From fec403cfc065ffdb55b77d3ed7ce539282a7476c Mon Sep 17 00:00:00 2001 From: "Boyarinov, Konstantin" Date: Thu, 30 Oct 2025 16:47:45 +0200 Subject: [PATCH 1/8] Add Tasks Aggregation RFC --- rfcs/proposed/tasks_aggregation/README.md | 140 ++++++++++++++++++ .../aggregating_group_aggregating_task.png | Bin 0 -> 24379 bytes .../assets/aggregating_group_chunking.png | Bin 0 -> 10967 bytes .../aggregating_group_consumer_thread.png | Bin 0 -> 26927 bytes .../assets/aggregating_group_first_run.png | Bin 0 -> 13819 bytes .../assets/aggregating_group_further_runs.png | Bin 0 -> 15238 bytes .../aggregating_group_process_chunks.png | Bin 0 -> 31205 bytes .../aggregating_group_producer_thread.png | Bin 0 -> 12911 bytes .../producer_consumer_consumer_threads.png | Bin 0 -> 37818 bytes .../producer_consumer_producer_thread.png | Bin 0 -> 12652 bytes 10 files changed, 140 insertions(+) create mode 100644 rfcs/proposed/tasks_aggregation/README.md create mode 100644 rfcs/proposed/tasks_aggregation/assets/aggregating_group_aggregating_task.png create mode 100644 rfcs/proposed/tasks_aggregation/assets/aggregating_group_chunking.png create mode 100644 rfcs/proposed/tasks_aggregation/assets/aggregating_group_consumer_thread.png create mode 100644 rfcs/proposed/tasks_aggregation/assets/aggregating_group_first_run.png create mode 100644 rfcs/proposed/tasks_aggregation/assets/aggregating_group_further_runs.png create mode 100644 rfcs/proposed/tasks_aggregation/assets/aggregating_group_process_chunks.png create mode 100644 rfcs/proposed/tasks_aggregation/assets/aggregating_group_producer_thread.png create mode 100644 rfcs/proposed/tasks_aggregation/assets/producer_consumer_consumer_threads.png create mode 100644 rfcs/proposed/tasks_aggregation/assets/producer_consumer_producer_thread.png diff --git a/rfcs/proposed/tasks_aggregation/README.md b/rfcs/proposed/tasks_aggregation/README.md new file mode 100644 index 0000000000..8a6b0ba787 --- /dev/null +++ b/rfcs/proposed/tasks_aggregation/README.md @@ -0,0 +1,140 @@ +# Tasks Aggregation + +This proposal describes a possible solution to the producer-consumer inefficiency that arises in the current ``task_group`` API. + +## Introduction + +Consider using a ``tbb::task_group`` in an application implementing a producer-consumer pattern where one producer thread generates work items, +and multiple consumer threads process them in parallel: + +```cpp +void producer() { + tbb::task_group tg; + + auto work_item = get_work_item(); + + while (work_item != end_of_work) { + tg.run(process{work_item}); + } + + tg.wait(); +} +``` + +The ``get_work_item()`` function generates the work item for processing, e.g., by reading an input file or stream. When there is no more work to generate, it +returns ``end_of_work``. The exact number of work items to be generated is unknown in advance, which makes using parallel algorithms like +``tbb::parallel_for`` or ``tbb::parallel_for_each`` potentially inefficient, as they require buffering work items to form an input sequence. +``tbb::task_group`` is semantically appropriate, as it immediately submits the available work for execution. + +In the current implementation, ``tbb::task_group::run`` creates a task to process the given function and spawns it by pushing it into the local task pool of +the producing thread. + + + +When consumer (worker) threads join the task arena, they must search for tasks to process. |Since the only source of tasks in the arena is the local task pool +of the producing thread, consumer threads will steal individual tasks from it. + + + +After executing the stolen tasks, consumer threads run out of work again and must steal from the same producer's local task pool. +Because the number of consumer threads can be large, stealing tasks from a single producer's task pool creates a bottleneck, resulting in significant +performance penalties and poor scalability. + +Another bottleneck is a reference counter inside the ``tbb::task_group`` that is used to register the task in the group. The reference counter is incremented when a task +is created and decremented when the task completes execution. + +To improve the scalability of the reference counter, oneTBB ADD_VERSION introduced a per-thread reference counters - each thread that creates a task registers its thread-local +counter as a leaf in the ``task_group``'s reference counter. Tasks created by a thread hold a reference in the thread-local counter, which in turn holds a single reference +in the group's reference counter. + +However, in the producer-consumer scenario, where all tasks are created by a single producer thread, this approach does not improve scalability. + +## Proposed solution + +The proposed solution combines the existing task spawning mechanism of ``task_group`` with an aggregator. For simplicity, let's refer to the API +implementing this idea as *aggregating-task-group*, which provides two methods ``run(task_body)`` and ``wait()`` - similar to the regular ``task_group``. + +Tasks in an *aggregating-task-group* are represented as nodes in a singly linked list; each task contains a pointer to the next task. + +The *aggregating-task-group* supports a thread-local singly linked list of tasks, which is initially empty(``atomic`` set to ``nullptr``). + +Each call to ``run()`` appends a task to the calling thread's list using an atomic CAS operation. + +If the inserted task is the first task in the list, ``run()`` spawns a service *aggregating-task* pointing to the list. + +The *aggregating-task* grabs the list it points to (``list_head.exchange(nullptr)``) and splits the grabbed list into chunks for processing. + +Consider the producer-consumer example from the introduction, now using *aggregating-task-group* instead of ``tbb::task_group``. + +When the *aggregating-task-group* is created, the list of the producer thread is empty. + + + +The first task submitted to the group is appended to the list, and since the list was empty, it spawns (adds to the local task pool) +the ``agg_task``, which points to the producer thread's task list. + +Aggregating tasks reserve a reference in the ``task_group``'s global counter and maintain a separate counter for tasks processing the chunks. +The lifetime of ``agg_task`` is extended until the last chunk of the grabbed list is processed. + + + +Subsequent tasks submitted to the group are appended to the head of the list without spawning additional aggregating tasks. + + + +When a consumer worker thread looks for work, the only task available for stealing is ``agg_task``. + + + +When the consumer thread executes the ``agg_task``, it grabs the task list and makes it empty again. Further calls to ``run()`` will +append the task to the empty list and spawn additional *aggregating-task*s to grab the new list. + + + +After grabbing the task list, the consumer thread splits the list into chunks of fixed size. Assume a chunk size of ``2``. +In the example above, it will create two service *chunk-task*s pointing to the sub-lists. One is spawned, while the other is bypassed. + +Chunk tasks reserve a reference in the aggregating task's reference counter and maintain another counter for individual listed tasks in the chunk. +The lifetime of a chunk task is extended until the last listed task in the chunk is executed. + + + +Another consumer thread can now steal the *chunk-task* from the first consumer thread's local task pool. + +When the consumer thread executes the *chunk-task*, it spawns all tasks except one and bypasses the last. + + + +### Alternative Approaches for Chunking + +Alternative approaches for how the *aggregating-task* should process the grabbed list can be considered. + +For example, it may follow the approach of ``tbb::parallel_for_each`` for forward iterators: it cuts a single chunk +from the list, re-spawns the *chunking-task* with the reduced list, and bypasses the *chunk-task*. + +For example, it may follow the approach of ``tbb::parallel_for_each`` for forward iterators: it cuts the single chunk +from the list, re-spawns the *chunking-task* with the reduced list and bypasses the *chunk-task*. When a worker thread +executes the stolen *chunking-task*, it cuts and bypasses the task for the next chunk and re-spawns the *chunking-task*. + +The *chunking-task* is re-spawned until the list is empty. + +## Possible APIs + +It does not make sense to completely replace ``task_group::run`` spawning mechanism with task aggregation, as this could +introduce unnecessary performance penalties in other ``task_group`` use cases (e.g., divide-and-conquer patterns), where +tasks are produced by multiple threads or generate additional tasks. + +There are several options for expressing tasks aggregation in the public API: + +1. A separate ``task_group``-like API, e.g., ``tbb::aggregating_task_group``, providing the same set + of member functions as ``tbb::task_group``. +2. Integrate it into the regular ``tbb::task_group`` API by providing an additional overload for ``task_group::run`` that + accepts a policy parameter, or a separate function in ``task_group``. +3. Introduce a separate API not connected with the task groups. One option is ``tbb::parallel_while(generator, predicate, body)``, + which executed ``body(input)`` in parallel while ``pred(input) == true``, with ``input`` generated by repeated calls to ``generator()``. + +## Open Questions + +* Which public APIshould be used to implement tasks aggregation? See the [Possible APIs section](#possible-apis) for details. +* Which chunking approach should be implemented? See the [Alternative Approaches for Chunking section](#alternative-approaches-for-chunking) for details. +* What chunk size should be chosen? diff --git a/rfcs/proposed/tasks_aggregation/assets/aggregating_group_aggregating_task.png b/rfcs/proposed/tasks_aggregation/assets/aggregating_group_aggregating_task.png new file mode 100644 index 0000000000000000000000000000000000000000..e0a478a3ccb4457cf86c0f7f341e8b3594b4dc62 GIT binary patch literal 24379 zcmdSBX*|^J|2I4$bY@LO2vH%~%D#;dMfO6r5oZgs@5>lP*=e(6Uy|J;{kpNs_eC#V?tfGK>C)5FoZ2RA z6>ZXkfy>AH-ss-G8nyr0DBkz@FMXCPFOq7;G>!@VcxZq)EmLzS)AZDg!8%tc%l3>> z%o}mH^3X`ptkzHsbJ?b`jE=+}V(MF^_nk^xhwtA%PJfSCsas#^l<#@}-lIEpv9mK^ z9hzeubn_TkC#0^QeD4kw_)oCdMLwFN*USHxuQI>qO;1;Y-wnRZCGBw6t>_D0FlcGm z&Y06dV{UJoyeDs>#VaCBPxR+6WPx~>$T$XxHH2yQ5=<|N>pmkzFy9l8Rt2N0kDwT)OdiT_}h4I|Dr-x0wJ!M8Qc9$qt4?fFz zM?!Mz-P}O`s35xs`e1E(Z-~2Iqt~DVUZe>n+u=~+ z&gWNj1rJxa>y&mnqwKNz5yk zB&h{yWP2Q;N*^I2&ihV=o<0SB$V5D3w?}(Gl>^E4Z-Xw;^_CqjAeG-bAGRC&?PoIB 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+++++++++++++++++++--- 1 file changed, 151 insertions(+), 22 deletions(-) diff --git a/rfcs/proposed/tasks_aggregation/README.md b/rfcs/proposed/tasks_aggregation/README.md index 8a6b0ba787..5268194ea1 100644 --- a/rfcs/proposed/tasks_aggregation/README.md +++ b/rfcs/proposed/tasks_aggregation/README.md @@ -4,17 +4,18 @@ This proposal describes a possible solution to the producer-consumer inefficienc ## Introduction -Consider using a ``tbb::task_group`` in an application implementing a producer-consumer pattern where one producer thread generates work items, +Consider using a ``tbb::task_group`` in an application implementing a single producer - multiple consumers pattern, where one producer thread generates work items, and multiple consumer threads process them in parallel: ```cpp -void producer() { + +void produce() { tbb::task_group tg; auto work_item = get_work_item(); while (work_item != end_of_work) { - tg.run(process{work_item}); + tg.run(process{work_item}); // Can be executed by one of the producer threads } tg.wait(); @@ -23,7 +24,8 @@ void producer() { The ``get_work_item()`` function generates the work item for processing, e.g., by reading an input file or stream. When there is no more work to generate, it returns ``end_of_work``. The exact number of work items to be generated is unknown in advance, which makes using parallel algorithms like -``tbb::parallel_for`` or ``tbb::parallel_for_each`` potentially inefficient, as they require buffering work items to form an input sequence. +``tbb::parallel_for`` or ``tbb::parallel_for_each`` potentially inefficient, as they require buffering work items to form an input sequence +that delays computations on the first-came work items. ``tbb::task_group`` is semantically appropriate, as it immediately submits the available work for execution. In the current implementation, ``tbb::task_group::run`` creates a task to process the given function and spawns it by pushing it into the local task pool of @@ -31,12 +33,12 @@ the producing thread. -When consumer (worker) threads join the task arena, they must search for tasks to process. |Since the only source of tasks in the arena is the local task pool +When consumer (worker) threads join the task arena, they must search for tasks to process. Since the only source of tasks in the arena is the local task pool of the producing thread, consumer threads will steal individual tasks from it. -After executing the stolen tasks, consumer threads run out of work again and must steal from the same producer's local task pool. +After executing the stolen task, consumer threads run out of work again and must steal from the same producer's local task pool. Because the number of consumer threads can be large, stealing tasks from a single producer's task pool creates a bottleneck, resulting in significant performance penalties and poor scalability. @@ -44,37 +46,125 @@ Another bottleneck is a reference counter inside the ``tbb::task_group`` that is is created and decremented when the task completes execution. To improve the scalability of the reference counter, oneTBB ADD_VERSION introduced a per-thread reference counters - each thread that creates a task registers its thread-local -counter as a leaf in the ``task_group``'s reference counter. Tasks created by a thread hold a reference in the thread-local counter, which in turn holds a single reference +counter as a leaf in the ``task_group``'s reference counter. Tasks created by a thread holds a reference in the thread-local counter, which in turn holds a single reference in the group's reference counter. -However, in the producer-consumer scenario, where all tasks are created by a single producer thread, this approach does not improve scalability. +However, in the scenario above all tasks are created by a producer thread, so all of them holds references in a single producer thread's counter. + +## Proposed Design + +### Resolving the Single Task Source Bottleneck + +This document proposes a task distribution mechanism combining the existing ``task_group`` spawning mechanism with the idea of the aggregator. -## Proposed solution +For simplicity, let's refer to the API implementing this idea as *aggregating-task-group* providing two +basic methods ``run(task_body)`` and ``wait()``, similar to the regular ``task_group``. -The proposed solution combines the existing task spawning mechanism of ``task_group`` with an aggregator. For simplicity, let's refer to the API -implementing this idea as *aggregating-task-group*, which provides two methods ``run(task_body)`` and ``wait()`` - similar to the regular ``task_group``. +Tasks in an *aggregating-task-group* forms a binary task tree where the first and the second layers +contains a single task and each other N-th layer contains up to 2^N tasks. -Tasks in an *aggregating-task-group* are represented as nodes in a singly linked list; each task contains a pointer to the next task. +Each tree node contains three pointers to other nodes +* Pointer to the left node on the next layer +* Pointer to the right node on the next layer +* Pointer to the parent node on the previous layer -The *aggregating-task-group* supports a thread-local singly linked list of tasks, which is initially empty(``atomic`` set to ``nullptr``). +Additionally, each node contains the number of items in the corresponding subtree. + +```mermaid +flowchart TD + task0[task0
num_items = 6] --> task1 + task1[task1
num_items = 5] --> task2 + task1 --> task3 + task2[task2
num_items = 3] --> task4[task4
num_items = 1] + task2 --> task5[task5
num_items = 1] + task3[task3
num_items = 2] --> task6[task6
num_items = 1] + task1 --> task0 + task2 --> task1 + task3 --> task1 + task4 --> task2 + task5 --> task2 + task6 --> task3 + + + linkStyle 0,1,2,3,4,5 stroke:#e74c3c,stroke-width:2px,color:#e74c3c + linkStyle 6,7,8,9,10,11 stroke:#0000FF,stroke-width:2px,color:#0000FF +``` -Each call to ``run()`` appends a task to the calling thread's list using an atomic CAS operation. +Binary tree of tasks supports three basic actions: +* Adding a task into the tree +* Splitting the tree into halves +* Grabbing all items in the tree -If the inserted task is the first task in the list, ``run()`` spawns a service *aggregating-task* pointing to the list. +The detailed description of all of these operations will be provided in separate sections below. -The *aggregating-task* grabs the list it points to (``list_head.exchange(nullptr)``) and splits the grabbed list into chunks for processing. +The *aggregating-task-group* instance supports a thread-local atomic pointer to the binary tree which is initially empty(``atomic`` set to ``nullptr``). + +Each call to ``run()`` appends a task to the calling thread's binary tree and if the tree was empty (i.e. the inserted task is the first in the tree), +spawns a service *grab-task* pointing to the tree. + +The sequence diagram for ``run`` is shown below: + +```mermaid +flowchart TD + run["run(task_body)"] --> allocate_tree_task[t = allocate task] + allocate_tree_task --> add_task["first = thread_local_tree.add_task(t)"] + add_task --> first_check[first ?] + first_check --> |Yes| allocate_grab_task[gt = allocate grab task] + first_check --> |No| exit + allocate_grab_task --> spawn_grab_task["spawn(gt)"] + spawn_grab_task --> exit +``` + +When the worker thread executes the *grab-task* stolen from the producer's local task pool, it grabs all the items in the producer's tree. + +Since the producer's tree is left empty, further call to ``run`` would append a task to the empty tree, resulting in spawning new *grab-task*s. + +After grabbing the task tree, the *grab-task* bypasses the head task of the tree. + +The bypassed head task checks the number of items in the tree, and if it exceeds the specified grainsize - splits the tree into halves. +The head task of the second half of the tree is spawned and the currently executing task (that is now head of the first half of the tree) +is re-executed, resulting in recursive splitting until the grainsize is reached. +Once the grainsize is reached, the head of the tree spawns its left and right leaf tasks if any and executes the associated user body. + +The sequence diagram for the grab and split tasks is shown below. + +```mermaid +flowchart TD + grab["grab_task(tree)"] --> grab_all["head = tree.grab_all()"] + grab_all --> bypass_head[bypass head] + bypass_head --> head_body["head body"] + head_body --> is_divisible_check[head->is_divisible ?] + is_divisible_check --> |Yes| split_head["head2 = split(head)"] + is_divisible_check --> |No| start_execute["t = head"] + split_head --> spawn_head2["spawn(head2)"] + spawn_head2 --> |Other thread| head2_body + head2_body --> head_assign[head = head2] + head_assign --> is_divisible_check + start_execute --> left_check[t->left == nullptr ?] + left_check --> |Yes| execute_t[execute t's function] + left_check --> |No| spawn_left["spawn(t->left)"] + execute_t --> exit + spawn_left --> |Other thread| left_body + spawn_left --> |This thread| right_check[t->right == nullptr ?] + right_check --> |Yes| execute_t + right_check --> |No| spawn_right["spawn(t->right)"] + spawn_right --> |Other thread| right_body + spawn_right --> |This thread| execute_t + left_body --> assign_t_left["t = t->left"] + assign_t_left --> left_check + right_body --> assign_t_right["t = t->right"] + assign_t_right --> left_check +``` Consider the producer-consumer example from the introduction, now using *aggregating-task-group* instead of ``tbb::task_group``. -When the *aggregating-task-group* is created, the list of the producer thread is empty. +When the *aggregating-task-group* is created, the task tree of the producer thread is empty. - +The first task submitted to the group is appended to the tree, and since the tree was empty, the ``grab_task`` is spawned (added to the local task pool). -The first task submitted to the group is appended to the list, and since the list was empty, it spawns (adds to the local task pool) -the ``agg_task``, which points to the producer thread's task list. +Subsequent task submitted to the group are appended to the tree without spawning any tasks until the ``grab_task`` executes. -Aggregating tasks reserve a reference in the ``task_group``'s global counter and maintain a separate counter for tasks processing the chunks. -The lifetime of ``agg_task`` is extended until the last chunk of the grabbed list is processed. +Once a consumer worker thread steals and executes the ``grab_task``, @@ -138,3 +228,42 @@ There are several options for expressing tasks aggregation in the public API: * Which public APIshould be used to implement tasks aggregation? See the [Possible APIs section](#possible-apis) for details. * Which chunking approach should be implemented? See the [Alternative Approaches for Chunking section](#alternative-approaches-for-chunking) for details. * What chunk size should be chosen? + + + + + + + + +### Adding a task into the tree + +Consider having an empty binary task tree. +The first added task replaces the head of the tree. The second task is inserted as a left subtask of the head. The right subtask of the tree is always ``nullptr`` to support the tree structure described above. + +Starting from the third inserted task, the tasks are inserted into the left or right subtree containing less number of items. + +The sequence diagram of the insertion is shown below: + +```mermaid +flowchart TD + + I[insert new_task into the tree] --> head_check[head == nullptr?] + head_check --> |Yes| head_insert[head = new_task] + head_check --> |No| subtree_insert[subtree = head->left] + subtree_insert --> subtree_check[subtree == nullptr?] + subtree_check --> |Yes| subtree_head_insert[subtree = new_task] + subtree_check --> |No| subtree_head_left_check[subtree->left == nullptr?] + subtree_head_left_check --> |Yes| subtree_head_left_insert[subtree->left = new_task] + subtree_head_left_check --> |No| subtree_head_right_check[subtree->right == new_task?] + subtree_head_right_check --> |Yes| subtree_head_right_insert[subtree->right = new_task] + subtree_head_right_check --> |No| subtree_head_size_check[subtree->left->num_items
<
subtree->right->num_items
?] + subtree_head_size_check --> |Yes| subtree_switch_left[subtree = subtree->left] + subtree_head_size_check --> |No| subtree_switch_right[subtree = subtree->right] + subtree_switch_left --> subtree_head_left_check + subtree_switch_right --> subtree_head_left_check + head_insert --> exit + subtree_head_insert --> exit + subtree_head_left_insert --> exit + subtree_head_right_insert --> exit +``` \ No newline at end of file From d93f52a6245136a1e7f617a19a3f226c6a24f87e Mon Sep 17 00:00:00 2001 From: "Boyarinov, Konstantin" Date: Thu, 11 Dec 2025 13:49:50 +0200 Subject: [PATCH 3/8] Save progress --- rfcs/proposed/tasks_aggregation/README.md | 96 ++++++++++++++++++----- 1 file changed, 78 insertions(+), 18 deletions(-) diff --git a/rfcs/proposed/tasks_aggregation/README.md b/rfcs/proposed/tasks_aggregation/README.md index 5268194ea1..d5f99e41e5 100644 --- a/rfcs/proposed/tasks_aggregation/README.md +++ b/rfcs/proposed/tasks_aggregation/README.md @@ -164,38 +164,98 @@ The first task submitted to the group is appended to the tree, and since the tre Subsequent task submitted to the group are appended to the tree without spawning any tasks until the ``grab_task`` executes. -Once a consumer worker thread steals and executes the ``grab_task``, +Once a consumer worker thread steals and executes the ``grab_task``, it grabs the tree containing some amount of tasks. Let's say it contains 1000 tasks. - +Starting from this moment, the producer's task tree is empty again, hence subsequent ``run``s would spawn additional ``grab_task``s. -Subsequent tasks submitted to the group are appended to the head of the list without spawning additional aggregating tasks. +Consumer thread check if the grabbed tree is divisible and if it is - splits the tree into halves, spawns a task representing a second subtree, and re-executes +the current tree's head to keep dividing the tree until the desired grainsize is achieved. - +Let's consider a grainsize of 150 tasks. Producer thread appended 500 tasks into the task tree before the consumer thread 1 came and grabbed them. +Thread 1 splits the tree into two halves (each containing 250 tasks) and spawns a head into thread 1's local task pool. -When a consumer worker thread looks for work, the only task available for stealing is ``agg_task``. +Assume Thread 2 steals the spawned task from Thread 1 - it will also split the subtree into two halves, each containing 125 tasks, but will spawn the second half to Thread 2's +local task pool. - +Thread 3 can now steal the task spawned by Thread 2 and since it is not divisible - spawns the entire subtree to its local task pool. -When the consumer thread executes the ``agg_task``, it grabs the task list and makes it empty again. Further calls to ``run()`` will -append the task to the empty list and spawn additional *aggregating-task*s to grab the new list. +Same amount of tasks would be spawned by Thread 2 when the task would be re-executed. - +Thread 1 would split the tree once more resulting it once more stealing (let's say by Thread 4). -After grabbing the task list, the consumer thread splits the list into chunks of fixed size. Assume a chunk size of ``2``. -In the example above, it will create two service *chunk-task*s pointing to the sub-lists. One is spawned, while the other is bypassed. +Hence, for such a model, instead of spawning 500 tasks by the Producer Thread and further stealing from a single source, 4 threads have spawned 125 tasks each, resulting +in 4 different task sources for stealing that improves the stealing scalability. -Chunk tasks reserve a reference in the aggregating task's reference counter and maintain another counter for individual listed tasks in the chunk. -The lifetime of a chunk task is extended until the last listed task in the chunk is executed. +This model can be interpreted also in the following manner. Consider a producer is going to generate N input work items. As it was mentioned, N is unknown in advance. +The execution using *aggregating-task-group* would be similar to buffering some amount of inputs, execute async parallel for on top of the buffer and start buffering again. +So processing *N* input work items can result in *M* ``parallel_for`` invocations over *K0*, *K1*, ..., *KM* iterations where *K0* + *K1* + ... + *KM* == *N*. - +### Resolving the Reference Counting Bottleneck -Another consumer thread can now steal the *chunk-task* from the first consumer thread's local task pool. +As it was mentioned in the introduction, ``tbb::task_group`` in the single producer-multiple consumers case suffers from a bottleneck caused by tasks reference counting. -When the consumer thread executes the *chunk-task*, it spawns all tasks except one and bypasses the last. +Since all of the tasks are created by a single thread, it's ``thread_reference_vertex`` holds a single reference in a ``task_group``s ``wait_context``, while each +task holds reference in a producer's ``thread_reference_vertex``. Since the tasks are likely stolen and executed by consumer threads, reference counter in producer's +``thread_reference_vertex`` would be decremented by multiple threads, resulting in negative performance effects. + +In the *aggregating-task-group* task distribution approach, this can be solved by adding a reference counter to each task of the tree. A reference is held: +1. For a task itself +2. For a left task in a tree +3. For a right task in a tree + +Before spawning, the *grab-task* holds a reference in a calling thread ``thread_reference_vertex``. Once it executes and grabs the task tree, it substitutes itself +with the head of the grabbed subtree in a waiting tree. + +The lifetime of each task is extended until the reference counter is non-zero. Similar to + +```mermaid +flowchart TD + producer_vertex[producer thread vertex
ref_count = 1] --> tg_wait_context[task_group wait context
ref_count = 1] + task0[task0
ref_count = 2] --> producer_vertex + task0 --> task1[task1
ref_count = 3] + task1 --> task2[task2
ref_count = 1] + task1 --> task3[task3
ref_count = 1] + task1 --> task0 + task2 --> task1 + task3 --> task1 + + linkStyle 0,1,5,6,7 stroke:blue,stroke-width:2px,color:blue + linkStyle 2,3,4 stroke:red,stroke-width:2px,color:red +``` + +In a diagram above, red arrows denotes a left or right child in a task tree and are not involved in a reference counting. +Blue arrows denotes a parent relationship in a waiting tree. + +When a subtree is split into halves, only the red arrows are changing. The waiting tree remain unchanged. +Once a task executed it's body, it releases a self-reference held in a reference counter. Once the reference counter reaches zero, +the task is deallocated and releases a reference in it's parent in a waiting tree. + +This approach allows to distribute the reference counting across multiple tasks and avoid bottleneck on a single counter. + +### Allocator Bottleneck + +Another bottleneck that appears in a single producer- multiple consumer scenario is an allocation bottleneck. In a regular ``task_group`` (as well as +in most of TBB parallel algorithms), tasks are allocated in a per-thread small object pool. Only an owner thread can allocate from a pool, but multiple +threads can release a memory to a pool. + +Since all of the tasks are created (i.e. allocated) by a single producer's small object pool, multiple stealers would release the memory to a single pool +during the group's work. Producer's small object pool becomes another bottleneck in a system. + +Current version of the document do not propose concrete solution for this bottleneck. Possible solution may be to avoid deallocating the task tree on the +consumer's side, but create a service *free-task* pointing to the task tree and submitting it directly to the producer to guarantee that the memory would +be deallocated by the owner. But since the original tree structure is broken by the constant tree splitting, this approach is not straightforward to implement. + +## Performance Analysis + +## Alternative Approaches Considered + +## Possible APIs + +## Open Questions + +## Task Tree operations in Details - -### Alternative Approaches for Chunking Alternative approaches for how the *aggregating-task* should process the grabbed list can be considered. From b87a911265fc60dfe1f1a6faf9557a44639c54c2 Mon Sep 17 00:00:00 2001 From: "Boyarinov, Konstantin" Date: Fri, 12 Dec 2025 14:08:57 +0200 Subject: [PATCH 4/8] Add Table of Contents --- rfcs/proposed/task_aggregation/README.md | 410 ++++++++++++++++++ .../aggregating_group_aggregating_task.png | Bin .../assets/aggregating_group_chunking.png | Bin .../aggregating_group_consumer_thread.png | Bin .../assets/aggregating_group_first_run.png | Bin .../assets/aggregating_group_further_runs.png | Bin .../aggregating_group_process_chunks.png | Bin .../aggregating_group_producer_thread.png | Bin .../assets/perf_comp_parallel_for.png | Bin 0 -> 33251 bytes .../assets/perf_scalability.png | Bin 0 -> 32536 bytes .../producer_consumer_consumer_threads.png | Bin .../producer_consumer_producer_thread.png | Bin rfcs/proposed/tasks_aggregation/README.md | 329 -------------- 13 files changed, 410 insertions(+), 329 deletions(-) create mode 100644 rfcs/proposed/task_aggregation/README.md rename rfcs/proposed/{tasks_aggregation => task_aggregation}/assets/aggregating_group_aggregating_task.png (100%) rename rfcs/proposed/{tasks_aggregation => task_aggregation}/assets/aggregating_group_chunking.png (100%) rename rfcs/proposed/{tasks_aggregation => task_aggregation}/assets/aggregating_group_consumer_thread.png (100%) rename rfcs/proposed/{tasks_aggregation => task_aggregation}/assets/aggregating_group_first_run.png (100%) rename rfcs/proposed/{tasks_aggregation => task_aggregation}/assets/aggregating_group_further_runs.png (100%) rename rfcs/proposed/{tasks_aggregation => task_aggregation}/assets/aggregating_group_process_chunks.png (100%) rename rfcs/proposed/{tasks_aggregation => task_aggregation}/assets/aggregating_group_producer_thread.png (100%) create mode 100644 rfcs/proposed/task_aggregation/assets/perf_comp_parallel_for.png create mode 100644 rfcs/proposed/task_aggregation/assets/perf_scalability.png rename rfcs/proposed/{tasks_aggregation => task_aggregation}/assets/producer_consumer_consumer_threads.png (100%) rename rfcs/proposed/{tasks_aggregation => task_aggregation}/assets/producer_consumer_producer_thread.png (100%) delete mode 100644 rfcs/proposed/tasks_aggregation/README.md diff --git a/rfcs/proposed/task_aggregation/README.md b/rfcs/proposed/task_aggregation/README.md new file mode 100644 index 0000000000..87c7e4de63 --- /dev/null +++ b/rfcs/proposed/task_aggregation/README.md @@ -0,0 +1,410 @@ +# Task Aggregation + +This proposal outlines a possible solution to producer-consumer inefficiencies in the current ``task_group`` API. + +## Table of Contents + +* 1 [Introduction](#introduction) +* 2 [Proposed Design](#proposed-design) + * 2.1 [Resolving the Single Task Source Bottleneck](#resolving-the-single-task-source-bottleneck) + * 2.2 [Resolving the Reference Counting Bottleneck](#resolving-the-reference-counting-bottleneck) + * 2.3 [Allocator Bottleneck](#allocator-bottleneck) + * 2.4 [Grabbing Thread Slowdown](#grabbing-thread-slowdown) +* 3 [Performance Analysis](#performance-analysis) +* 4 [Possible APIs](#possible-apis) +* 5 [Open Questions](#open-questions) +* 6 [Task Tree Operations in Detail](#task-tree-operations-in-detail) + * 6.1 [Synchronization Between Grabber and Producer](#synchronization-between-grabber-and-producer) + * 6.2 [Adding a Task to the Tree](#adding-a-task-to-the-tree) + * 6.3 [Splitting the Tree](#splitting-the-tree) +* 7 [Alternative Approaches Considered](#alternative-approaches-considered) + +## Introduction + +Consider an application that uses ``tbb::task_group`` with a single-producer/multiple-consumers pattern: one producer thread generates work items, and +multiple consumer threads process them in parallel. + +```cpp + +void produce() { + tbb::task_group tg; + + auto work_item = get_work_item(); + + while (work_item != end_of_work) { + tg.run(process(work_item)); // Can be executed by any worker + } + + tg.wait(); +} +``` + +The ``get_work_item()`` function generates work items, e.g., by reading from an input file or stream. When no more work remains, it +returns ``end_of_work``. + +Because the number of work items is unknown in advance, algorithms such as ``tbb::parallel_for`` or ``tbb::parallel_for_each`` can be inefficient: +they require buffering to create an input sequence, delaying computation of the earliest items. + +``tbb::task_group`` is semantically appropriate because it submits available work immediately for execution. + +In current implementation, ``tbb::task_group::run`` creates a task for the given callable and pushes it into the producer thread's local task pool. + +When worker (consumer) threads join the task arena, they search for tasks to process. Because the producer's local task pool is the sole task source, +worker threads steal tasks from it. + +After completing a stolen task, workers become idle and steal again from the same producer's pool. +When many consumers contend for a single producer's pool, stealing becomes a bottleneck, leading to performance +penalties and poor scalability. + +Another bottleneck is the ``tbb::task_group``'s reference counter used to register tasks in the group. The counter +increments when a task is created and decrements when the task completes. + +To improve scalability, oneTBB 2022.1.0 introduced per-thread reference counters: each thread that creates +tasks registers its thread-local counter as a leaf in the group's counter. + +Tasks created by a thread hold references in the thread-local counter, which in turn holds a single reference in the group's counter. + +However, in the scenario above, all tasks are created by the producer thread, so all reference converge into a single producer's counter. + +## Proposed Design + +### Resolving the Single Task Source Bottleneck + +This document proposes a task distribution mechanism that combines the existing ``task_group`` spawning model with an aggregator. + +For simplicity, we refer to this API as *aggregating-task-group* providing ``run(task_body)`` and ``wait()`` methods, +analogous to ``tbb::task_group``. + +Tasks in an *aggregating-task-group* form a binary task tree: the first and second layers contain a single task, and the N-th layer contains up to $2^N$ tasks. + +Each tree node contains three pointers: +* Pointer to the left child (next layer) +* Pointer to the right child (next layer) +* Pointer to the parent (previous layer) + +Additionally, each node stores the number of items in its subtree. + +```mermaid +flowchart TD + task0[task0
num_items = 7] --> task1 + task1[task1
num_items = 6] --> task2 + task1 --> task3 + task2[task2
num_items = 3] --> task4[task4
num_items = 1] + task2 --> task5[task5
num_items = 1] + task3[task3
num_items = 2] --> task6[task6
num_items = 1] + task1 --> task0 + task2 --> task1 + task3 --> task1 + task4 --> task2 + task5 --> task2 + task6 --> task3 + + + linkStyle 0,1,2,3,4,5 stroke:red,stroke-width:2px,color:red + linkStyle 6,7,8,9,10,11 stroke:blue,stroke-width:2px,color:blue +``` + +The binary task tree supports three basic actions: +* Add a task to the tree +* Split the tree into halves +* Grab all items in the tree + +Detailed description of these operations appear in [Task Tree Operations in Detail](#task-tree-operations-in-detail) section. + +Each *aggregating-task-group* instance maintains a thread-local atomic pointer to its binary tree, initially empty. + +Each ``run()`` appends a task to the calling thread's tree and if the tree was empty, it also spawns a service *grab-task* +that references the tree. + +```mermaid +flowchart TD + run["run(task_body)"] --> allocate_tree_task[t = allocate task] + allocate_tree_task --> add_task["first = thread_local_tree.add_task(t)"] + add_task --> first_check[first ?] + first_check --> |Yes| allocate_grab_task[gt = allocate grab task] + first_check --> |No| exit + allocate_grab_task --> spawn_grab_task["spawn(gt)"] + spawn_grab_task --> exit +``` + +When a worker executes the stolen *grab-task*, it grabs all items from the producer's tree. + +Because the producer's tree is left empty, subsequent ``run`` calls append tasks and spawn new *grab-task*s. + +After grabbing the tree, the *grab-task* bypasses the head node. + +The head checks the tree's item count and, if it exceeds the specified grainsize, splits the tree into halves. +It then spawns the head of the second half, while the current task (now the head of the first half) re-executes, +recursively splitting until the grainsize is reached. + +Once the grainsize is reached, the head spawns its children recursively and executes the user body. + +```mermaid +flowchart TD + grab["grab_task(tree)"] --> grab_all["head = tree.grab_all()"] + grab_all --> bypass_head[bypass head] + bypass_head --> head_body["head body"] + head_body --> is_divisible_check[head->is_divisible ?] + is_divisible_check --> |Yes| split_head["head2 = split(head)"] + is_divisible_check --> |No| spawn_children("recursive children spawn") + split_head --> spawn_head2["spawn(head2)"] + spawn_head2 --> |This thread| is_divisible_check + spawn_head2 --> |Other thread| head2_body + head2_body --> head_assign[head = head2] + head_assign --> is_divisible_check + spawn_children --> execute_body["execute head's user body"] + execute_body --> exit + +``` + +Revisit the producer-consumer example using *aggregating-task-group* instead of ``tbb::task_group``. + +When the *aggregating-task-group* is created, the producer's task tree is empty. + +The first submitted task is appended and because the tree was empty, a *grab-task* is spawned (added to the local task pool). + +Subsequent tasks are appended without spawning until the *grab-task* executes. +When a worker steals and executes the *grab-task*, it grabs the tree. + +From that moment, the producer's tree is empty again, so subsequent ``run`` call spawn additional *grab-task*. + +The consumer checks whether the grabbed tree is divisible; if so, it splits the tree into halves, spawns a task for the +second subtree, and re-executes the head to continue dividing until the desired grainsize is reached. + +Assume a grainsize of 150 tasks. The producer appended 500 tasks into the task tree before the consumer thread 1 came +and grabbed them. Thread 1 splits the tree into two halves (250 tasks each) and spawns the head of the second half into +its local pool. + +Thread 2 steals that task, splits the subtree into two halves (125 each), and spawns the second half to its own local pool. + +Thread 3 steals the task spawned by Thread 2, and since it is no longer divisible, it spawns the entire subtree into its local pool. + +Thread 2 will similarly spawn the other half when its task re-executes. + +Thread 1 splits again, enabling another steal (e.g., by Thread 4). + +Thus, instead of the producer spawning 500 tasks into a single source, four threads each spawn 125 tasks, creating four +independent work sources that improve stealing scalability. + +This model can also be interpreted as follows. Suppose the producer will generate *N* input items, where *N* is unknown in advance. +Execution with *aggregating-task-group* resembles buffering a batch of inputs, running an asynchronous parallel-for over the batch, then buffering again. + +Processing *N* items may result in *M* parallel-for invocations over $K_0$, $K_1$, ..., $K_M$ iterations, where $\sum_{i=0}^{M} K_i = N $ + +### Resolving the Reference Counting Bottleneck + +As noted earlier, ``tbb::task_group`` suffers a reference-counting bottleneck in the single-producer/multiple-consumers case. + +Because all tasks are created by a single thread, its ``thread_reference_vertex`` holds one reference in the group's +``wait_context``, while each task holds a reference in the producer's ``thread_reference_vertex``. + +As tasks are stolen and executed by consumers, the producer's ``thread_reference_vertex`` counter is decremented concurrently +by many threads, causing contention and performance loss. + +In *aggregating-task-group*, it is addressed by adding per-task reference counters. References are held for: +1. The task itself +2. Its left child (if any) +3. Its right child (if any) + +Before spawning, the *grab-task* holds a reference in a producer's ``thread_reference_vertex``. After execution and grab, +it replaces itself with the head of the grabbed tree in the waiting tree. + +Each task's lifetime extends until its reference count reaches zero. + +```mermaid +flowchart TD + producer_vertex[producer thread vertex
ref_count = 1] --> tg_wait_context[task_group wait context
ref_count = 1] + task0[task0
ref_count = 2] --> producer_vertex + task0 --> task1[task1
ref_count = 3] + task1 --> task2[task2
ref_count = 1] + task1 --> task3[task3
ref_count = 1] + task1 --> task0 + task2 --> task1 + task3 --> task1 + + linkStyle 0,1,5,6,7 stroke:blue,stroke-width:2px,color:blue + linkStyle 2,3,4 stroke:red,stroke-width:2px,color:red +``` + +In a diagram, red arrows denote left/right child links in the task tree and are not involved in a reference counting. +Blue arrows denote parent links in the waiting tree. + +When a tree is split, only the red links change; the waiting tree remains unchanged. + +After a task executes its body, it releases its self-reference. When its count reaches zero, the task is deallocated and +releases its parent's reference in the waiting tree. + +This approach distributes reference counting across tasks and avoids a single-counter bottleneck. + +### Allocator Bottleneck + +Another bottleneck that appears in a single-producer/multiple-consumer scenario is an allocation bottleneck. + +In ``tbb::task_group`` (and most TBB algorithms), tasks are allocated from per-thread small-object pools. Only the owner thread allocates from its pool, while multiple threads can release memory to it. + +Because all tasks are created by a single producer's pool, multiple stealers release memory into that same pool during execution. The producer's pool becomes another system bottleneck. + +This document does not yet propose a concrete solution for this bottleneck. + +### Grabbing Thread Slowdown + +We also consider slowing down the grabbing thread when the desired tree's item count is below the recommended threshold. +In that case, the grabbing thread can sleep intermittently and re-check the tree size. After waking, if the tree size +exceeds the threshold, does not grow further, or a fixed number of sleeps is exceeded, the thread grabs +the tree and begins division. + +## Performance Analysis + +Consider the following benchmark to evaluate the proposed approach. +The producer requests parallel computation over *N* input items. It splits the input into *NumChunks* and evaluates three approaches: +1. ``tbb::parallel_for`` over *N* inputs using ``simple_partitioner`` with grainsize *NumChunks*. Although *N* is unknown in real single-producer/multiple-consumers scenarios (making this approach impractical), it serves as an upper-bound "gold" baseline for the benchmark. +2. Submit *NumChunks* tasks to a regular ``tbb::task_group``. +3. Submit *NumChunks* tasks to *aggregating-task-group*. + +Experiments were run on a 256-CPU, dual-NUMA machine. + +The chart below compares ``tbb::task_group`` and *aggregating-task-group* against ``parallel_for``. + + + +The table and chart report slowdowns relative to ``parallel_for``. + +For small task counts, ``tbb::task_group`` matches ``parallel_for``; beyond a threshold, its slowdown grows dramatically. + +*aggregating-task-group* also slows relative to ``parallel_for``, but performs up to 10x better than a regular ``task_group``. + +The next chart shows scalability for all three approaches at the largest chunk count. + + + +*aggregating-task-group* scales significantly better than regular ``task_group``. + +## Possible APIs + +It is not advisable to replace ``tbb::task_group`` with the proposed aggregation algorithm wholesale, as that could introduce +unnecessary overhead in scenarios where tasks are submitted by multiple threads (e.g., divide-and-conquer). + +This document does not yet propose a concrete API. The following options can be considered: +1. A separate ``task_group``-like API with the same member functions. +2. Integration into ``tbb::task_group``, e.g., an additional ``run()`` overload with a policy parameter or a separate member function. +3. A separate API (e.g., ``tbb::parallel_while(generator, predicate, body))``), equivalent to +``while(input = generator(), predicate(input)) { body(input); }``, where the generator and the predicate +run on the calling thread while the loop bodies execute in parallel. + +## Open Questions + +* Should task aggregation be exposed as part of the public TBB API? +* Which public API surface is most appropriate? See [Possible APIs section](#possible-apis). +* Which configuration parameters and heuristics are appropriate (e.g., tree division grainsize, grabbing-thread slowdown, etc.)? +* Solutions for the producer's [allocator bottleneck](#allocator-bottleneck) should be considered. + +## Task Tree Operations in Detail + +### Synchronization Between Grabber and Producer + +Because the task tree is thread-local and only one *grab-task* is spawned per non-empty tree, synchronization is needed only +between the producer (which adds tasks) and the grabber (which collects tasks for distribution). + +Synchronization can be implemented with a single atomic pointer to the tree head and a spin-lock built on that atomic. +The algorithm reserves a sentinel ``locked`` value of type ``tree_task*``. + +The producer locks its thread-local tree by exchanging the atomic value to ``locked``. After inserting a new leaf into the tree, the producer +exchanges ``locked`` back to the head pointer. + +The grabber reads the atomic head; if it is ``locked``, it spins until unlocked. Once unlocked, it attempts to CAS to set the head to ``nullptr``. +If CAS fails (the producer re-locked the tree), the grabber spins and retries. + +```mermaid +flowchart TD + grab --> load_tree["tree = head.load()"] + load_tree --> check_locked["is_locked(tree) ?"] + check_locked --> |Yes| spin_while_locked["tree = spin_while_locked(head)"] + check_locked --> |No| try_grab["head.cas(tree, nullptr)"] + spin_while_locked --> try_grab + try_grab --> |Success| exit_grab["Exit to tree distribution"] + try_grab --> |Fail| spin_while_locked + + add_task[add_task
producer] --> lock_tree["tree = head.exchange(nullptr)"] + lock_tree --> insert["do insert"] + insert --> unlock_tree["head.exchange(tree)"] + unlock_tree --> exit_producer["Exit"] +``` + +### Adding a Task to the Tree + +Consider an empty binary task tree. The first added task becomes the head. The second task is inserted as the head's left child. +The right child of the head is always ``nullptr`` to maintain the structure described above. + +From the third task onward, insert into the subtree (left or right) with fewer items. + +```mermaid +flowchart TD + + I[insert new_task into the tree] --> head_check[head == nullptr?] + head_check --> |Yes| head_insert[head = new_task] + head_check --> |No| subtree_insert[subtree = head->left] + subtree_insert --> subtree_check[subtree == nullptr?] + subtree_check --> |Yes| subtree_head_insert[subtree = new_task] + subtree_check --> |No| subtree_head_left_check[subtree->left == nullptr?] + subtree_head_left_check --> |Yes| subtree_head_left_insert[subtree->left = new_task] + subtree_head_left_check --> |No| subtree_head_right_check[subtree->right == new_task?] + subtree_head_right_check --> |Yes| subtree_head_right_insert[subtree->right = new_task] + subtree_head_right_check --> |No| subtree_head_size_check[subtree->left->num_items
<
subtree->right->num_items
?] + subtree_head_size_check --> |Yes| subtree_switch_left[subtree = subtree->left] + subtree_head_size_check --> |No| subtree_switch_right[subtree = subtree->right] + subtree_switch_left --> subtree_head_left_check + subtree_switch_right --> subtree_head_left_check + head_insert --> exit + subtree_head_insert --> exit + subtree_head_left_insert --> exit + subtree_head_right_insert --> exit +``` + +### Splitting the Tree + +As noted above, each node maintains pointer to left/right children and to its parent. + +This document proposes requiring the grainsize to be >= 4 to simplify splitting. + +Splitting occurs only after a grab; thus, the splitting thread exclusively owns the tree, and no synchronization is needed. + +Only the first two layers of the tree are needed for splitting. Let's call them ``head`` and ``next``. Splitting is done by setting +``head->left = next->right``, then ``next->right = nullptr``, and update counters accordingly. + +Parent pointers are not modified so the waiting tree remains intact for reference counting. + +Before splitting + +```mermaid +flowchart TD + head[head
num_items = 8] --> next[next
num_items = 7] + next --> t1[t1
num_items = 3] + next --> t2[t2
num_items = 3] + t1 --> t3[t3
num_items = 1] + t1 --> t4[t4
num_items = 1] + t2 --> t5[t5
num_items = 1] + t2 --> t6[t6
num_items = 1] +``` + +After splitting + +```mermaid +flowchart TD + head[head
num_items = 4] --> t2[t2
num_items = 3] + t2 --> t5[t5
num_items = 1] + t2 --> t6[t6
num_items = 1] + + next[next
num_items = 4] --> t1[t1
num_items = 3] + t1 --> t3[t3
num_items = 1] + t1 --> t4[t4
num_items = 1] +``` + +## Alternative Approaches Considered + +An alternative considered was aggregating tasks in a producer-side singly-linked list instead of a tree. +This simplifies synchronization between the grabber and producer using a single atomic: producer uses CAS; grabber uses exchange. + +However, after grabbing a list, the efficient options are limited: crop a prefix, spawn a service task to process the tail, then spawn tasks from the cropped prefix. +This requires traversing the cropped tasks twice, because the tail's service task must be spawned first to be stolen first. + +A binary task tree provides significantly better distribution (akin to ``parallel_for``'s work splitting). diff --git a/rfcs/proposed/tasks_aggregation/assets/aggregating_group_aggregating_task.png b/rfcs/proposed/task_aggregation/assets/aggregating_group_aggregating_task.png similarity index 100% rename from rfcs/proposed/tasks_aggregation/assets/aggregating_group_aggregating_task.png rename to rfcs/proposed/task_aggregation/assets/aggregating_group_aggregating_task.png diff --git a/rfcs/proposed/tasks_aggregation/assets/aggregating_group_chunking.png b/rfcs/proposed/task_aggregation/assets/aggregating_group_chunking.png similarity index 100% rename from rfcs/proposed/tasks_aggregation/assets/aggregating_group_chunking.png rename to rfcs/proposed/task_aggregation/assets/aggregating_group_chunking.png diff --git a/rfcs/proposed/tasks_aggregation/assets/aggregating_group_consumer_thread.png b/rfcs/proposed/task_aggregation/assets/aggregating_group_consumer_thread.png similarity index 100% rename from rfcs/proposed/tasks_aggregation/assets/aggregating_group_consumer_thread.png rename to rfcs/proposed/task_aggregation/assets/aggregating_group_consumer_thread.png diff --git a/rfcs/proposed/tasks_aggregation/assets/aggregating_group_first_run.png b/rfcs/proposed/task_aggregation/assets/aggregating_group_first_run.png similarity index 100% rename from rfcs/proposed/tasks_aggregation/assets/aggregating_group_first_run.png rename to rfcs/proposed/task_aggregation/assets/aggregating_group_first_run.png diff --git a/rfcs/proposed/tasks_aggregation/assets/aggregating_group_further_runs.png b/rfcs/proposed/task_aggregation/assets/aggregating_group_further_runs.png similarity index 100% rename from rfcs/proposed/tasks_aggregation/assets/aggregating_group_further_runs.png rename to rfcs/proposed/task_aggregation/assets/aggregating_group_further_runs.png diff --git a/rfcs/proposed/tasks_aggregation/assets/aggregating_group_process_chunks.png b/rfcs/proposed/task_aggregation/assets/aggregating_group_process_chunks.png similarity index 100% rename from rfcs/proposed/tasks_aggregation/assets/aggregating_group_process_chunks.png rename to rfcs/proposed/task_aggregation/assets/aggregating_group_process_chunks.png diff --git a/rfcs/proposed/tasks_aggregation/assets/aggregating_group_producer_thread.png 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zD)XM$0P#`^@^;)?LP3N5DHoQVYzj%is9`d+uc{HxEMktTYWm69lH_&Mgv5FweDWha zP_3DLE6Vb}UkPI%VJ=0IdUNfqe5po0BPafqNfV5PG^)yAybjBBaphyIVKhTZPtD{X z)M6PZe72FRUkq%R-4SDgRpq2@jz-&^QvF>>vhQ_csh%(t;d>sdEqr2^Yc61v(BoF+ z{vOL8=;HgEzA9T012I*tjI>I=r6%hids*Isq~`qm&tpZH>M?W76IbNM$Shi-Oq(tE zZf6&zcRa=Hn9ER{GiP9T?m)qyyKaC7m=ImbcTU{OV}Qjws*Hm9t@#*(0JrcuV`WMH zbv6p}h5M1rDca0mg_RJI+k9cj#)+j@CAWuOPKF9V^O@;=>lT1`ios7+J~Mh2?`&>C z(ZaiQb86;n`P$h| Date: Wed, 17 Dec 2025 15:51:38 +0200 Subject: [PATCH 7/8] Update rfcs/proposed/task_aggregation/README.md Co-authored-by: Mike Voss --- rfcs/proposed/task_aggregation/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rfcs/proposed/task_aggregation/README.md b/rfcs/proposed/task_aggregation/README.md index ea3ef144fe..4df11a3bfd 100644 --- a/rfcs/proposed/task_aggregation/README.md +++ b/rfcs/proposed/task_aggregation/README.md @@ -337,7 +337,7 @@ flowchart TD subtree_check --> |Yes| subtree_head_insert[head->left = new_task] subtree_check --> |No| subtree_head_left_check[subtree->left == nullptr?] subtree_head_left_check --> |Yes| subtree_head_left_insert[subtree->left = new_task] - subtree_head_left_check --> |No| subtree_head_right_check[subtree->right == new_task?] + subtree_head_left_check --> |No| subtree_head_right_check[subtree->right == nullptr?] subtree_head_right_check --> |Yes| subtree_head_right_insert[subtree->right = new_task] subtree_head_right_check --> |No| subtree_head_size_check[subtree->left->num_items
<
subtree->right->num_items
?] subtree_head_size_check --> |Yes| subtree_switch_left[subtree = subtree->left] From 40ed0dcd0625a0be0df9f7c6a9b71e565f50cab0 Mon Sep 17 00:00:00 2001 From: Konstantin Boyarinov Date: Wed, 17 Dec 2025 15:52:02 +0200 Subject: [PATCH 8/8] Update rfcs/proposed/task_aggregation/README.md Co-authored-by: Mike Voss --- rfcs/proposed/task_aggregation/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rfcs/proposed/task_aggregation/README.md b/rfcs/proposed/task_aggregation/README.md index 4df11a3bfd..0723786683 100644 --- a/rfcs/proposed/task_aggregation/README.md +++ b/rfcs/proposed/task_aggregation/README.md @@ -157,7 +157,7 @@ flowchart TD ``` -Revisit the producer-consumer example using *aggregating-task-group* instead of ``tbb::task_group``. +Let's revisit the producer-consumer example using *aggregating-task-group* instead of ``tbb::task_group``. When the *aggregating-task-group* is created, the producer's task tree is empty.

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zD)XM$0P#`^@^;)?LP3N5DHoQVYzj%is9`d+uc{HxEMktTYWm69lH_&Mgv5FweDWha zP_3DLE6Vb}UkPI%VJ=0IdUNfqe5po0BPafqNfV5PG^)yAybjBBaphyIVKhTZPtD{X z)M6PZe72FRUkq%R-4SDgRpq2@jz-&^QvF>>vhQ_csh%(t;d>sdEqr2^Yc61v(BoF+ z{vOL8=;HgEzA9T012I*tjI>I=r6%hids*Isq~`qm&tpZH>M?W76IbNM$Shi-Oq(tE zZf6&zcRa=Hn9ER{GiP9T?m)qyyKaC7m=ImbcTU{OV}Qjws*Hm9t@#*(0JrcuV`WMH zbv6p}h5M1rDca0mg_RJI+k9cj#)+j@CAWuOPKF9V^O@;=>lT1`ios7+J~Mh2?`&>C z(ZaiQb86;n`P$h| - -When consumer (worker) threads join the task arena, they must search for tasks to process. Since the only source of tasks in the arena is the local task pool -of the producing thread, consumer threads will steal individual tasks from it. - - - -After executing the stolen task, consumer threads run out of work again and must steal from the same producer's local task pool. -Because the number of consumer threads can be large, stealing tasks from a single producer's task pool creates a bottleneck, resulting in significant -performance penalties and poor scalability. - -Another bottleneck is a reference counter inside the ``tbb::task_group`` that is used to register the task in the group. The reference counter is incremented when a task -is created and decremented when the task completes execution. - -To improve the scalability of the reference counter, oneTBB ADD_VERSION introduced a per-thread reference counters - each thread that creates a task registers its thread-local -counter as a leaf in the ``task_group``'s reference counter. Tasks created by a thread holds a reference in the thread-local counter, which in turn holds a single reference -in the group's reference counter. - -However, in the scenario above all tasks are created by a producer thread, so all of them holds references in a single producer thread's counter. - -## Proposed Design - -### Resolving the Single Task Source Bottleneck - -This document proposes a task distribution mechanism combining the existing ``task_group`` spawning mechanism with the idea of the aggregator. - -For simplicity, let's refer to the API implementing this idea as *aggregating-task-group* providing two -basic methods ``run(task_body)`` and ``wait()``, similar to the regular ``task_group``. - -Tasks in an *aggregating-task-group* forms a binary task tree where the first and the second layers -contains a single task and each other N-th layer contains up to 2^N tasks. - -Each tree node contains three pointers to other nodes -* Pointer to the left node on the next layer -* Pointer to the right node on the next layer -* Pointer to the parent node on the previous layer - -Additionally, each node contains the number of items in the corresponding subtree. - -```mermaid -flowchart TD - task0[task0
num_items = 6] --> task1 - task1[task1
num_items = 5] --> task2 - task1 --> task3 - task2[task2
num_items = 3] --> task4[task4
num_items = 1] - task2 --> task5[task5
num_items = 1] - task3[task3
num_items = 2] --> task6[task6
num_items = 1] - task1 --> task0 - task2 --> task1 - task3 --> task1 - task4 --> task2 - task5 --> task2 - task6 --> task3 - - - linkStyle 0,1,2,3,4,5 stroke:#e74c3c,stroke-width:2px,color:#e74c3c - linkStyle 6,7,8,9,10,11 stroke:#0000FF,stroke-width:2px,color:#0000FF -``` - -Binary tree of tasks supports three basic actions: -* Adding a task into the tree -* Splitting the tree into halves -* Grabbing all items in the tree - -The detailed description of all of these operations will be provided in separate sections below. - -The *aggregating-task-group* instance supports a thread-local atomic pointer to the binary tree which is initially empty(``atomic`` set to ``nullptr``). - -Each call to ``run()`` appends a task to the calling thread's binary tree and if the tree was empty (i.e. the inserted task is the first in the tree), -spawns a service *grab-task* pointing to the tree. - -The sequence diagram for ``run`` is shown below: - -```mermaid -flowchart TD - run["run(task_body)"] --> allocate_tree_task[t = allocate task] - allocate_tree_task --> add_task["first = thread_local_tree.add_task(t)"] - add_task --> first_check[first ?] - first_check --> |Yes| allocate_grab_task[gt = allocate grab task] - first_check --> |No| exit - allocate_grab_task --> spawn_grab_task["spawn(gt)"] - spawn_grab_task --> exit -``` - -When the worker thread executes the *grab-task* stolen from the producer's local task pool, it grabs all the items in the producer's tree. - -Since the producer's tree is left empty, further call to ``run`` would append a task to the empty tree, resulting in spawning new *grab-task*s. - -After grabbing the task tree, the *grab-task* bypasses the head task of the tree. - -The bypassed head task checks the number of items in the tree, and if it exceeds the specified grainsize - splits the tree into halves. -The head task of the second half of the tree is spawned and the currently executing task (that is now head of the first half of the tree) -is re-executed, resulting in recursive splitting until the grainsize is reached. -Once the grainsize is reached, the head of the tree spawns its left and right leaf tasks if any and executes the associated user body. - -The sequence diagram for the grab and split tasks is shown below. - -```mermaid -flowchart TD - grab["grab_task(tree)"] --> grab_all["head = tree.grab_all()"] - grab_all --> bypass_head[bypass head] - bypass_head --> head_body["head body"] - head_body --> is_divisible_check[head->is_divisible ?] - is_divisible_check --> |Yes| split_head["head2 = split(head)"] - is_divisible_check --> |No| start_execute["t = head"] - split_head --> spawn_head2["spawn(head2)"] - spawn_head2 --> |Other thread| head2_body - head2_body --> head_assign[head = head2] - head_assign --> is_divisible_check - start_execute --> left_check[t->left == nullptr ?] - left_check --> |Yes| execute_t[execute t's function] - left_check --> |No| spawn_left["spawn(t->left)"] - execute_t --> exit - spawn_left --> |Other thread| left_body - spawn_left --> |This thread| right_check[t->right == nullptr ?] - right_check --> |Yes| execute_t - right_check --> |No| spawn_right["spawn(t->right)"] - spawn_right --> |Other thread| right_body - spawn_right --> |This thread| execute_t - left_body --> assign_t_left["t = t->left"] - assign_t_left --> left_check - right_body --> assign_t_right["t = t->right"] - assign_t_right --> left_check -``` - -Consider the producer-consumer example from the introduction, now using *aggregating-task-group* instead of ``tbb::task_group``. - -When the *aggregating-task-group* is created, the task tree of the producer thread is empty. - -The first task submitted to the group is appended to the tree, and since the tree was empty, the ``grab_task`` is spawned (added to the local task pool). - -Subsequent task submitted to the group are appended to the tree without spawning any tasks until the ``grab_task`` executes. - -Once a consumer worker thread steals and executes the ``grab_task``, it grabs the tree containing some amount of tasks. Let's say it contains 1000 tasks. - -Starting from this moment, the producer's task tree is empty again, hence subsequent ``run``s would spawn additional ``grab_task``s. - -Consumer thread check if the grabbed tree is divisible and if it is - splits the tree into halves, spawns a task representing a second subtree, and re-executes -the current tree's head to keep dividing the tree until the desired grainsize is achieved. - -Let's consider a grainsize of 150 tasks. Producer thread appended 500 tasks into the task tree before the consumer thread 1 came and grabbed them. -Thread 1 splits the tree into two halves (each containing 250 tasks) and spawns a head into thread 1's local task pool. - -Assume Thread 2 steals the spawned task from Thread 1 - it will also split the subtree into two halves, each containing 125 tasks, but will spawn the second half to Thread 2's -local task pool. - -Thread 3 can now steal the task spawned by Thread 2 and since it is not divisible - spawns the entire subtree to its local task pool. - -Same amount of tasks would be spawned by Thread 2 when the task would be re-executed. - -Thread 1 would split the tree once more resulting it once more stealing (let's say by Thread 4). - -Hence, for such a model, instead of spawning 500 tasks by the Producer Thread and further stealing from a single source, 4 threads have spawned 125 tasks each, resulting -in 4 different task sources for stealing that improves the stealing scalability. - -This model can be interpreted also in the following manner. Consider a producer is going to generate N input work items. As it was mentioned, N is unknown in advance. -The execution using *aggregating-task-group* would be similar to buffering some amount of inputs, execute async parallel for on top of the buffer and start buffering again. -So processing *N* input work items can result in *M* ``parallel_for`` invocations over *K0*, *K1*, ..., *KM* iterations where *K0* + *K1* + ... + *KM* == *N*. - -### Resolving the Reference Counting Bottleneck - -As it was mentioned in the introduction, ``tbb::task_group`` in the single producer-multiple consumers case suffers from a bottleneck caused by tasks reference counting. - -Since all of the tasks are created by a single thread, it's ``thread_reference_vertex`` holds a single reference in a ``task_group``s ``wait_context``, while each -task holds reference in a producer's ``thread_reference_vertex``. Since the tasks are likely stolen and executed by consumer threads, reference counter in producer's -``thread_reference_vertex`` would be decremented by multiple threads, resulting in negative performance effects. - -In the *aggregating-task-group* task distribution approach, this can be solved by adding a reference counter to each task of the tree. A reference is held: -1. For a task itself -2. For a left task in a tree -3. For a right task in a tree - -Before spawning, the *grab-task* holds a reference in a calling thread ``thread_reference_vertex``. Once it executes and grabs the task tree, it substitutes itself -with the head of the grabbed subtree in a waiting tree. - -The lifetime of each task is extended until the reference counter is non-zero. Similar to - -```mermaid -flowchart TD - producer_vertex[producer thread vertex
ref_count = 1] --> tg_wait_context[task_group wait context
ref_count = 1] - task0[task0
ref_count = 2] --> producer_vertex - task0 --> task1[task1
ref_count = 3] - task1 --> task2[task2
ref_count = 1] - task1 --> task3[task3
ref_count = 1] - task1 --> task0 - task2 --> task1 - task3 --> task1 - - linkStyle 0,1,5,6,7 stroke:blue,stroke-width:2px,color:blue - linkStyle 2,3,4 stroke:red,stroke-width:2px,color:red -``` - -In a diagram above, red arrows denotes a left or right child in a task tree and are not involved in a reference counting. -Blue arrows denotes a parent relationship in a waiting tree. - -When a subtree is split into halves, only the red arrows are changing. The waiting tree remain unchanged. -Once a task executed it's body, it releases a self-reference held in a reference counter. Once the reference counter reaches zero, -the task is deallocated and releases a reference in it's parent in a waiting tree. - -This approach allows to distribute the reference counting across multiple tasks and avoid bottleneck on a single counter. - -### Allocator Bottleneck - -Another bottleneck that appears in a single producer- multiple consumer scenario is an allocation bottleneck. In a regular ``task_group`` (as well as -in most of TBB parallel algorithms), tasks are allocated in a per-thread small object pool. Only an owner thread can allocate from a pool, but multiple -threads can release a memory to a pool. - -Since all of the tasks are created (i.e. allocated) by a single producer's small object pool, multiple stealers would release the memory to a single pool -during the group's work. Producer's small object pool becomes another bottleneck in a system. - -Current version of the document do not propose concrete solution for this bottleneck. Possible solution may be to avoid deallocating the task tree on the -consumer's side, but create a service *free-task* pointing to the task tree and submitting it directly to the producer to guarantee that the memory would -be deallocated by the owner. But since the original tree structure is broken by the constant tree splitting, this approach is not straightforward to implement. - -## Performance Analysis - -## Alternative Approaches Considered - -## Possible APIs - -## Open Questions - -## Task Tree operations in Details - - - -Alternative approaches for how the *aggregating-task* should process the grabbed list can be considered. - -For example, it may follow the approach of ``tbb::parallel_for_each`` for forward iterators: it cuts a single chunk -from the list, re-spawns the *chunking-task* with the reduced list, and bypasses the *chunk-task*. - -For example, it may follow the approach of ``tbb::parallel_for_each`` for forward iterators: it cuts the single chunk -from the list, re-spawns the *chunking-task* with the reduced list and bypasses the *chunk-task*. When a worker thread -executes the stolen *chunking-task*, it cuts and bypasses the task for the next chunk and re-spawns the *chunking-task*. - -The *chunking-task* is re-spawned until the list is empty. - -## Possible APIs - -It does not make sense to completely replace ``task_group::run`` spawning mechanism with task aggregation, as this could -introduce unnecessary performance penalties in other ``task_group`` use cases (e.g., divide-and-conquer patterns), where -tasks are produced by multiple threads or generate additional tasks. - -There are several options for expressing tasks aggregation in the public API: - -1. A separate ``task_group``-like API, e.g., ``tbb::aggregating_task_group``, providing the same set - of member functions as ``tbb::task_group``. -2. Integrate it into the regular ``tbb::task_group`` API by providing an additional overload for ``task_group::run`` that - accepts a policy parameter, or a separate function in ``task_group``. -3. Introduce a separate API not connected with the task groups. One option is ``tbb::parallel_while(generator, predicate, body)``, - which executed ``body(input)`` in parallel while ``pred(input) == true``, with ``input`` generated by repeated calls to ``generator()``. - -## Open Questions - -* Which public APIshould be used to implement tasks aggregation? See the [Possible APIs section](#possible-apis) for details. -* Which chunking approach should be implemented? See the [Alternative Approaches for Chunking section](#alternative-approaches-for-chunking) for details. -* What chunk size should be chosen? - - - - - - - - -### Adding a task into the tree - -Consider having an empty binary task tree. -The first added task replaces the head of the tree. The second task is inserted as a left subtask of the head. The right subtask of the tree is always ``nullptr`` to support the tree structure described above. - -Starting from the third inserted task, the tasks are inserted into the left or right subtree containing less number of items. - -The sequence diagram of the insertion is shown below: - -```mermaid -flowchart TD - - I[insert new_task into the tree] --> head_check[head == nullptr?] - head_check --> |Yes| head_insert[head = new_task] - head_check --> |No| subtree_insert[subtree = head->left] - subtree_insert --> subtree_check[subtree == nullptr?] - subtree_check --> |Yes| subtree_head_insert[subtree = new_task] - subtree_check --> |No| subtree_head_left_check[subtree->left == nullptr?] - subtree_head_left_check --> |Yes| subtree_head_left_insert[subtree->left = new_task] - subtree_head_left_check --> |No| subtree_head_right_check[subtree->right == new_task?] - subtree_head_right_check --> |Yes| subtree_head_right_insert[subtree->right = new_task] - subtree_head_right_check --> |No| subtree_head_size_check[subtree->left->num_items
<
subtree->right->num_items
?] - subtree_head_size_check --> |Yes| subtree_switch_left[subtree = subtree->left] - subtree_head_size_check --> |No| subtree_switch_right[subtree = subtree->right] - subtree_switch_left --> subtree_head_left_check - subtree_switch_right --> subtree_head_left_check - head_insert --> exit - subtree_head_insert --> exit - subtree_head_left_insert --> exit - subtree_head_right_insert --> exit -``` \ No newline at end of file From 42a5cfcc4ef87f3fa8a054004a214877fc6b0136 Mon Sep 17 00:00:00 2001 From: "Boyarinov, Konstantin" Date: Fri, 12 Dec 2025 14:11:30 +0200 Subject: [PATCH 5/8] Remove unnecessary assets --- .../aggregating_group_aggregating_task.png | Bin 24379 -> 0 bytes .../assets/aggregating_group_chunking.png | Bin 10967 -> 0 bytes .../aggregating_group_consumer_thread.png | Bin 26927 -> 0 bytes .../assets/aggregating_group_first_run.png | Bin 13819 -> 0 bytes .../assets/aggregating_group_further_runs.png | Bin 15238 -> 0 bytes .../assets/aggregating_group_process_chunks.png | 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z|A6Y}EG~D+`JagZX_@}7j6aYH{Qt}NKY?3pk0r{rU0Hnw{|0j`;=O!sAEQ|ywn71w6BN2zxWd66|m1By4)J+DIMCDj)b1-ho z#f7(H!6%@gBiUv+*UPpU?#`{2E;%VEBI099XS#QmnKzUCmh~O!{W>AJxm7?>B3HYU zfVc4SX(Y$`$jD*3vro!PQ_#`~B$B!mxPOiXQYbCq2Xm72$7XX?HKxBz1~iaWHOYW} zOBQv`209=#O4vvxH2Yq6_TbiV1PV5Fm-l;FXnJ5EXD%XQH(|21wN+OhbuiyDC&{2A zGQ#O@@XT| From 3d6ba6e2572cd40eafc5109c57a236cb07bcb41a Mon Sep 17 00:00:00 2001 From: "Boyarinov, Konstantin" Date: Fri, 12 Dec 2025 14:55:45 +0200 Subject: [PATCH 6/8] Minor improvements --- rfcs/proposed/task_aggregation/README.md | 28 ++++++------------ .../assets/perf_scalability.png | Bin 32536 -> 32685 bytes 2 files changed, 9 insertions(+), 19 deletions(-) diff --git a/rfcs/proposed/task_aggregation/README.md b/rfcs/proposed/task_aggregation/README.md index 87c7e4de63..ea3ef144fe 100644 --- a/rfcs/proposed/task_aggregation/README.md +++ b/rfcs/proposed/task_aggregation/README.md @@ -161,18 +161,8 @@ Revisit the producer-consumer example using *aggregating-task-group* instead of When the *aggregating-task-group* is created, the producer's task tree is empty. -The first submitted task is appended and because the tree was empty, a *grab-task* is spawned (added to the local task pool). - -Subsequent tasks are appended without spawning until the *grab-task* executes. -When a worker steals and executes the *grab-task*, it grabs the tree. - -From that moment, the producer's tree is empty again, so subsequent ``run`` call spawn additional *grab-task*. - -The consumer checks whether the grabbed tree is divisible; if so, it splits the tree into halves, spawns a task for the -second subtree, and re-executes the head to continue dividing until the desired grainsize is reached. - -Assume a grainsize of 150 tasks. The producer appended 500 tasks into the task tree before the consumer thread 1 came -and grabbed them. Thread 1 splits the tree into two halves (250 tasks each) and spawns the head of the second half into +The producer appended 500 tasks into the task tree before the consumer thread 1 came and grabbed them. +Thread 1 splits the tree into two halves (250 tasks each) and spawns the head of the second half into its local pool. Thread 2 steals that task, splits the subtree into two halves (125 each), and spawns the second half to its own local pool. @@ -207,7 +197,7 @@ In *aggregating-task-group*, it is addressed by adding per-task reference counte 3. Its right child (if any) Before spawning, the *grab-task* holds a reference in a producer's ``thread_reference_vertex``. After execution and grab, -it replaces itself with the head of the grabbed tree in the waiting tree. +it replaces itself in the waiting tree with the head of the grabbed tree. Each task's lifetime extends until its reference count reaches zero. @@ -324,7 +314,7 @@ flowchart TD try_grab --> |Success| exit_grab["Exit to tree distribution"] try_grab --> |Fail| spin_while_locked - add_task[add_task
producer] --> lock_tree["tree = head.exchange(nullptr)"] + add_task[add_task
producer] --> lock_tree["tree = head.exchange(locked)"] lock_tree --> insert["do insert"] insert --> unlock_tree["head.exchange(tree)"] unlock_tree --> exit_producer["Exit"] @@ -335,7 +325,7 @@ flowchart TD Consider an empty binary task tree. The first added task becomes the head. The second task is inserted as the head's left child. The right child of the head is always ``nullptr`` to maintain the structure described above. -From the third task onward, insert into the subtree (left or right) with fewer items. +From the third task onward, the algorithm inserts into the subtree (left or right) with fewer items. ```mermaid flowchart TD @@ -344,7 +334,7 @@ flowchart TD head_check --> |Yes| head_insert[head = new_task] head_check --> |No| subtree_insert[subtree = head->left] subtree_insert --> subtree_check[subtree == nullptr?] - subtree_check --> |Yes| subtree_head_insert[subtree = new_task] + subtree_check --> |Yes| subtree_head_insert[head->left = new_task] subtree_check --> |No| subtree_head_left_check[subtree->left == nullptr?] subtree_head_left_check --> |Yes| subtree_head_left_insert[subtree->left = new_task] subtree_head_left_check --> |No| subtree_head_right_check[subtree->right == new_task?] @@ -364,13 +354,13 @@ flowchart TD As noted above, each node maintains pointer to left/right children and to its parent. -This document proposes requiring the grainsize to be >= 4 to simplify splitting. +This document proposes requiring the grainsize to be $>= 4$ to simplify splitting. Splitting occurs only after a grab; thus, the splitting thread exclusively owns the tree, and no synchronization is needed. -Only the first two layers of the tree are needed for splitting. Let's call them ``head`` and ``next``. Splitting is done by setting -``head->left = next->right``, then ``next->right = nullptr``, and update counters accordingly. +Only the first two layers of the tree are needed for splitting. Let's call them ``head`` and ``next``. +Splitting is done by setting ``head->left = next->right``, then ``next->right = nullptr``, and update counters accordingly. Parent pointers are not modified so the waiting tree remains intact for reference counting. 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