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ForkJoinPool.java
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ForkJoinPool.java
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/*
* 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
* or visit www.oracle.com if you need additional information or have any
* questions.
*/
/*
* 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 java.lang.Thread.UncaughtExceptionHandler;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collection;
import java.util.Collections;
import java.util.List;
import java.util.concurrent.AbstractExecutorService;
import java.util.concurrent.Callable;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Future;
import java.util.concurrent.RejectedExecutionException;
import java.util.concurrent.RunnableFuture;
import java.util.concurrent.ThreadLocalRandom;
import java.util.concurrent.TimeUnit;
/**
* An {@link ExecutorService} for running {@link ForkJoinTask}s.
* A {@code ForkJoinPool} provides the entry point for submissions
* from non-{@code ForkJoinTask} clients, as well as management and
* monitoring operations.
*
* <p>A {@code ForkJoinPool} differs from other kinds of {@link
* ExecutorService} mainly by virtue of employing
* <em>work-stealing</em>: all threads in the pool attempt to find and
* execute tasks submitted to the pool and/or created by other active
* tasks (eventually blocking waiting for work if none exist). This
* enables efficient processing when most tasks spawn other subtasks
* (as do most {@code ForkJoinTask}s), as well as when many small
* tasks are submitted to the pool from external clients. Especially
* when setting <em>asyncMode</em> to true in constructors, {@code
* ForkJoinPool}s may also be appropriate for use with event-style
* tasks that are never joined.
*
* <p>A static {@link #commonPool()} is available and appropriate for
* most applications. The common pool is used by any ForkJoinTask that
* is not explicitly submitted to a specified pool. Using the common
* pool normally reduces resource usage (its threads are slowly
* reclaimed during periods of non-use, and reinstated upon subsequent
* use).
*
* <p>For applications that require separate or custom pools, a {@code
* ForkJoinPool} may be constructed with a given target parallelism
* level; by default, equal to the number of available processors. The
* pool attempts to maintain enough active (or available) threads by
* dynamically adding, suspending, or resuming internal worker
* threads, even if some tasks are stalled waiting to join others.
* However, no such adjustments are guaranteed in the face of blocked
* I/O or other unmanaged synchronization. The nested {@link
* ManagedBlocker} interface enables extension of the kinds of
* synchronization accommodated.
*
* <p>In addition to execution and lifecycle control methods, this
* class provides status check methods (for example
* {@link #getStealCount}) that are intended to aid in developing,
* tuning, and monitoring fork/join applications. Also, method
* {@link #toString} returns indications of pool state in a
* convenient form for informal monitoring.
*
* <p>As is the case with other ExecutorServices, there are three
* main task execution methods summarized in the following table.
* These are designed to be used primarily by clients not already
* engaged in fork/join computations in the current pool. The main
* forms of these methods accept instances of {@code ForkJoinTask},
* but overloaded forms also allow mixed execution of plain {@code
* Runnable}- or {@code Callable}- based activities as well. However,
* tasks that are already executing in a pool should normally instead
* use the within-computation forms listed in the table unless using
* async event-style tasks that are not usually joined, in which case
* there is little difference among choice of methods.
*
* <table BORDER CELLPADDING=3 CELLSPACING=1>
* <caption>Summary of task execution methods</caption>
* <tr>
* <td></td>
* <td ALIGN=CENTER> <b>Call from non-fork/join clients</b></td>
* <td ALIGN=CENTER> <b>Call from within fork/join computations</b></td>
* </tr>
* <tr>
* <td> <b>Arrange async execution</b></td>
* <td> {@link #execute(ForkJoinTask)}</td>
* <td> {@link ForkJoinTask#fork}</td>
* </tr>
* <tr>
* <td> <b>Await and obtain result</b></td>
* <td> {@link #invoke(ForkJoinTask)}</td>
* <td> {@link ForkJoinTask#invoke}</td>
* </tr>
* <tr>
* <td> <b>Arrange exec and obtain Future</b></td>
* <td> {@link #submit(ForkJoinTask)}</td>
* <td> {@link ForkJoinTask#fork} (ForkJoinTasks <em>are</em> Futures)</td>
* </tr>
* </table>
*
* <p>The common pool is by default constructed with default
* parameters, but these may be controlled by setting three
* {@linkplain System#getProperty system properties}:
* <ul>
* <li>{@code java.util.concurrent.ForkJoinPool.common.parallelism}
* - the parallelism level, a non-negative integer
* <li>{@code java.util.concurrent.ForkJoinPool.common.threadFactory}
* - the class name of a {@link ForkJoinWorkerThreadFactory}
* <li>{@code java.util.concurrent.ForkJoinPool.common.exceptionHandler}
* - the class name of a {@link UncaughtExceptionHandler}
* </ul>
* The system class loader is used to load these classes.
* Upon any error in establishing these settings, default parameters
* are used. It is possible to disable or limit the use of threads in
* the common pool by setting the parallelism property to zero, and/or
* using a factory that may return {@code null}.
*
* <p><b>Implementation notes</b>: This implementation restricts the
* maximum number of running threads to 32767. Attempts to create
* pools with greater than the maximum number result in
* {@code IllegalArgumentException}.
*
* <p>This implementation rejects submitted tasks (that is, by throwing
* {@link RejectedExecutionException}) only when the pool is shut down
* or internal resources have been exhausted.
*
* @since 1.7
* @author Doug Lea
*/
@sun.misc.Contended
public class ForkJoinPool extends AbstractExecutorService {
/*
* Implementation Overview
*
* This class and its nested classes provide the main
* functionality and control for a set of worker threads:
* Submissions from non-FJ threads enter into submission queues.
* Workers take these tasks and typically split them into subtasks
* that may be stolen by other workers. Preference rules give
* first priority to processing tasks from their own queues (LIFO
* or FIFO, depending on mode), then to randomized FIFO steals of
* tasks in other queues.
*
* WorkQueues
* ==========
*
* Most operations occur within work-stealing queues (in nested
* class WorkQueue). These are special forms of Deques that
* support only three of the four possible end-operations -- push,
* pop, and poll (aka steal), under the further constraints that
* push and pop are called only from the owning thread (or, as
* extended here, under a lock), while poll 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).
* See also "Correct and Efficient Work-Stealing for Weak Memory
* Models" by Le, Pop, Cohen, and Nardelli, PPoPP 2013
* (http://www.di.ens.fr/~zappa/readings/ppopp13.pdf) for an
* analysis of memory ordering (atomic, volatile etc) issues. 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 poll (steal) from being on the indices ("base" and "top") to
* the slots themselves. So, both a successful pop and poll
* 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 base
* or top. 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 top
* == base means the queue is empty, but otherwise may err on the
* side of possibly making the queue appear nonempty when a push,
* pop, or poll have not fully committed. Note that this means
* that the poll 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 poll or new push on any empty queue to
* complete. (This is why we normally use method pollAt and its
* variants that try once at the apparent base index, else
* consider alternative actions, rather than method poll.)
*
* This approach also enables support of a user mode in which local
* task processing is in FIFO, not LIFO order, simply by using
* poll rather than pop. This can be useful 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.)
*
* WorkQueues are also used in a similar way for tasks submitted
* to the pool. We cannot mix these tasks in the same queues used
* for work-stealing (this would contaminate lifo/fifo
* processing). Instead, we randomly associate submission queues
* with submitting threads, using a form of hashing. The
* ThreadLocalRandom probe value serves as a hash code for
* choosing existing queues, and may be randomly repositioned upon
* contention with other submitters. In essence, submitters act
* like workers except that they are restricted to executing local
* tasks that they submitted (or in the case of CountedCompleters,
* others with the same root task). However, because most
* shared/external queue operations are more expensive than
* internal, and because, at steady state, external submitters
* will compete for CPU with workers, ForkJoinTask.join and
* related methods disable them from repeatedly helping to process
* tasks if all workers are active. Insertion of tasks in shared
* mode requires a lock (mainly to protect in the case of
* resizing) but we use only a simple spinlock (using bits in
* field qlock), because submitters encountering a busy queue move
* on to try or create other queues -- they block only when
* creating and registering new queues.
*
* Management
* ==========
*
* The main throughput advantages of work-stealing stem from
* decentralized control -- workers mostly take tasks from
* themselves or each other. We cannot negate this in the
* implementation of other management responsibilities. The main
* tactic for avoiding bottlenecks is packing nearly all
* essentially atomic control state into two volatile variables
* that are by far most often read (not written) as status and
* consistency checks.
*
* Field "ctl" contains 64 bits holding all the information needed
* to atomically decide to add, inactivate, enqueue (on an event
* queue), dequeue, and/or re-activate workers. To enable this
* packing, we restrict maximum parallelism to (1<<15)-1 (which is
* far in excess of normal operating range) to allow ids, counts,
* and their negations (used for thresholding) to fit into 16bit
* fields.
*
* Field "plock" is a form of sequence lock with a saturating
* shutdown bit (similarly for per-queue "qlocks"), mainly
* protecting updates to the workQueues array, as well as to
* enable shutdown. When used as a lock, it is normally only very
* briefly held, so is nearly always available after at most a
* brief spin, but we use a monitor-based backup strategy to
* block when needed.
*
* Recording WorkQueues. WorkQueues are recorded in the
* "workQueues" array that is created upon first use and expanded
* if necessary. Updates to the array while recording new workers
* and unrecording terminated ones are protected from each other
* by a lock but the array is otherwise concurrently readable, and
* accessed directly. To simplify index-based operations, the
* array size is always a power of two, and all readers must
* tolerate null slots. Worker queues are at odd indices. Shared
* (submission) queues are at even indices, up to a maximum of 64
* slots, to limit growth even if array needs to expand to add
* more workers. Grouping them together in this way simplifies and
* speeds up task scanning.
*
* All worker thread creation is on-demand, triggered by task
* submissions, replacement of terminated workers, and/or
* compensation for blocked workers. However, all other support
* code is set up to work with other policies. To ensure that we
* do not hold on to worker references that would prevent GC, ALL
* accesses to workQueues are via indices into the workQueues
* array (which is one source of some of the messy code
* constructions here). In essence, the workQueues array serves as
* a weak reference mechanism. Thus for example the wait queue
* field of ctl stores indices, not references. Access to the
* workQueues in associated methods (for example signalWork) must
* both index-check and null-check the IDs. All such accesses
* ignore bad IDs by returning out early from what they are doing,
* since this can only be associated with termination, in which
* case it is OK to give up. All uses of the workQueues array
* also check that it is non-null (even if previously
* non-null). This allows nulling during termination, which is
* currently not necessary, but remains an option for
* resource-revocation-based shutdown schemes. It also helps
* reduce JIT issuance of uncommon-trap code, which tends to
* unnecessarily complicate control flow in some methods.
*
* Event Queuing. Unlike HPC work-stealing frameworks, we cannot
* let workers spin indefinitely scanning for tasks when none can
* be found immediately, and we cannot start/resume workers unless
* there appear to be tasks available. On the other hand, we must
* quickly prod them into action when new tasks are submitted or
* generated. In many usages, ramp-up time to activate workers is
* the main limiting factor in overall performance (this is
* compounded at program start-up by JIT compilation and
* allocation). So we try to streamline this as much as possible.
* We park/unpark workers after placing in an event wait queue
* when they cannot find work. This "queue" is actually a simple
* Treiber stack, headed by the "id" field of ctl, plus a 15bit
* counter value (that reflects the number of times a worker has
* been inactivated) to avoid ABA effects (we need only as many
* version numbers as worker threads). Successors are held in
* field WorkQueue.nextWait. Queuing deals with several intrinsic
* races, mainly that a task-producing thread can miss seeing (and
* signalling) another thread that gave up looking for work but
* has not yet entered the wait queue. We solve this by requiring
* a full sweep of all workers (via repeated calls to method
* scan()) both before and after a newly waiting worker is added
* to the wait queue. Because enqueued workers may actually be
* rescanning rather than waiting, we set and clear the "parker"
* field of WorkQueues to reduce unnecessary calls to unpark.
* (This requires a secondary recheck to avoid missed signals.)
* Note the unusual conventions about Thread.interrupts
* surrounding parking and other blocking: Because interrupts are
* used solely to alert threads to check termination, which is
* checked anyway upon blocking, we clear status (using
* Thread.interrupted) before any call to park, so that park does
* not immediately return due to status being set via some other
* unrelated call to interrupt in user code.
*
* Signalling. We create or wake up workers only when there
* appears to be at least one task they might be able to find and
* execute. When a submission is added or another worker adds a
* task to a queue that has fewer than two tasks, they signal
* waiting workers (or trigger creation of new ones if fewer than
* the given parallelism level -- signalWork). These primary
* signals are buttressed by others whenever other threads remove
* a task from a queue and notice that there are other tasks there
* as well. So in general, pools will be over-signalled. On most
* platforms, signalling (unpark) overhead time is noticeably
* long, and the time between signalling a thread and it actually
* making progress can be very noticeably long, so it is worth
* offloading these delays from critical paths as much as
* possible. Additionally, workers spin-down gradually, by staying
* alive so long as they see the ctl state changing. Similar
* stability-sensing techniques are also used before blocking in
* awaitJoin and helpComplete.
*
* Trimming workers. To release resources after periods of lack of
* use, a worker starting to wait when the pool is quiescent will
* time out and terminate if the pool has remained quiescent for a
* given period -- a short period if there are more threads than
* parallelism, longer as the number of threads decreases. This
* will slowly propagate, eventually terminating all workers after
* periods of non-use.
*
* Shutdown and Termination. A call to shutdownNow atomically sets
* a plock bit and then (non-atomically) sets each worker's
* qlock status, cancels all unprocessed tasks, and wakes up
* all waiting workers. Detecting whether termination should
* commence after a non-abrupt shutdown() call requires more work
* and bookkeeping. We need consensus about quiescence (i.e., that
* there is no more work). The active count provides a primary
* indication but non-abrupt shutdown still requires a rechecking
* scan for any workers that are inactive but not queued.
*
* Joining Tasks
* =============
*
* Any of several actions may be taken when one worker is waiting
* to join a task stolen (or always held) by another. Because we
* are multiplexing many tasks on to a pool of workers, we can't
* just let them block (as in Thread.join). We also cannot just
* reassign the joiner's run-time stack with another and replace
* it later, which would be a form of "continuation", that even if
* possible is not necessarily a good idea since we sometimes need
* both an unblocked task and its continuation to progress.
* Instead we combine two tactics:
*
* Helping: Arranging for the joiner to execute some task that it
* would be running if the steal had not occurred.
*
* Compensating: Unless there are already enough live threads,
* method tryCompensate() may create or re-activate a spare
* thread to compensate for blocked joiners until they unblock.
*
* A third form (implemented in tryRemoveAndExec) amounts to
* helping a hypothetical compensator: If we can readily tell that
* a possible action of a compensator is to steal and execute the
* task being joined, the joining thread can do so directly,
* without the need for a compensation thread (although at the
* expense of larger run-time stacks, but the tradeoff is
* typically worthwhile).
*
* The ManagedBlocker extension API can't use helping so relies
* only on compensation in method awaitBlocker.
*
* The algorithm in tryHelpStealer entails 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 tryHelpStealer 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 sometimes
* requires a linear scan of workQueues array to locate stealers,
* but often doesn't because stealers leave hints (that may become
* stale/wrong) of where to locate them. It is only a hint
* because a worker might have had multiple steals and the hint
* records only one of them (usually the most current). Hinting
* 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.
*
* Helping actions for CountedCompleters are much simpler: Method
* helpComplete can take and execute any task with the same root
* as the task being waited on. However, this still entails some
* traversal of completer chains, so is less efficient than using
* CountedCompleters without explicit joins.
*
* It is impossible to keep exactly the target parallelism number
* of threads running at any given time. Determining the
* existence of conservatively safe helping targets, the
* availability of already-created spares, and the apparent need
* to create new spares are all racy, so we rely on multiple
* retries of each. Compensation in the apparent absence of
* helping opportunities is challenging to control on JVMs, where
* GC and other activities can stall progress of tasks that in
* turn stall out many other dependent tasks, without us being
* able to determine whether they will ever require compensation.
* Even though work-stealing otherwise encounters little
* degradation in the presence of more threads than cores,
* aggressively adding new threads in such cases entails risk of
* unwanted positive feedback control loops in which more threads
* cause more dependent stalls (as well as delayed progress of
* unblocked threads to the point that we know they are available)
* leading to more situations requiring more threads, and so
* on. This aspect of control can be seen as an (analytically
* intractable) game with an opponent that may choose the worst
* (for us) active thread to stall at any time. We take several
* precautions to bound losses (and thus bound gains), mainly in
* methods tryCompensate and awaitJoin.
*
* Common Pool
* ===========
*
* The static common pool always exists after static
* initialization. Since it (or any other created pool) need
* never be used, we minimize initial construction overhead and
* footprint to the setup of about a dozen fields, with no nested
* allocation. Most bootstrapping occurs within method
* fullExternalPush during the first submission to the pool.
*
* When external threads submit to the common pool, they can
* perform subtask processing (see externalHelpJoin and related
* methods). This caller-helps policy makes it sensible to set
* common pool parallelism level to one (or more) less than the
* total number of available cores, or even zero for pure
* caller-runs. We do not need to record whether external
* submissions are to the common pool -- if not, externalHelpJoin
* returns quickly (at the most helping to signal some common pool
* workers). These submitters would otherwise be blocked waiting
* for completion, so the extra effort (with liberally sprinkled
* task status checks) in inapplicable cases amounts to an odd
* form of limited spin-wait before blocking in ForkJoinTask.join.
*
* Style notes
* ===========
*
* There is a lot of representation-level coupling among classes
* ForkJoinPool, ForkJoinWorkerThread, and ForkJoinTask. The
* fields of WorkQueue maintain data structures managed by
* ForkJoinPool, so are directly accessed. There is little point
* trying to reduce this, since any associated future changes in
* representations will need to be accompanied by algorithmic
* changes anyway. Several methods intrinsically sprawl because
* they must accumulate sets of consistent reads of volatiles held
* in local variables. Methods signalWork() and scan() are the
* main bottlenecks, so are especially heavily
* micro-optimized/mangled. There are lots of inline assignments
* (of form "while ((local = field) != 0)") which are usually the
* simplest way to ensure the required read orderings (which are
* sometimes critical). This leads to a "C"-like style of listing
* declarations of these locals at the heads of methods or blocks.
* There are several occurrences of the unusual "do {} while
* (!cas...)" which is the simplest way to force an update of a
* CAS'ed variable. There are also other coding oddities (including
* several unnecessary-looking hoisted null checks) that help
* some methods perform reasonably even when interpreted (not
* compiled).
*
* The order of declarations in this file is:
* (1) Static utility functions
* (2) Nested (static) classes
* (3) Static fields
* (4) Fields, along with constants used when unpacking some of them
* (5) Internal control methods
* (6) Callbacks and other support for ForkJoinTask methods
* (7) Exported methods
* (8) Static block initializing statics in minimally dependent order
*/
// Static utilities
/**
* If there is a security manager, makes sure caller has
* permission to modify threads.
*/
private static void checkPermission() {
SecurityManager security = System.getSecurityManager();
if (security != null)
security.checkPermission(modifyThreadPermission);
}
// Nested classes
/**
* Factory for creating new {@link ForkJoinWorkerThread}s.
* A {@code ForkJoinWorkerThreadFactory} must be defined and used
* for {@code ForkJoinWorkerThread} subclasses that extend base
* functionality or initialize threads with different contexts.
*/
public static interface ForkJoinWorkerThreadFactory {
/**
* Returns a new worker thread operating in the given pool.
*
* @param pool the pool this thread works in
* @return the new worker thread
* @throws NullPointerException if the pool is null
*/
public ForkJoinWorkerThread newThread(ForkJoinPool pool);
}
/**
* Default ForkJoinWorkerThreadFactory implementation; creates a
* new ForkJoinWorkerThread.
*/
static final class DefaultForkJoinWorkerThreadFactory
implements ForkJoinWorkerThreadFactory {
public final ForkJoinWorkerThread newThread(ForkJoinPool pool) {
return new ForkJoinWorkerThread(pool);
}
}
/**
* Class for artificial tasks that are used to replace the target
* of local joins if they are removed from an interior queue slot
* in WorkQueue.tryRemoveAndExec. We don't need the proxy to
* actually do anything beyond having a unique identity.
*/
static final class EmptyTask extends ForkJoinTask<Void> {
private static final long serialVersionUID = -7721805057305804111L;
EmptyTask() { status = ForkJoinTask.NORMAL; } // force done
public final Void getRawResult() { return null; }
public final void setRawResult(Void x) {}
public final boolean exec() { return true; }
}
/**
* Queues supporting work-stealing as well as external task
* submission. See above for main rationale and algorithms.
* Implementation relies heavily on "Unsafe" intrinsics
* and selective use of "volatile":
*
* Field "base" is the index (mod array.length) of the least valid
* queue slot, which is always the next position to steal (poll)
* from if nonempty. Reads and writes require volatile orderings
* but not CAS, because updates are only performed after slot
* CASes.
*
* Field "top" is the index (mod array.length) of the next queue
* slot to push to or pop from. It is written only by owner thread
* for push, or under lock for external/shared push, and accessed
* by other threads only after reading (volatile) base. Both top
* and base are allowed to wrap around on overflow, but (top -
* base) (or more commonly -(base - top) to force volatile read of
* base before top) still estimates size. The lock ("qlock") is
* forced to -1 on termination, causing all further lock attempts
* to fail. (Note: we don't need CAS for termination state because
* upon pool shutdown, all shared-queues will stop being used
* anyway.) Nearly all lock bodies are set up so that exceptions
* within lock bodies are "impossible" (modulo JVM errors that
* would cause failure anyway.)
*
* The array slots are read and written using the emulation of
* volatiles/atomics provided by Unsafe. Insertions must in
* general use putOrderedObject as a form of releasing store to
* ensure that all writes to the task object are ordered before
* its publication in the queue. All removals entail a CAS to
* null. The array is always a power of two. To ensure safety of
* Unsafe array operations, all accesses perform explicit null
* checks and implicit bounds checks via power-of-two masking.
*
* In addition to basic queuing support, this class contains
* fields described elsewhere to control execution. It turns out
* to work better memory-layout-wise to include them in this class
* rather than a separate class.
*
* Performance on most platforms is very sensitive to placement of
* instances of both WorkQueues and their arrays -- we absolutely
* do not want multiple WorkQueue instances or multiple queue
* arrays sharing cache lines. (It would be best for queue objects
* and their arrays to share, but there is nothing available to
* help arrange that). The @Contended annotation alerts JVMs to
* try to keep instances apart.
*/
@sun.misc.Contended
static final class WorkQueue {
/**
* Capacity of work-stealing queue array upon initialization.
* Must be a power of two; at least 4, but should be larger to
* reduce or eliminate cacheline sharing among queues.
* Currently, it is much larger, as a partial workaround for
* the fact that JVMs often place arrays in locations that
* share GC bookkeeping (especially cardmarks) such that
* per-write accesses encounter serious memory contention.
*/
static final int INITIAL_QUEUE_CAPACITY = 1 << 13;
/**
* Maximum size for queue arrays. Must be a power of two less
* than or equal to 1 << (31 - width of array entry) to ensure
* lack of wraparound of index calculations, but defined to a
* value a bit less than this to help users trap runaway
* programs before saturating systems.
*/
static final int MAXIMUM_QUEUE_CAPACITY = 1 << 26; // 64M
volatile int eventCount; // encoded inactivation count; < 0 if inactive
int nextWait; // encoded record of next event waiter
int nsteals; // number of steals
int hint; // steal index hint
short poolIndex; // index of this queue in pool
final short mode; // 0: lifo, > 0: fifo, < 0: shared
volatile int qlock; // 1: locked, -1: terminate; else 0
volatile int base; // index of next slot for poll
int top; // index of next slot for push
ForkJoinTask<?>[] array; // the elements (initially unallocated)
final ForkJoinPool pool; // the containing pool (may be null)
final ForkJoinWorkerThread owner; // owning thread or null if shared
volatile Thread parker; // == owner during call to park; else null
volatile ForkJoinTask<?> currentJoin; // task being joined in awaitJoin
ForkJoinTask<?> currentSteal; // current non-local task being executed
WorkQueue(ForkJoinPool pool, ForkJoinWorkerThread owner, int mode,
int seed) {
this.pool = pool;
this.owner = owner;
this.mode = (short)mode;
this.hint = seed; // store initial seed for runWorker
// Place indices in the center of array (that is not yet allocated)
base = top = INITIAL_QUEUE_CAPACITY >>> 1;
}
/**
* Returns the approximate number of tasks in the queue.
*/
final int queueSize() {
int n = base - top; // non-owner callers must read base first
return (n >= 0) ? 0 : -n; // ignore transient negative
}
/**
* Provides a more accurate estimate of whether this queue has
* any tasks than does queueSize, by checking whether a
* near-empty queue has at least one unclaimed task.
*/
final boolean isEmpty() {
ForkJoinTask<?>[] a; int m, s;
int n = base - (s = top);
return (n >= 0 ||
(n == -1 &&
((a = array) == null ||
(m = a.length - 1) < 0 ||
U.getObject
(a, (long)((m & (s - 1)) << ASHIFT) + ABASE) == null)));
}
/**
* Pushes a task. Call only by owner in unshared queues. (The
* shared-queue version is embedded in method externalPush.)
*
* @param task the task. Caller must ensure non-null.
* @throws RejectedExecutionException if array cannot be resized
*/
final void push(ForkJoinTask<?> task) {
ForkJoinTask<?>[] a; ForkJoinPool p;
int s = top, n;
if ((a = array) != null) { // ignore if queue removed
int m = a.length - 1;
U.putOrderedObject(a, ((m & s) << ASHIFT) + ABASE, task);
if ((n = (top = s + 1) - base) <= 2)
(p = pool).signalWork(p.workQueues, this);
else if (n >= m)
growArray();
}
}
/**
* Initializes or doubles the capacity of array. Call either
* by owner or with lock held -- it is OK for base, but not
* top, to move while resizings are in progress.
*/
final ForkJoinTask<?>[] growArray() {
ForkJoinTask<?>[] oldA = array;
int size = oldA != null ? oldA.length << 1 : INITIAL_QUEUE_CAPACITY;
if (size > MAXIMUM_QUEUE_CAPACITY)
throw new RejectedExecutionException("Queue capacity exceeded");
int oldMask, t, b;
ForkJoinTask<?>[] a = array = new ForkJoinTask<?>[size];
if (oldA != null && (oldMask = oldA.length - 1) >= 0 &&
(t = top) - (b = base) > 0) {
int mask = size - 1;
do {
ForkJoinTask<?> x;
int oldj = ((b & oldMask) << ASHIFT) + ABASE;
int j = ((b & mask) << ASHIFT) + ABASE;
x = (ForkJoinTask<?>)U.getObjectVolatile(oldA, oldj);
if (x != null &&
U.compareAndSwapObject(oldA, oldj, x, null))
U.putObjectVolatile(a, j, x);
} while (++b != t);
}
return a;
}
/**
* Takes next task, if one exists, in LIFO order. Call only
* by owner in unshared queues.
*/
final ForkJoinTask<?> pop() {
ForkJoinTask<?>[] a; ForkJoinTask<?> t; int m;
if ((a = array) != null && (m = a.length - 1) >= 0) {
for (int s; (s = top - 1) - base >= 0;) {
long j = ((m & s) << ASHIFT) + ABASE;
if ((t = (ForkJoinTask<?>)U.getObject(a, j)) == null)
break;
if (U.compareAndSwapObject(a, j, t, null)) {
top = s;
return t;
}
}
}
return null;
}
/**
* Takes a task in FIFO order if b is base of queue and a task
* can be claimed without contention. Specialized versions
* appear in ForkJoinPool methods scan and tryHelpStealer.
*/
final ForkJoinTask<?> pollAt(int b) {
ForkJoinTask<?> t; ForkJoinTask<?>[] a;
if ((a = array) != null) {
int j = (((a.length - 1) & b) << ASHIFT) + ABASE;
if ((t = (ForkJoinTask<?>)U.getObjectVolatile(a, j)) != null &&
base == b && U.compareAndSwapObject(a, j, t, null)) {
U.putOrderedInt(this, QBASE, b + 1);
return t;
}
}
return null;
}
/**
* Takes next task, if one exists, in FIFO order.
*/
final ForkJoinTask<?> poll() {
ForkJoinTask<?>[] a; int b; ForkJoinTask<?> t;
while ((b = base) - top < 0 && (a = array) != null) {
int j = (((a.length - 1) & b) << ASHIFT) + ABASE;
t = (ForkJoinTask<?>)U.getObjectVolatile(a, j);
if (t != null) {
if (U.compareAndSwapObject(a, j, t, null)) {
U.putOrderedInt(this, QBASE, b + 1);
return t;
}
}
else if (base == b) {
if (b + 1 == top)
break;
Thread.yield(); // wait for lagging update (very rare)
}
}
return null;
}
/**
* Takes next task, if one exists, in order specified by mode.
*/
final ForkJoinTask<?> nextLocalTask() {
return mode == 0 ? pop() : poll();
}
/**
* Returns next task, if one exists, in order specified by mode.
*/
final ForkJoinTask<?> peek() {
ForkJoinTask<?>[] a = array; int m;
if (a == null || (m = a.length - 1) < 0)
return null;
int i = mode == 0 ? top - 1 : base;
int j = ((i & m) << ASHIFT) + ABASE;
return (ForkJoinTask<?>)U.getObjectVolatile(a, j);
}
/**
* Pops the given task only if it is at the current top.
* (A shared version is available only via FJP.tryExternalUnpush)
*/
final boolean tryUnpush(ForkJoinTask<?> t) {
ForkJoinTask<?>[] a; int s;
if ((a = array) != null && (s = top) != base &&
U.compareAndSwapObject
(a, (((a.length - 1) & --s) << ASHIFT) + ABASE, t, null)) {
top = s;
return true;
}
return false;
}
/**
* Removes and cancels all known tasks, ignoring any exceptions.
*/
final void cancelAll() {
ForkJoinTask.cancelIgnoringExceptions(currentJoin);
ForkJoinTask.cancelIgnoringExceptions(currentSteal);
for (ForkJoinTask<?> t; (t = poll()) != null; )
ForkJoinTask.cancelIgnoringExceptions(t);
}
// Specialized execution methods
/**
* Polls and runs tasks until empty.
*/
final void pollAndExecAll() {
for (ForkJoinTask<?> t; (t = poll()) != null;)
t.doExec();
}
/**
* Executes a top-level task and any local tasks remaining
* after execution.
*/
final void runTask(ForkJoinTask<?> task) {
if ((currentSteal = task) != null) {
task.doExec();
ForkJoinTask<?>[] a = array;
int md = mode;
++nsteals;
currentSteal = null;
if (md != 0)
pollAndExecAll();
else if (a != null) {
int s, m = a.length - 1;
ForkJoinTask<?> t;
while ((s = top - 1) - base >= 0 &&
(t = (ForkJoinTask<?>)U.getAndSetObject
(a, ((m & s) << ASHIFT) + ABASE, null)) != null) {
top = s;
t.doExec();
}
}
}
}
/**
* If present, removes from queue and executes the given task,
* or any other cancelled task. Returns (true) on any CAS
* or consistency check failure so caller can retry.
*
* @return false if no progress can be made, else true
*/
final boolean tryRemoveAndExec(ForkJoinTask<?> task) {
boolean stat;
ForkJoinTask<?>[] a; int m, s, b, n;
if (task != null && (a = array) != null && (m = a.length - 1) >= 0 &&
(n = (s = top) - (b = base)) > 0) {
boolean removed = false, empty = true;
stat = true;
for (ForkJoinTask<?> t;;) { // traverse from s to b
long j = ((--s & m) << ASHIFT) + ABASE;
t = (ForkJoinTask<?>)U.getObject(a, j);
if (t == null) // inconsistent length
break;
else if (t == task) {
if (s + 1 == top) { // pop
if (!U.compareAndSwapObject(a, j, task, null))
break;
top = s;
removed = true;
}
else if (base == b) // replace with proxy
removed = U.compareAndSwapObject(a, j, task,
new EmptyTask());
break;
}
else if (t.status >= 0)
empty = false;
else if (s + 1 == top) { // pop and throw away
if (U.compareAndSwapObject(a, j, t, null))
top = s;
break;
}
if (--n == 0) {
if (!empty && base == b)
stat = false;
break;
}
}
if (removed)
task.doExec();
}
else
stat = false;
return stat;
}
/**
* Tries to poll for and execute the given task or any other
* task in its CountedCompleter computation.
*/
final boolean pollAndExecCC(CountedCompleter<?> root) {
ForkJoinTask<?>[] a; int b; Object o; CountedCompleter<?> t, r;
if ((b = base) - top < 0 && (a = array) != null) {
long j = (((a.length - 1) & b) << ASHIFT) + ABASE;
if ((o = U.getObjectVolatile(a, j)) == null)
return true; // retry
if (o instanceof CountedCompleter) {
for (t = (CountedCompleter<?>)o, r = t;;) {
if (r == root) {
if (base == b &&
U.compareAndSwapObject(a, j, t, null)) {
U.putOrderedInt(this, QBASE, b + 1);
t.doExec();
}
return true;
}
else if ((r = r.completer) == null)
break; // not part of root computation
}
}
}
return false;
}
/**
* Tries to pop and execute the given task or any other task
* in its CountedCompleter computation.
*/
final boolean externalPopAndExecCC(CountedCompleter<?> root) {
ForkJoinTask<?>[] a; int s; Object o; CountedCompleter<?> t, r;
if (base - (s = top) < 0 && (a = array) != null) {
long j = (((a.length - 1) & (s - 1)) << ASHIFT) + ABASE;
if ((o = U.getObject(a, j)) instanceof CountedCompleter) {
for (t = (CountedCompleter<?>)o, r = t;;) {
if (r == root) {
if (U.compareAndSwapInt(this, QLOCK, 0, 1)) {
if (top == s && array == a &&
U.compareAndSwapObject(a, j, t, null)) {
top = s - 1;
qlock = 0;
t.doExec();