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Package Contents
Parallelizing Simple Loops
Parallelizing Complex Loops
Parallelizing Data Flow and Dependence Graphs
Work Isolation
Exceptions and Cancellation
Containers
Mutual Exclusion
Timing
Memory Allocation
The Task Scheduler
Design Patterns
Migrating from Threading Building Blocks (TBB)
Constrained APIs
Appendix A Costs of Time Slicing
Appendix B Mixing With Other Threading Packages
References
oneapi::tbb::info namespace
parallel_for_each Body semantics and requirements
parallel_sort ranges interface extension
Type-specified message keys for join_node
Scalable Memory Pools
Helper Functions for Expressing Graphs
concurrent_lru_cache
task_arena::constraints extensions
oneapi::tbb::info namespace extensions
task_group extensions
The customizing mutex type for concurrent_hash_map
Visible to Intel only — GUID: GUID-BF8FF4CD-3C1E-412C-8F90-5549DDB7DBF3
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Local Serializer
Context
Consider an interactive program. To maximize concurrency and responsiveness, operations requested by the user can be implemented as tasks. The order of operations can be important. For example, suppose the program presents editable text to the user. There might be operations to select text and delete selected text. Reversing the order of “select” and “delete” operations on the same buffer would be bad. However, commuting operations on different buffers might be okay. Hence the goal is to establish serial ordering of tasks associated with a given object, but not constrain ordering of tasks between different objects.
Forces
Operations associated with a certain object must be performed in serial order.
Serializing with a lock would be wasteful because threads would be waiting at the lock when they could be doing useful work elsewhere.
Solution
Sequence the work items using a FIFO (first-in first-out structure). Always keep an item in flight if possible. If no item is in flight when a work item appears, put the item in flight. Otherwise, push the item onto the FIFO. When the current item in flight completes, pop another item from the FIFO and put it in flight.
The logic can be implemented without mutexes, by using concurrent_queue for the FIFO and atomic<int> to count the number of items waiting and in flight. The example explains the accounting in detail.
Example
The following example builds on the Non-Preemptive Priorities example to implement local serialization in addition to priorities. It implements three priority levels and local serializers. The user interface for it follows:
enum Priority {
P_High,
P_Medium,
P_Low
};
template<typename Func>
void EnqueueWork( Priority p, Func f, Serializer* s=NULL );
Template function EnqueueWork causes functor f to run when the three constraints in the following table are met.
Constraint |
Resolved by class… |
|---|---|
Any prior work for the Serializer has completed. |
Serializer |
A thread is available. |
RunWorkItem |
No higher priority work is ready to run. |
ReadyPileType |
Constraints on a given functor are resolved from top to bottom in the table. The first constraint does not exist when s is NULL. The implementation of EnqueueWork packages the functor in a SerializedWorkItem and routes it to the class that enforces the first relevant constraint between pieces of work.
template<typename Func>
void EnqueueWork( Priority p, Func f, Serializer* s=NULL ) {
WorkItem* item = new SerializedWorkItem<Func>( p, f, s );
if( s )
s->add(item);
else
ReadyPile.add(item);
}
A SerializedWorkItem is derived from a WorkItem, which serves as a way to pass around a prioritized piece of work without knowing further details of the work.
// Abstract base class for a prioritized piece of work.
class WorkItem {
public:
WorkItem( Priority p ) : priority(p) {}
// Derived class defines the actual work.
virtual void run() = 0;
const Priority priority;
};
template<typename Func>
class SerializedWorkItem: public WorkItem {
Serializer* serializer;
Func f;
/*override*/ void run() {
f();
Serializer* s = serializer;
// Destroy f before running Serializer’s next functor.
delete this;
if( s )
s->noteCompletion();
}
public:
SerializedWorkItem( Priority p, const Func& f_, Serializer* s ) :
WorkItem(p), serializer(s), f(f_)
{}
};
Base class WorkItem is the same as class WorkItem in the example for Non-Preemptive Priorities. The notion of serial constraints is completely hidden from the base class, thus permitting the framework to extend other kinds of constraints or lack of constraints. Class SerializedWorkItem is essentially ConcreteWorkItem from the example for Non-Preemptive Priorities, extended with a Serializer aspect.
Virtual method run() is invoked when it becomes time to run the functor. It performs three steps:
Run the functor.
Destroy the functor.
Notify the Serializer that the functor completed, and thus unconstraining the next waiting functor.
Step 3 is the difference from the operation of ConcreteWorkItem::run. Step 2 could be done after step 3 in some contexts to increase concurrency slightly. However, the presented order is recommended because if step 2 takes non-trivial time, it likely has side effects that should complete before the next functor runs.
Class Serializer implements the core of the Local Serializer pattern:
class Serializer {
oneapi::tbb::concurrent_queue<WorkItem*> queue;
std::atomic<int> count; // Count of queued items and in-flight item
void moveOneItemToReadyPile() { // Transfer item from queue to ReadyPile
WorkItem* item;
queue.try_pop(item);
ReadyPile.add(item);
}
public:
void add( WorkItem* item ) {
queue.push(item);
if( ++count==1 )
moveOneItemToReadyPile();
}
void noteCompletion() { // Called when WorkItem completes.
if( --count!=0 )
moveOneItemToReadyPile();
}
};
The class maintains two members:
A queue of WorkItem waiting for prior work to complete.
A count of queued or in-flight work.
Mutexes are avoided by using concurrent_queue<WorkItem*> and atomic<int> along with careful ordering of operations. The transitions of count are the key understanding how class Serializer works.
If method add increments count from 0 to 1, this indicates that no other work is in flight and thus the work should be moved to the ReadyPile.
If method noteCompletion decrements count and it is not from 1 to 0, then the queue is non-empty and another item in the queue should be moved to ReadyPile.
Class ReadyPile is explained in the example for Non-Preemptive Priorities.
If priorities are not necessary, there are two variations on method moveOneItemToReadyPile, with different implications.
Method moveOneItemToReadyPile could directly invokeitem->run(). This approach has relatively low overhead and high thread locality for a given Serializer. But it is unfair. If the Serializer has a continual stream of tasks, the thread operating on it will keep servicing those tasks to the exclusion of others.
Method moveOneItemToReadyPile could invoke task::enqueue to enqueue a task that invokes item->run(). Doing so introduces higher overhead and less locality than the first approach, but avoids starvation.
The conflict between fairness and maximum locality is fundamental. The best resolution depends upon circumstance.
The pattern generalizes to constraints on work items more general than those maintained by class Serializer. A generalized Serializer::add determines if a work item is unconstrained, and if so, runs it immediately. A generalized Serializer::noteCompletion runs all previously constrained items that have become unconstrained by the completion of the current work item. The term “run” means to run work immediately, or if there are more constraints, forwarding the work to the next constraint resolver.