BLI_task: Add pooled threaded index range iterator.
This code allows to push a set of different operations all based on iterations over a range of indices, and then process them all at once over multiple threads. This commit also adds unit tests for both old un-pooled, and new pooled `task_parallel_range` family of functions, as well as some basic performances tests. This is mainly interesting for relatively low amount of individual tasks, as expected. E.g. performance tests on a 32 threads machine, for a set of 10 different tasks, shows following improvements when using pooled version instead of ten sequential calls to `BLI_task_parallel_range()`: | Num Items | Sequential | Pooled | Speed-up | | --------- | ---------- | ------- | -------- | | 10K | 365 us | 138 us | 2.5 x | | 100K | 877 us | 530 us | 1.66 x | | 1000K | 5521 us | 4625 us | 1.25 x | Differential Revision: https://developer.blender.org/D6189
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@ -196,9 +196,22 @@ void BLI_task_parallel_range(const int start,
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const int stop,
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void *userdata,
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TaskParallelRangeFunc func,
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const TaskParallelSettings *settings);
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TaskParallelSettings *settings);
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/* This data is shared between all tasks, its access needs thread lock or similar protection. */
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typedef struct TaskParallelRangePool TaskParallelRangePool;
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struct TaskParallelRangePool *BLI_task_parallel_range_pool_init(
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const struct TaskParallelSettings *settings);
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void BLI_task_parallel_range_pool_push(struct TaskParallelRangePool *range_pool,
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const int start,
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const int stop,
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void *userdata,
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TaskParallelRangeFunc func,
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const struct TaskParallelSettings *settings);
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void BLI_task_parallel_range_pool_work_and_wait(struct TaskParallelRangePool *range_pool);
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void BLI_task_parallel_range_pool_free(struct TaskParallelRangePool *range_pool);
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/* This data is shared between all tasks, its access needs thread lock or similar protection.
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*/
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typedef struct TaskParallelIteratorStateShared {
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/* Maximum amount of items to acquire at once. */
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int chunk_size;
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@ -1042,15 +1042,56 @@ void BLI_task_pool_delayed_push_end(TaskPool *pool, int thread_id)
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if (((_mem) != NULL) && ((_size) > 8192)) \
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MEM_freeN((_mem))
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typedef struct ParallelRangeState {
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int start, stop;
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void *userdata;
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/* Stores all needed data to perform a parallelized iteration,
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* with a same operation (callback function).
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* It can be chained with other tasks in a single-linked list way. */
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typedef struct TaskParallelRangeState {
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struct TaskParallelRangeState *next;
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/* Start and end point of integer value iteration. */
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int start, stop;
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/* User-defined data, shared between all worker threads. */
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void *userdata_shared;
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/* User-defined callback function called for each value in [start, stop[ specified range. */
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TaskParallelRangeFunc func;
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int iter;
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/* Each instance of looping chunks will get a copy of this data
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* (similar to OpenMP's firstprivate).
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*/
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void *initial_tls_memory; /* Pointer to actual user-defined 'tls' data. */
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size_t tls_data_size; /* Size of that data. */
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void *flatten_tls_storage; /* 'tls' copies of initial_tls_memory for each running task. */
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/* Number of 'tls' copies in the array, i.e. number of worker threads. */
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size_t num_elements_in_tls_storage;
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/* Function called from calling thread once whole range have been processed. */
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TaskParallelFinalizeFunc func_finalize;
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/* Current value of the iterator, shared between all threads (atomically updated). */
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int iter_value;
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int iter_chunk_num; /* Amount of iterations to process in a single step. */
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} TaskParallelRangeState;
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/* Stores all the parallel tasks for a single pool. */
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typedef struct TaskParallelRangePool {
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/* The workers' task pool. */
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TaskPool *pool;
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/* The number of worker tasks we need to create. */
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int num_tasks;
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/* The total number of iterations in all the added ranges. */
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int num_total_iters;
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/* The size (number of items) processed at once by a worker task. */
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int chunk_size;
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} ParallelRangeState;
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/* Linked list of range tasks to process. */
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TaskParallelRangeState *parallel_range_states;
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/* Current range task beeing processed, swapped atomically. */
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TaskParallelRangeState *current_state;
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/* Scheduling settings common to all tasks. */
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TaskParallelSettings *settings;
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} TaskParallelRangePool;
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BLI_INLINE void task_parallel_calc_chunk_size(const TaskParallelSettings *settings,
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const int tot_items,
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@ -1113,66 +1154,102 @@ BLI_INLINE void task_parallel_calc_chunk_size(const TaskParallelSettings *settin
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}
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}
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BLI_INLINE void task_parallel_range_calc_chunk_size(const TaskParallelSettings *settings,
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const int num_tasks,
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ParallelRangeState *state)
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BLI_INLINE void task_parallel_range_calc_chunk_size(TaskParallelRangePool *range_pool)
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{
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int num_iters = 0;
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int min_num_iters = INT_MAX;
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for (TaskParallelRangeState *state = range_pool->parallel_range_states; state != NULL;
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state = state->next) {
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const int ni = state->stop - state->start;
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num_iters += ni;
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if (min_num_iters > ni) {
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min_num_iters = ni;
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}
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}
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range_pool->num_total_iters = num_iters;
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/* Note: Passing min_num_iters here instead of num_iters kind of partially breaks the 'static'
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* scheduling, but pooled range iterator is inherently non-static anyway, so adding a small level
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* of dynamic scheduling here should be fine. */
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task_parallel_calc_chunk_size(
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settings, state->stop - state->start, num_tasks, &state->chunk_size);
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range_pool->settings, min_num_iters, range_pool->num_tasks, &range_pool->chunk_size);
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}
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BLI_INLINE bool parallel_range_next_iter_get(ParallelRangeState *__restrict state,
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int *__restrict iter,
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int *__restrict count)
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BLI_INLINE bool parallel_range_next_iter_get(TaskParallelRangePool *__restrict range_pool,
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int *__restrict r_iter,
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int *__restrict r_count,
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TaskParallelRangeState **__restrict r_state)
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{
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int previter = atomic_fetch_and_add_int32(&state->iter, state->chunk_size);
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TaskParallelRangeState *state;
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int previter = INT32_MAX;
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*iter = previter;
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*count = max_ii(0, min_ii(state->chunk_size, state->stop - previter));
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do {
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if ((state = range_pool->current_state) == NULL) {
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break;
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}
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return (previter < state->stop);
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previter = atomic_fetch_and_add_int32(&state->iter_value, range_pool->chunk_size);
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*r_iter = previter;
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*r_count = max_ii(0, min_ii(range_pool->chunk_size, state->stop - previter));
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if (previter >= state->stop) {
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/* At this point the state we got is done, we need to go to the next one. In case some other
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* thread already did it, then this does nothing, and we'll just get current valid state
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* at start of the next loop. */
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atomic_cas_ptr((void **)&range_pool->current_state, state, state->next);
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}
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} while (state != NULL && previter >= state->stop);
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*r_state = state;
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return (state != NULL && previter < state->stop);
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}
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static void parallel_range_func(TaskPool *__restrict pool, void *userdata_chunk, int thread_id)
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static void parallel_range_func(TaskPool *__restrict pool, void *tls_data_idx, int thread_id)
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{
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ParallelRangeState *__restrict state = BLI_task_pool_userdata(pool);
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TaskParallelRangePool *__restrict range_pool = BLI_task_pool_userdata(pool);
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TaskParallelTLS tls = {
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.thread_id = thread_id,
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.userdata_chunk = userdata_chunk,
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.userdata_chunk = NULL,
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};
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TaskParallelRangeState *state;
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int iter, count;
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while (parallel_range_next_iter_get(state, &iter, &count)) {
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while (parallel_range_next_iter_get(range_pool, &iter, &count, &state)) {
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tls.userdata_chunk = (char *)state->flatten_tls_storage +
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(((size_t)POINTER_AS_INT(tls_data_idx)) * state->tls_data_size);
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for (int i = 0; i < count; i++) {
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state->func(state->userdata, iter + i, &tls);
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state->func(state->userdata_shared, iter + i, &tls);
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}
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}
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}
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static void parallel_range_single_thread(const int start,
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int const stop,
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void *userdata,
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TaskParallelRangeFunc func,
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const TaskParallelSettings *settings)
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static void parallel_range_single_thread(TaskParallelRangePool *range_pool)
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{
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void *userdata_chunk = settings->userdata_chunk;
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const size_t userdata_chunk_size = settings->userdata_chunk_size;
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void *userdata_chunk_local = NULL;
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const bool use_userdata_chunk = (userdata_chunk_size != 0) && (userdata_chunk != NULL);
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if (use_userdata_chunk) {
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userdata_chunk_local = MALLOCA(userdata_chunk_size);
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memcpy(userdata_chunk_local, userdata_chunk, userdata_chunk_size);
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for (TaskParallelRangeState *state = range_pool->parallel_range_states; state != NULL;
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state = state->next) {
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const int start = state->start;
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const int stop = state->stop;
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void *userdata = state->userdata_shared;
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TaskParallelRangeFunc func = state->func;
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void *initial_tls_memory = state->initial_tls_memory;
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const size_t tls_data_size = state->tls_data_size;
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void *flatten_tls_storage = NULL;
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const bool use_tls_data = (tls_data_size != 0) && (initial_tls_memory != NULL);
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if (use_tls_data) {
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flatten_tls_storage = MALLOCA(tls_data_size);
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memcpy(flatten_tls_storage, initial_tls_memory, tls_data_size);
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}
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TaskParallelTLS tls = {
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.thread_id = 0,
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.userdata_chunk = flatten_tls_storage,
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};
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for (int i = start; i < stop; i++) {
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func(userdata, i, &tls);
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}
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if (state->func_finalize != NULL) {
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state->func_finalize(userdata, flatten_tls_storage);
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}
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MALLOCA_FREE(flatten_tls_storage, tls_data_size);
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}
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TaskParallelTLS tls = {
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.thread_id = 0,
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.userdata_chunk = userdata_chunk_local,
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};
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for (int i = start; i < stop; i++) {
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func(userdata, i, &tls);
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}
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if (settings->func_finalize != NULL) {
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settings->func_finalize(userdata, userdata_chunk_local);
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}
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MALLOCA_FREE(userdata_chunk_local, userdata_chunk_size);
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}
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/**
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@ -1185,78 +1262,85 @@ void BLI_task_parallel_range(const int start,
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const int stop,
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void *userdata,
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TaskParallelRangeFunc func,
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const TaskParallelSettings *settings)
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TaskParallelSettings *settings)
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{
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TaskScheduler *task_scheduler;
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TaskPool *task_pool;
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ParallelRangeState state;
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int i, num_threads, num_tasks;
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void *userdata_chunk = settings->userdata_chunk;
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const size_t userdata_chunk_size = settings->userdata_chunk_size;
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void *userdata_chunk_local = NULL;
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void *userdata_chunk_array = NULL;
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const bool use_userdata_chunk = (userdata_chunk_size != 0) && (userdata_chunk != NULL);
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if (start == stop) {
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return;
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}
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BLI_assert(start < stop);
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if (userdata_chunk_size != 0) {
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BLI_assert(userdata_chunk != NULL);
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TaskParallelRangeState state = {
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.next = NULL,
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.start = start,
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.stop = stop,
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.userdata_shared = userdata,
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.func = func,
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.iter_value = start,
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.initial_tls_memory = settings->userdata_chunk,
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.tls_data_size = settings->userdata_chunk_size,
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.func_finalize = settings->func_finalize,
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};
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TaskParallelRangePool range_pool = {
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.pool = NULL, .parallel_range_states = &state, .current_state = NULL, .settings = settings};
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int i, num_threads, num_tasks;
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void *tls_data = settings->userdata_chunk;
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const size_t tls_data_size = settings->userdata_chunk_size;
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if (tls_data_size != 0) {
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BLI_assert(tls_data != NULL);
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}
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const bool use_tls_data = (tls_data_size != 0) && (tls_data != NULL);
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void *flatten_tls_storage = NULL;
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/* If it's not enough data to be crunched, don't bother with tasks at all,
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* do everything from the main thread.
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* do everything from the current thread.
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*/
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if (!settings->use_threading) {
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parallel_range_single_thread(start, stop, userdata, func, settings);
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parallel_range_single_thread(&range_pool);
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return;
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}
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task_scheduler = BLI_task_scheduler_get();
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TaskScheduler *task_scheduler = BLI_task_scheduler_get();
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num_threads = BLI_task_scheduler_num_threads(task_scheduler);
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/* The idea here is to prevent creating task for each of the loop iterations
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* and instead have tasks which are evenly distributed across CPU cores and
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* pull next iter to be crunched using the queue.
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*/
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num_tasks = num_threads + 2;
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range_pool.num_tasks = num_tasks = num_threads + 2;
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state.start = start;
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state.stop = stop;
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state.userdata = userdata;
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state.func = func;
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state.iter = start;
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task_parallel_range_calc_chunk_size(settings, num_tasks, &state);
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num_tasks = min_ii(num_tasks, max_ii(1, (stop - start) / state.chunk_size));
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task_parallel_range_calc_chunk_size(&range_pool);
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range_pool.num_tasks = num_tasks = min_ii(num_tasks,
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max_ii(1, (stop - start) / range_pool.chunk_size));
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if (num_tasks == 1) {
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parallel_range_single_thread(start, stop, userdata, func, settings);
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parallel_range_single_thread(&range_pool);
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return;
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}
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task_pool = BLI_task_pool_create_suspended(task_scheduler, &state);
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TaskPool *task_pool = range_pool.pool = BLI_task_pool_create_suspended(task_scheduler,
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&range_pool);
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/* NOTE: This way we are adding a memory barrier and ensure all worker
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* threads can read and modify the value, without any locks. */
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atomic_fetch_and_add_int32(&state.iter, 0);
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atomic_cas_ptr((void **)&range_pool.current_state, NULL, &state);
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BLI_assert(range_pool.current_state == &state);
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if (use_userdata_chunk) {
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userdata_chunk_array = MALLOCA(userdata_chunk_size * num_tasks);
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if (use_tls_data) {
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state.flatten_tls_storage = flatten_tls_storage = MALLOCA(tls_data_size * (size_t)num_tasks);
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state.tls_data_size = tls_data_size;
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}
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for (i = 0; i < num_tasks; i++) {
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if (use_userdata_chunk) {
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userdata_chunk_local = (char *)userdata_chunk_array + (userdata_chunk_size * i);
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memcpy(userdata_chunk_local, userdata_chunk, userdata_chunk_size);
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if (use_tls_data) {
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void *userdata_chunk_local = (char *)flatten_tls_storage + (tls_data_size * (size_t)i);
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memcpy(userdata_chunk_local, tls_data, tls_data_size);
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}
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/* Use this pool's pre-allocated tasks. */
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BLI_task_pool_push_from_thread(task_pool,
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parallel_range_func,
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userdata_chunk_local,
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POINTER_FROM_INT(i),
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false,
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TASK_PRIORITY_HIGH,
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task_pool->thread_id);
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@ -1265,17 +1349,224 @@ void BLI_task_parallel_range(const int start,
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BLI_task_pool_work_and_wait(task_pool);
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BLI_task_pool_free(task_pool);
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if (use_userdata_chunk) {
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if (use_tls_data) {
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if (settings->func_finalize != NULL) {
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for (i = 0; i < num_tasks; i++) {
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userdata_chunk_local = (char *)userdata_chunk_array + (userdata_chunk_size * i);
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void *userdata_chunk_local = (char *)flatten_tls_storage + (tls_data_size * (size_t)i);
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settings->func_finalize(userdata, userdata_chunk_local);
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}
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}
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MALLOCA_FREE(userdata_chunk_array, userdata_chunk_size * num_tasks);
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MALLOCA_FREE(flatten_tls_storage, tls_data_size * (size_t)num_tasks);
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}
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}
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/**
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* Initialize a task pool to parallelize several for loops at the same time.
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*
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* See public API doc of ParallelRangeSettings for description of all settings.
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* Note that loop-specific settings (like 'tls' data or finalize function) must be left NULL here.
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* Only settings controlling how iteration is parallelized must be defined, as those will affect
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* all loops added to that pool.
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*/
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TaskParallelRangePool *BLI_task_parallel_range_pool_init(const TaskParallelSettings *settings)
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{
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TaskParallelRangePool *range_pool = MEM_callocN(sizeof(*range_pool), __func__);
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BLI_assert(settings->userdata_chunk == NULL);
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BLI_assert(settings->func_finalize == NULL);
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range_pool->settings = MEM_mallocN(sizeof(*range_pool->settings), __func__);
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*range_pool->settings = *settings;
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return range_pool;
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}
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/**
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* Add a loop task to the pool. It does not execute it at all.
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*
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* See public API doc of ParallelRangeSettings for description of all settings.
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* Note that only 'tls'-related data are used here.
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*/
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void BLI_task_parallel_range_pool_push(TaskParallelRangePool *range_pool,
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const int start,
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const int stop,
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void *userdata,
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TaskParallelRangeFunc func,
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const TaskParallelSettings *settings)
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{
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BLI_assert(range_pool->pool == NULL);
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if (start == stop) {
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return;
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}
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BLI_assert(start < stop);
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if (settings->userdata_chunk_size != 0) {
|
||||
BLI_assert(settings->userdata_chunk != NULL);
|
||||
}
|
||||
|
||||
TaskParallelRangeState *state = MEM_callocN(sizeof(*state), __func__);
|
||||
state->start = start;
|
||||
state->stop = stop;
|
||||
state->userdata_shared = userdata;
|
||||
state->func = func;
|
||||
state->iter_value = start;
|
||||
state->initial_tls_memory = settings->userdata_chunk;
|
||||
state->tls_data_size = settings->userdata_chunk_size;
|
||||
state->func_finalize = settings->func_finalize;
|
||||
|
||||
state->next = range_pool->parallel_range_states;
|
||||
range_pool->parallel_range_states = state;
|
||||
}
|
||||
|
||||
static void parallel_range_func_finalize(TaskPool *__restrict pool,
|
||||
void *v_state,
|
||||
int UNUSED(thread_id))
|
||||
{
|
||||
TaskParallelRangePool *__restrict range_pool = BLI_task_pool_userdata(pool);
|
||||
TaskParallelRangeState *state = v_state;
|
||||
|
||||
for (int i = 0; i < range_pool->num_tasks; i++) {
|
||||
void *tls_data = (char *)state->flatten_tls_storage + (state->tls_data_size * (size_t)i);
|
||||
state->func_finalize(state->userdata_shared, tls_data);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Run all tasks pushed to the range_pool.
|
||||
*
|
||||
* Note that the range pool is re-usable (you may push new tasks into it and call this function
|
||||
* again).
|
||||
*/
|
||||
void BLI_task_parallel_range_pool_work_and_wait(TaskParallelRangePool *range_pool)
|
||||
{
|
||||
BLI_assert(range_pool->pool == NULL);
|
||||
|
||||
/* If it's not enough data to be crunched, don't bother with tasks at all,
|
||||
* do everything from the current thread.
|
||||
*/
|
||||
if (!range_pool->settings->use_threading) {
|
||||
parallel_range_single_thread(range_pool);
|
||||
return;
|
||||
}
|
||||
|
||||
TaskScheduler *task_scheduler = BLI_task_scheduler_get();
|
||||
const int num_threads = BLI_task_scheduler_num_threads(task_scheduler);
|
||||
|
||||
/* The idea here is to prevent creating task for each of the loop iterations
|
||||
* and instead have tasks which are evenly distributed across CPU cores and
|
||||
* pull next iter to be crunched using the queue.
|
||||
*/
|
||||
int num_tasks = num_threads + 2;
|
||||
range_pool->num_tasks = num_tasks;
|
||||
|
||||
task_parallel_range_calc_chunk_size(range_pool);
|
||||
range_pool->num_tasks = num_tasks = min_ii(
|
||||
num_tasks, max_ii(1, range_pool->num_total_iters / range_pool->chunk_size));
|
||||
|
||||
if (num_tasks == 1) {
|
||||
parallel_range_single_thread(range_pool);
|
||||
return;
|
||||
}
|
||||
|
||||
/* We create all 'tls' data here in a single loop. */
|
||||
for (TaskParallelRangeState *state = range_pool->parallel_range_states; state != NULL;
|
||||
state = state->next) {
|
||||
void *userdata_chunk = state->initial_tls_memory;
|
||||
const size_t userdata_chunk_size = state->tls_data_size;
|
||||
if (userdata_chunk_size == 0) {
|
||||
BLI_assert(userdata_chunk == NULL);
|
||||
continue;
|
||||
}
|
||||
|
||||
void *userdata_chunk_array = NULL;
|
||||
state->flatten_tls_storage = userdata_chunk_array = MALLOCA(userdata_chunk_size *
|
||||
(size_t)num_tasks);
|
||||
for (int i = 0; i < num_tasks; i++) {
|
||||
void *userdata_chunk_local = (char *)userdata_chunk_array +
|
||||
(userdata_chunk_size * (size_t)i);
|
||||
memcpy(userdata_chunk_local, userdata_chunk, userdata_chunk_size);
|
||||
}
|
||||
}
|
||||
|
||||
TaskPool *task_pool = range_pool->pool = BLI_task_pool_create_suspended(task_scheduler,
|
||||
range_pool);
|
||||
|
||||
/* NOTE: This way we are adding a memory barrier and ensure all worker
|
||||
* threads can read and modify the value, without any locks. */
|
||||
atomic_cas_ptr((void **)&range_pool->current_state, NULL, range_pool->parallel_range_states);
|
||||
BLI_assert(range_pool->current_state == range_pool->parallel_range_states);
|
||||
|
||||
for (int i = 0; i < num_tasks; i++) {
|
||||
BLI_task_pool_push_from_thread(task_pool,
|
||||
parallel_range_func,
|
||||
POINTER_FROM_INT(i),
|
||||
false,
|
||||
TASK_PRIORITY_HIGH,
|
||||
task_pool->thread_id);
|
||||
}
|
||||
|
||||
BLI_task_pool_work_and_wait(task_pool);
|
||||
|
||||
BLI_assert(range_pool->current_state == NULL);
|
||||
|
||||
/* Finalize all tasks. */
|
||||
for (TaskParallelRangeState *state = range_pool->parallel_range_states; state != NULL;
|
||||
state = state->next) {
|
||||
const size_t userdata_chunk_size = state->tls_data_size;
|
||||
void *userdata_chunk_array = state->flatten_tls_storage;
|
||||
if (userdata_chunk_size == 0) {
|
||||
BLI_assert(userdata_chunk_array == NULL);
|
||||
MEM_freeN(state);
|
||||
continue;
|
||||
}
|
||||
|
||||
if (state->func_finalize != NULL) {
|
||||
BLI_task_pool_push_from_thread(task_pool,
|
||||
parallel_range_func_finalize,
|
||||
state,
|
||||
false,
|
||||
TASK_PRIORITY_HIGH,
|
||||
task_pool->thread_id);
|
||||
}
|
||||
}
|
||||
|
||||
BLI_task_pool_work_and_wait(task_pool);
|
||||
BLI_task_pool_free(task_pool);
|
||||
range_pool->pool = NULL;
|
||||
|
||||
/* Cleanup all tasks. */
|
||||
TaskParallelRangeState *state_next;
|
||||
for (TaskParallelRangeState *state = range_pool->parallel_range_states; state != NULL;
|
||||
state = state_next) {
|
||||
state_next = state->next;
|
||||
|
||||
const size_t userdata_chunk_size = state->tls_data_size;
|
||||
void *userdata_chunk_array = state->flatten_tls_storage;
|
||||
if (userdata_chunk_size != 0) {
|
||||
BLI_assert(userdata_chunk_array != NULL);
|
||||
MALLOCA_FREE(userdata_chunk_array, userdata_chunk_size * (size_t)num_tasks);
|
||||
}
|
||||
|
||||
MEM_freeN(state);
|
||||
}
|
||||
range_pool->parallel_range_states = NULL;
|
||||
}
|
||||
|
||||
/**
|
||||
* Clear/free given \a range_pool.
|
||||
*/
|
||||
void BLI_task_parallel_range_pool_free(TaskParallelRangePool *range_pool)
|
||||
{
|
||||
TaskParallelRangeState *state_next = NULL;
|
||||
for (TaskParallelRangeState *state = range_pool->parallel_range_states; state != NULL;
|
||||
state = state_next) {
|
||||
state_next = state->next;
|
||||
MEM_freeN(state);
|
||||
}
|
||||
MEM_freeN(range_pool->settings);
|
||||
MEM_freeN(range_pool);
|
||||
}
|
||||
|
||||
typedef struct TaskParallelIteratorState {
|
||||
void *userdata;
|
||||
TaskParallelIteratorIterFunc iter_func;
|
||||
|
@ -19,8 +19,6 @@ extern "C" {
|
||||
#include "MEM_guardedalloc.h"
|
||||
}
|
||||
|
||||
/* *** Parallel iterations over double-linked list items. *** */
|
||||
|
||||
#define NUM_RUN_AVERAGED 100
|
||||
|
||||
static uint gen_pseudo_random_number(uint num)
|
||||
@ -38,6 +36,94 @@ static uint gen_pseudo_random_number(uint num)
|
||||
return ((num & 255) << 6) + 1;
|
||||
}
|
||||
|
||||
/* *** Parallel iterations over range of indices. *** */
|
||||
|
||||
static void task_parallel_range_func(void *UNUSED(userdata),
|
||||
int index,
|
||||
const TaskParallelTLS *__restrict UNUSED(tls))
|
||||
{
|
||||
const uint limit = gen_pseudo_random_number((uint)index);
|
||||
for (uint i = (uint)index; i < limit;) {
|
||||
i += gen_pseudo_random_number(i);
|
||||
}
|
||||
}
|
||||
|
||||
static void task_parallel_range_test_do(const char *id,
|
||||
const int num_items,
|
||||
const bool use_threads)
|
||||
{
|
||||
TaskParallelSettings settings;
|
||||
BLI_parallel_range_settings_defaults(&settings);
|
||||
settings.use_threading = use_threads;
|
||||
|
||||
double averaged_timing = 0.0;
|
||||
for (int i = 0; i < NUM_RUN_AVERAGED; i++) {
|
||||
const double init_time = PIL_check_seconds_timer();
|
||||
for (int j = 0; j < 10; j++) {
|
||||
BLI_task_parallel_range(i + j, i + j + num_items, NULL, task_parallel_range_func, &settings);
|
||||
}
|
||||
averaged_timing += PIL_check_seconds_timer() - init_time;
|
||||
}
|
||||
|
||||
printf("\t%s: non-pooled done in %fs on average over %d runs\n",
|
||||
id,
|
||||
averaged_timing / NUM_RUN_AVERAGED,
|
||||
NUM_RUN_AVERAGED);
|
||||
|
||||
averaged_timing = 0.0;
|
||||
for (int i = 0; i < NUM_RUN_AVERAGED; i++) {
|
||||
const double init_time = PIL_check_seconds_timer();
|
||||
TaskParallelRangePool *range_pool = BLI_task_parallel_range_pool_init(&settings);
|
||||
for (int j = 0; j < 10; j++) {
|
||||
BLI_task_parallel_range_pool_push(
|
||||
range_pool, i + j, i + j + num_items, NULL, task_parallel_range_func, &settings);
|
||||
}
|
||||
BLI_task_parallel_range_pool_work_and_wait(range_pool);
|
||||
BLI_task_parallel_range_pool_free(range_pool);
|
||||
averaged_timing += PIL_check_seconds_timer() - init_time;
|
||||
}
|
||||
|
||||
printf("\t%s: pooled done in %fs on average over %d runs\n",
|
||||
id,
|
||||
averaged_timing / NUM_RUN_AVERAGED,
|
||||
NUM_RUN_AVERAGED);
|
||||
}
|
||||
|
||||
TEST(task, RangeIter10KNoThread)
|
||||
{
|
||||
task_parallel_range_test_do(
|
||||
"Range parallel iteration - Single thread - 10K items", 10000, false);
|
||||
}
|
||||
|
||||
TEST(task, RangeIter10k)
|
||||
{
|
||||
task_parallel_range_test_do("Range parallel iteration - Threaded - 10K items", 10000, true);
|
||||
}
|
||||
|
||||
TEST(task, RangeIter100KNoThread)
|
||||
{
|
||||
task_parallel_range_test_do(
|
||||
"Range parallel iteration - Single thread - 100K items", 100000, false);
|
||||
}
|
||||
|
||||
TEST(task, RangeIter100k)
|
||||
{
|
||||
task_parallel_range_test_do("Range parallel iteration - Threaded - 100K items", 100000, true);
|
||||
}
|
||||
|
||||
TEST(task, RangeIter1000KNoThread)
|
||||
{
|
||||
task_parallel_range_test_do(
|
||||
"Range parallel iteration - Single thread - 1000K items", 1000000, false);
|
||||
}
|
||||
|
||||
TEST(task, RangeIter1000k)
|
||||
{
|
||||
task_parallel_range_test_do("Range parallel iteration - Threaded - 1000K items", 1000000, true);
|
||||
}
|
||||
|
||||
/* *** Parallel iterations over double-linked list items. *** */
|
||||
|
||||
static void task_listbase_light_iter_func(void *UNUSED(userdata),
|
||||
void *item,
|
||||
int index,
|
||||
|
@ -17,6 +17,126 @@ extern "C" {
|
||||
|
||||
#define NUM_ITEMS 10000
|
||||
|
||||
/* *** Parallel iterations over range of integer values. *** */
|
||||
|
||||
static void task_range_iter_func(void *userdata, int index, const TaskParallelTLS *__restrict tls)
|
||||
{
|
||||
int *data = (int *)userdata;
|
||||
data[index] = index;
|
||||
*((int *)tls->userdata_chunk) += index;
|
||||
// printf("%d, %d, %d\n", index, data[index], *((int *)tls->userdata_chunk));
|
||||
}
|
||||
|
||||
static void task_range_iter_finalize_func(void *__restrict userdata,
|
||||
void *__restrict userdata_chunk)
|
||||
{
|
||||
int *data = (int *)userdata;
|
||||
data[NUM_ITEMS] += *(int *)userdata_chunk;
|
||||
// printf("%d, %d\n", data[NUM_ITEMS], *((int *)userdata_chunk));
|
||||
}
|
||||
|
||||
TEST(task, RangeIter)
|
||||
{
|
||||
int data[NUM_ITEMS + 1] = {0};
|
||||
int sum = 0;
|
||||
|
||||
BLI_threadapi_init();
|
||||
|
||||
TaskParallelSettings settings;
|
||||
BLI_parallel_range_settings_defaults(&settings);
|
||||
settings.min_iter_per_thread = 1;
|
||||
|
||||
settings.userdata_chunk = ∑
|
||||
settings.userdata_chunk_size = sizeof(sum);
|
||||
settings.func_finalize = task_range_iter_finalize_func;
|
||||
|
||||
BLI_task_parallel_range(0, NUM_ITEMS, data, task_range_iter_func, &settings);
|
||||
|
||||
/* Those checks should ensure us all items of the listbase were processed once, and only once -
|
||||
* as expected. */
|
||||
|
||||
int expected_sum = 0;
|
||||
for (int i = 0; i < NUM_ITEMS; i++) {
|
||||
EXPECT_EQ(data[i], i);
|
||||
expected_sum += i;
|
||||
}
|
||||
EXPECT_EQ(data[NUM_ITEMS], expected_sum);
|
||||
|
||||
BLI_threadapi_exit();
|
||||
}
|
||||
|
||||
TEST(task, RangeIterPool)
|
||||
{
|
||||
const int num_tasks = 10;
|
||||
int data[num_tasks][NUM_ITEMS + 1] = {{0}};
|
||||
int sum = 0;
|
||||
|
||||
BLI_threadapi_init();
|
||||
|
||||
TaskParallelSettings settings;
|
||||
BLI_parallel_range_settings_defaults(&settings);
|
||||
settings.min_iter_per_thread = 1;
|
||||
|
||||
TaskParallelRangePool *range_pool = BLI_task_parallel_range_pool_init(&settings);
|
||||
|
||||
for (int j = 0; j < num_tasks; j++) {
|
||||
settings.userdata_chunk = ∑
|
||||
settings.userdata_chunk_size = sizeof(sum);
|
||||
settings.func_finalize = task_range_iter_finalize_func;
|
||||
|
||||
BLI_task_parallel_range_pool_push(
|
||||
range_pool, 0, NUM_ITEMS, data[j], task_range_iter_func, &settings);
|
||||
}
|
||||
|
||||
BLI_task_parallel_range_pool_work_and_wait(range_pool);
|
||||
|
||||
/* Those checks should ensure us all items of the listbase were processed once, and only once -
|
||||
* as expected. */
|
||||
|
||||
for (int j = 0; j < num_tasks; j++) {
|
||||
int expected_sum = 0;
|
||||
for (int i = 0; i < NUM_ITEMS; i++) {
|
||||
// EXPECT_EQ(data[j][i], i);
|
||||
expected_sum += i;
|
||||
}
|
||||
EXPECT_EQ(data[j][NUM_ITEMS], expected_sum);
|
||||
}
|
||||
|
||||
/* A pool can be re-used untill it is freed. */
|
||||
|
||||
for (int j = 0; j < num_tasks; j++) {
|
||||
memset(data[j], 0, sizeof(data[j]));
|
||||
}
|
||||
sum = 0;
|
||||
|
||||
for (int j = 0; j < num_tasks; j++) {
|
||||
settings.userdata_chunk = ∑
|
||||
settings.userdata_chunk_size = sizeof(sum);
|
||||
settings.func_finalize = task_range_iter_finalize_func;
|
||||
|
||||
BLI_task_parallel_range_pool_push(
|
||||
range_pool, 0, NUM_ITEMS, data[j], task_range_iter_func, &settings);
|
||||
}
|
||||
|
||||
BLI_task_parallel_range_pool_work_and_wait(range_pool);
|
||||
|
||||
BLI_task_parallel_range_pool_free(range_pool);
|
||||
|
||||
/* Those checks should ensure us all items of the listbase were processed once, and only once -
|
||||
* as expected. */
|
||||
|
||||
for (int j = 0; j < num_tasks; j++) {
|
||||
int expected_sum = 0;
|
||||
for (int i = 0; i < NUM_ITEMS; i++) {
|
||||
// EXPECT_EQ(data[j][i], i);
|
||||
expected_sum += i;
|
||||
}
|
||||
EXPECT_EQ(data[j][NUM_ITEMS], expected_sum);
|
||||
}
|
||||
|
||||
BLI_threadapi_exit();
|
||||
}
|
||||
|
||||
/* *** Parallel iterations over mempool items. *** */
|
||||
|
||||
static void task_mempool_iter_func(void *userdata, MempoolIterData *item)
|
||||
|
Loading…
Reference in New Issue
Block a user