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Diffstat (limited to 'source/blender/blenlib/intern/task_iterator.c')
| -rw-r--r-- | source/blender/blenlib/intern/task_iterator.c | 423 |
1 files changed, 423 insertions, 0 deletions
diff --git a/source/blender/blenlib/intern/task_iterator.c b/source/blender/blenlib/intern/task_iterator.c new file mode 100644 index 00000000000..ee459ac2548 --- /dev/null +++ b/source/blender/blenlib/intern/task_iterator.c @@ -0,0 +1,423 @@ +/* + * This program is free software; you can redistribute it and/or + * modify it under the terms of the GNU General Public License + * as published by the Free Software Foundation; either version 2 + * of the License, or (at your option) any later version. + * + * This program is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU General Public License for more details. + * + * You should have received a copy of the GNU General Public License + * along with this program; if not, write to the Free Software Foundation, + * Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. + */ + +/** \file + * \ingroup bli + * + * Parallel tasks over all elements in a container. + */ + +#include <stdlib.h> + +#include "MEM_guardedalloc.h" + +#include "DNA_listBase.h" + +#include "BLI_listbase.h" +#include "BLI_math.h" +#include "BLI_mempool.h" +#include "BLI_task.h" +#include "BLI_threads.h" + +#include "atomic_ops.h" + +/* Allows to avoid using malloc for userdata_chunk in tasks, when small enough. */ +#define MALLOCA(_size) ((_size) <= 8192) ? alloca((_size)) : MEM_mallocN((_size), __func__) +#define MALLOCA_FREE(_mem, _size) \ + if (((_mem) != NULL) && ((_size) > 8192)) \ + MEM_freeN((_mem)) + +BLI_INLINE void task_parallel_calc_chunk_size(const TaskParallelSettings *settings, + const int tot_items, + int num_tasks, + int *r_chunk_size) +{ + int chunk_size = 0; + + if (!settings->use_threading) { + /* Some users of this helper will still need a valid chunk size in case processing is not + * threaded. We can use a bigger one than in default threaded case then. */ + chunk_size = 1024; + num_tasks = 1; + } + else if (settings->min_iter_per_thread > 0) { + /* Already set by user, no need to do anything here. */ + chunk_size = settings->min_iter_per_thread; + } + else { + /* Multiplier used in heuristics below to define "optimal" chunk size. + * The idea here is to increase the chunk size to compensate for a rather measurable threading + * overhead caused by fetching tasks. With too many CPU threads we are starting + * to spend too much time in those overheads. + * First values are: 1 if num_tasks < 16; + * else 2 if num_tasks < 32; + * else 3 if num_tasks < 48; + * else 4 if num_tasks < 64; + * etc. + * Note: If we wanted to keep the 'power of two' multiplier, we'd need something like: + * 1 << max_ii(0, (int)(sizeof(int) * 8) - 1 - bitscan_reverse_i(num_tasks) - 3) + */ + const int num_tasks_factor = max_ii(1, num_tasks >> 3); + + /* We could make that 'base' 32 number configurable in TaskParallelSettings too, or maybe just + * always use that heuristic using TaskParallelSettings.min_iter_per_thread as basis? */ + chunk_size = 32 * num_tasks_factor; + + /* Basic heuristic to avoid threading on low amount of items. + * We could make that limit configurable in settings too. */ + if (tot_items > 0 && tot_items < max_ii(256, chunk_size * 2)) { + chunk_size = tot_items; + } + } + + BLI_assert(chunk_size > 0); + *r_chunk_size = chunk_size; +} + +typedef struct TaskParallelIteratorState { + void *userdata; + TaskParallelIteratorIterFunc iter_func; + TaskParallelIteratorFunc func; + + /* *** Data used to 'acquire' chunks of items from the iterator. *** */ + /* Common data also passed to the generator callback. */ + TaskParallelIteratorStateShared iter_shared; + /* Total number of items. If unknown, set it to a negative number. */ + int tot_items; +} TaskParallelIteratorState; + +static void parallel_iterator_func_do(TaskParallelIteratorState *__restrict state, + void *userdata_chunk) +{ + TaskParallelTLS tls = { + .userdata_chunk = userdata_chunk, + }; + + void **current_chunk_items; + int *current_chunk_indices; + int current_chunk_size; + + const size_t items_size = sizeof(*current_chunk_items) * (size_t)state->iter_shared.chunk_size; + const size_t indices_size = sizeof(*current_chunk_indices) * + (size_t)state->iter_shared.chunk_size; + + current_chunk_items = MALLOCA(items_size); + current_chunk_indices = MALLOCA(indices_size); + current_chunk_size = 0; + + for (bool do_abort = false; !do_abort;) { + if (state->iter_shared.spin_lock != NULL) { + BLI_spin_lock(state->iter_shared.spin_lock); + } + + /* Get current status. */ + int index = state->iter_shared.next_index; + void *item = state->iter_shared.next_item; + int i; + + /* 'Acquire' a chunk of items from the iterator function. */ + for (i = 0; i < state->iter_shared.chunk_size && !state->iter_shared.is_finished; i++) { + current_chunk_indices[i] = index; + current_chunk_items[i] = item; + state->iter_func(state->userdata, &tls, &item, &index, &state->iter_shared.is_finished); + } + + /* Update current status. */ + state->iter_shared.next_index = index; + state->iter_shared.next_item = item; + current_chunk_size = i; + + do_abort = state->iter_shared.is_finished; + + if (state->iter_shared.spin_lock != NULL) { + BLI_spin_unlock(state->iter_shared.spin_lock); + } + + for (i = 0; i < current_chunk_size; ++i) { + state->func(state->userdata, current_chunk_items[i], current_chunk_indices[i], &tls); + } + } + + MALLOCA_FREE(current_chunk_items, items_size); + MALLOCA_FREE(current_chunk_indices, indices_size); +} + +static void parallel_iterator_func(TaskPool *__restrict pool, void *userdata_chunk) +{ + TaskParallelIteratorState *__restrict state = BLI_task_pool_user_data(pool); + + parallel_iterator_func_do(state, userdata_chunk); +} + +static void task_parallel_iterator_no_threads(const TaskParallelSettings *settings, + TaskParallelIteratorState *state) +{ + /* Prepare user's TLS data. */ + void *userdata_chunk = settings->userdata_chunk; + const size_t userdata_chunk_size = settings->userdata_chunk_size; + void *userdata_chunk_local = NULL; + const bool use_userdata_chunk = (userdata_chunk_size != 0) && (userdata_chunk != NULL); + if (use_userdata_chunk) { + userdata_chunk_local = MALLOCA(userdata_chunk_size); + memcpy(userdata_chunk_local, userdata_chunk, userdata_chunk_size); + } + + /* Also marking it as non-threaded for the iterator callback. */ + state->iter_shared.spin_lock = NULL; + + parallel_iterator_func_do(state, userdata_chunk); + + if (use_userdata_chunk && settings->func_free != NULL) { + /* `func_free` should only free data that was created during execution of `func`. */ + settings->func_free(state->userdata, userdata_chunk_local); + } +} + +static void task_parallel_iterator_do(const TaskParallelSettings *settings, + TaskParallelIteratorState *state) +{ + const int num_threads = BLI_task_scheduler_num_threads(); + + task_parallel_calc_chunk_size( + settings, state->tot_items, num_threads, &state->iter_shared.chunk_size); + + if (!settings->use_threading) { + task_parallel_iterator_no_threads(settings, state); + return; + } + + const int chunk_size = state->iter_shared.chunk_size; + const int tot_items = state->tot_items; + const size_t num_tasks = tot_items >= 0 ? + (size_t)min_ii(num_threads, state->tot_items / chunk_size) : + (size_t)num_threads; + + BLI_assert(num_tasks > 0); + if (num_tasks == 1) { + task_parallel_iterator_no_threads(settings, state); + return; + } + + SpinLock spin_lock; + BLI_spin_init(&spin_lock); + state->iter_shared.spin_lock = &spin_lock; + + void *userdata_chunk = settings->userdata_chunk; + const size_t userdata_chunk_size = settings->userdata_chunk_size; + void *userdata_chunk_local = NULL; + void *userdata_chunk_array = NULL; + const bool use_userdata_chunk = (userdata_chunk_size != 0) && (userdata_chunk != NULL); + + TaskPool *task_pool = BLI_task_pool_create(state, TASK_PRIORITY_HIGH); + + if (use_userdata_chunk) { + userdata_chunk_array = MALLOCA(userdata_chunk_size * num_tasks); + } + + for (size_t i = 0; i < num_tasks; i++) { + if (use_userdata_chunk) { + userdata_chunk_local = (char *)userdata_chunk_array + (userdata_chunk_size * i); + memcpy(userdata_chunk_local, userdata_chunk, userdata_chunk_size); + } + /* Use this pool's pre-allocated tasks. */ + BLI_task_pool_push(task_pool, parallel_iterator_func, userdata_chunk_local, false, NULL); + } + + BLI_task_pool_work_and_wait(task_pool); + BLI_task_pool_free(task_pool); + + if (use_userdata_chunk && (settings->func_reduce != NULL || settings->func_free != NULL)) { + for (size_t i = 0; i < num_tasks; i++) { + userdata_chunk_local = (char *)userdata_chunk_array + (userdata_chunk_size * i); + if (settings->func_reduce != NULL) { + settings->func_reduce(state->userdata, userdata_chunk, userdata_chunk_local); + } + if (settings->func_free != NULL) { + settings->func_free(state->userdata, userdata_chunk_local); + } + } + MALLOCA_FREE(userdata_chunk_array, userdata_chunk_size * num_tasks); + } + + BLI_spin_end(&spin_lock); + state->iter_shared.spin_lock = NULL; +} + +/** + * This function allows to parallelize for loops using a generic iterator. + * + * \param userdata: Common userdata passed to all instances of \a func. + * \param iter_func: Callback function used to generate chunks of items. + * \param init_item: The initial item, if necessary (may be NULL if unused). + * \param init_index: The initial index. + * \param tot_items: The total amount of items to iterate over + * (if unknown, set it to a negative number). + * \param func: Callback function. + * \param settings: See public API doc of TaskParallelSettings for description of all settings. + * + * \note Static scheduling is only available when \a tot_items is >= 0. + */ + +void BLI_task_parallel_iterator(void *userdata, + TaskParallelIteratorIterFunc iter_func, + void *init_item, + const int init_index, + const int tot_items, + TaskParallelIteratorFunc func, + const TaskParallelSettings *settings) +{ + TaskParallelIteratorState state = {0}; + + state.tot_items = tot_items; + state.iter_shared.next_index = init_index; + state.iter_shared.next_item = init_item; + state.iter_shared.is_finished = false; + state.userdata = userdata; + state.iter_func = iter_func; + state.func = func; + + task_parallel_iterator_do(settings, &state); +} + +static void task_parallel_listbase_get(void *__restrict UNUSED(userdata), + const TaskParallelTLS *__restrict UNUSED(tls), + void **r_next_item, + int *r_next_index, + bool *r_do_abort) +{ + /* Get current status. */ + Link *link = *r_next_item; + + if (link->next == NULL) { + *r_do_abort = true; + } + *r_next_item = link->next; + (*r_next_index)++; +} + +/** + * This function allows to parallelize for loops over ListBase items. + * + * \param listbase: The double linked list to loop over. + * \param userdata: Common userdata passed to all instances of \a func. + * \param func: Callback function. + * \param settings: See public API doc of ParallelRangeSettings for description of all settings. + * + * \note There is no static scheduling here, + * since it would need another full loop over items to count them. + */ +void BLI_task_parallel_listbase(ListBase *listbase, + void *userdata, + TaskParallelIteratorFunc func, + const TaskParallelSettings *settings) +{ + if (BLI_listbase_is_empty(listbase)) { + return; + } + + TaskParallelIteratorState state = {0}; + + state.tot_items = BLI_listbase_count(listbase); + state.iter_shared.next_index = 0; + state.iter_shared.next_item = listbase->first; + state.iter_shared.is_finished = false; + state.userdata = userdata; + state.iter_func = task_parallel_listbase_get; + state.func = func; + + task_parallel_iterator_do(settings, &state); +} + +#undef MALLOCA +#undef MALLOCA_FREE + +typedef struct ParallelMempoolState { + void *userdata; + TaskParallelMempoolFunc func; +} ParallelMempoolState; + +static void parallel_mempool_func(TaskPool *__restrict pool, void *taskdata) +{ + ParallelMempoolState *__restrict state = BLI_task_pool_user_data(pool); + BLI_mempool_iter *iter = taskdata; + MempoolIterData *item; + + while ((item = BLI_mempool_iterstep(iter)) != NULL) { + state->func(state->userdata, item); + } +} + +/** + * This function allows to parallelize for loops over Mempool items. + * + * \param mempool: The iterable BLI_mempool to loop over. + * \param userdata: Common userdata passed to all instances of \a func. + * \param func: Callback function. + * \param use_threading: If \a true, actually split-execute loop in threads, + * else just do a sequential for loop + * (allows caller to use any kind of test to switch on parallelization or not). + * + * \note There is no static scheduling here. + */ +void BLI_task_parallel_mempool(BLI_mempool *mempool, + void *userdata, + TaskParallelMempoolFunc func, + const bool use_threading) +{ + TaskPool *task_pool; + ParallelMempoolState state; + int i, num_threads, num_tasks; + + if (BLI_mempool_len(mempool) == 0) { + return; + } + + if (!use_threading) { + BLI_mempool_iter iter; + BLI_mempool_iternew(mempool, &iter); + + for (void *item = BLI_mempool_iterstep(&iter); item != NULL; + item = BLI_mempool_iterstep(&iter)) { + func(userdata, item); + } + return; + } + + task_pool = BLI_task_pool_create(&state, TASK_PRIORITY_HIGH); + num_threads = BLI_task_scheduler_num_threads(); + + /* 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 item to be crunched using the threaded-aware BLI_mempool_iter. + */ + num_tasks = num_threads + 2; + + state.userdata = userdata; + state.func = func; + + BLI_mempool_iter *mempool_iterators = BLI_mempool_iter_threadsafe_create(mempool, + (size_t)num_tasks); + + for (i = 0; i < num_tasks; i++) { + /* Use this pool's pre-allocated tasks. */ + BLI_task_pool_push(task_pool, parallel_mempool_func, &mempool_iterators[i], false, NULL); + } + + BLI_task_pool_work_and_wait(task_pool); + BLI_task_pool_free(task_pool); + + BLI_mempool_iter_threadsafe_free(mempool_iterators); +} |