * using ordinary volatiles, the only way to accomplish these
     * efficiently is to use direct Unsafe calls. (Using external
     * AtomicIntegers and AtomicReferenceArrays for the indices and
     * array is significantly slower because of memory locality and
     * indirection effects.)
     *
     * Further, performance on most platforms is very sensitive to
     * placement and sizing of the (resizable) queue array.  Even
     * though these queues don't usually become all that big, the
     * initial size must be large enough to counteract cache
     * contention effects across multiple queues (especially in the
     * presence of GC cardmarking). Also, to improve thread-locality,
     * queues are initialized after starting.
     */

    /**
     * Creates a ForkJoinWorkerThread operating in the given pool.
     *
     * @param pool the pool this thread works in
     * @throws NullPointerException if pool is null
     */
    protected ForkJoinWorkerThread(ForkJoinPool pool) {
        super(pool.nextWorkerName());

    }

    // Public methods

    /**
     * Returns the pool hosting this thread.
     *
     * @return the pool
     */
    public ForkJoinPool getPool() {
        return null;
    }

    /**
     * Returns the index number of this thread in its pool.  The
     * returned value ranges from zero to the maximum number of
     * threads (minus one) that have ever been created in the pool.
     * This method may be useful for applications that track status or
     * collect results per-worker rather than per-task.
     *
     * @return the index number
     */
    public int getPoolIndex() {
        return 0;
    }

    // Randomization



    // Run State management

    /**
     * Initializes internal state after construction but before
     * processing any tasks. If you override this method, you must
     * invoke {@code super.onStart()} at the beginning of the method.
     * Initialization requires care: Most fields must have legal
     * default values, to ensure that attempted accesses from other
     * threads work correctly even before this thread starts
     * processing tasks.
     */
    protected void onStart() {
    }

    /**
     * Performs cleanup associated with termination of this worker
     * thread.  If you override this method, you must invoke
     * {@code super.onTermination} at the end of the overridden method.
     *
     * @param exception the exception causing this thread to abort due
     * to an unrecoverable error, or {@code null} if completed normally
     */
    protected void onTermination(Throwable exception) {

    }

    /**
     * This method is required to be public, but should never be
     * called explicitly. It performs the main run loop to execute
     * {@link ForkJoinTask}s.
     */
    public void run() {

    }

    /*
     * Intrinsics-based atomic writes for queue slots. These are
     * basically the same as methods in AtomicReferenceArray, but
     * specialized for (1) ForkJoinTask elements (2) requirement that
     * nullness and bounds checks have already been performed by
     * callers and (3) effective offsets are known not to overflow
     * from int to long (because of MAXIMUM_QUEUE_CAPACITY). We don't
     * need corresponding version for reads: plain array reads are OK
     * because they are protected by other volatile reads and are
     * confirmed by CASes.
     *
     * Most uses don't actually call these methods, but instead
     * contain inlined forms that enable more predictable
     * optimization.  We don't define the version of write used in
     * pushTask at all, but instead inline there a store-fenced array
     * slot write.
     *
     * Also in most methods, as a performance (not correctness) issue,
     * we'd like to encourage compilers not to arbitrarily postpone
     * setting queueTop after writing slot.  Currently there is no
     * intrinsic for arranging this, but using Unsafe putOrderedInt
     * may be a preferable strategy on some compilers even though its
     * main effect is a pre-, not post- fence. To simplify possible
     * changes, the option is left in comments next to the associated
     * assignments.
     */

    /**
     * CASes slot i of array q from t to null. Caller must ensure q is
     * non-null and index is in range.
     */
    private static final boolean casSlotNull(ForkJoinTask<?>[] q, int i,
                                             ForkJoinTask<?> t) {
        return false;
    }

    /**
     * Performs a volatile write of the given task at given slot of
     * array q.  Caller must ensure q is non-null and index is in
     * range. This method is used only during resets and backouts.
     */
    private static final void writeSlot(ForkJoinTask<?>[] q, int i,
                                        ForkJoinTask<?> t) {
    }

    // queue methods

    /**
     * Pushes a task. Call only from this thread.
     *
     * @param t the task. Caller must ensure non-null.
     */
    final void pushTask(ForkJoinTask<?> t) {

    }

    /**
     * Tries to take a task from the base of the queue, failing if
     * empty or contended. Note: Specializations of this code appear
     * in locallyDeqTask and elsewhere.
     *
     * @return a task, or null if none or contended
     */
    final ForkJoinTask<?> deqTask() {

        return null;
    }

    /**
     * Tries to take a task from the base of own queue.  Called only
     * by this thread.
     *
     * @return a task, or null if none
     */
    final ForkJoinTask<?> locallyDeqTask() {

        return null;
    }

    /**
     * Specialized version of popTask to pop only if topmost element
     * is the given task. Called only by this thread.
     *
     * @param t the task. Caller must ensure non-null.
     */
    final boolean unpushTask(ForkJoinTask<?> t) {

        return false;
    }

    /**
     * Returns next task, or null if empty or contended.
     */
    final ForkJoinTask<?> peekTask() {
        return null;
    }

    // Support methods for ForkJoinPool

    /**
     * Runs the given task, plus any local tasks until queue is empty
     */
    final void execTask(ForkJoinTask<?> t) {

    }

    /**
     * Removes and cancels all tasks in queue.  Can be called from any
     * thread.
     */
    final void cancelTasks() {

    }

    /**
     * Drains tasks to given collection c.
     *
     * @return the number of tasks drained
     */
    final int drainTasksTo(Collection<? super ForkJoinTask<?>> c) {
        return 0;
    }

    // Support methods for ForkJoinTask

    /**
     * Returns an estimate of the number of tasks in the queue.
     */
    final int getQueueSize() {
        return 0;
    }

    /**
     * Gets and removes a local task.
     *
     * @return a task, if available
     */
    final ForkJoinTask<?> pollLocalTask() {
        return null;
    }

    /**
     * Gets and removes a local or stolen task.
     *
     * @return a task, if available
     */
    final ForkJoinTask<?> pollTask() {

        return null;
    }

    /**
     * The maximum stolen->joining link depth allowed in helpJoinTask,
     * as well as the maximum number of retries (allowing on average
     * one staleness retry per level) per attempt to instead try
     * compensation.  Depths for legitimate chains are unbounded, but
     * we use a fixed constant to avoid (otherwise unchecked) cycles
     * and bound staleness of traversal parameters at the expense of
     * sometimes blocking when we could be helping.
     */
    private static final int MAX_HELP = 16;

    /**
     * Possibly runs some tasks and/or blocks, until joinMe is done.
     *
     * @param joinMe the task to join
     * @return completion status on exit
     */
    final int joinTask(ForkJoinTask<?> joinMe) {
        return 0;
    }




    /**
     * Implements ForkJoinTask.getSurplusQueuedTaskCount().  Returns
     * an estimate of the number of tasks, offset by a function of
     * number of idle workers.
     *
     * This method provides a cheap heuristic guide for task
     * partitioning when programmers, frameworks, tools, or languages
     * have little or no idea about task granularity.  In essence by
     * offering this method, we ask users only about tradeoffs in
     * overhead vs expected throughput and its variance, rather than
     * how finely to partition tasks.
     *
     * In a steady state strict (tree-structured) computation, each
     * thread makes available for stealing enough tasks for other
     * threads to remain active. Inductively, if all threads play by
     * the same rules, each thread should make available only a
     * constant number of tasks.
     *
     * The minimum useful constant is just 1. But using a value of 1
     * would require immediate replenishment upon each steal to
     * maintain enough tasks, which is infeasible.  Further,
     * partitionings/granularities of offered tasks should minimize
     * steal rates, which in general means that threads nearer the top
     * of computation tree should generate more than those nearer the
     * bottom. In perfect steady state, each thread is at
     * approximately the same level of computation tree. However,
     * producing extra tasks amortizes the uncertainty of progress and
     * diffusion assumptions.
     *
     * So, users will want to use values larger, but not much larger
     * than 1 to both smooth over transient shortages and hedge
     * against uneven progress; as traded off against the cost of
     * extra task overhead. We leave the user to pick a threshold
     * value to compare with the results of this call to guide
     * decisions, but recommend values such as 3.
     *
     * When all threads are active, it is on average OK to estimate
     * surplus strictly locally. In steady-state, if one thread is
     * maintaining say 2 surplus tasks, then so are others. So we can
     * just use estimated queue length (although note that (queueTop -
     * queueBase) can be an overestimate because of stealers lagging
     * increments of queueBase).  However, this strategy alone leads
     * to serious mis-estimates in some non-steady-state conditions
     * (ramp-up, ramp-down, other stalls). We can detect many of these
     * by further considering the number of "idle" threads, that are
     * known to have zero queued tasks, so compensate by a factor of
     * (#idle/#active) threads.
     */
    final int getEstimatedSurplusTaskCount() {
        return 0;
    }

    /**
     * Runs tasks until {@code pool.isQuiescent()}. We piggyback on
     * pool's active count ctl maintenance, but rather than blocking
     * when tasks cannot be found, we rescan until all others cannot
     * find tasks either. The bracketing by pool quiescerCounts
     * updates suppresses pool auto-shutdown mechanics that could
     * otherwise prematurely terminate the pool because all threads
     * appear to be inactive.
     */
    final void helpQuiescePool() {

    }


}
