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Cleanup: Remove unused threading C-API functions

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BLI_task.hh has newer/better equivalents now.

Pull Request: https://projects.blender.org/blender/blender/pulls/115539
This commit is contained in:
Hans Goudey
2023-12-01 15:29:36 +01:00
committed by Hans Goudey
parent 5e370ee643
commit 4c79b87d9a
5 changed files with 0 additions and 622 deletions
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source/blender/blenlib/BLI_task.h
-67
View File
@@ -208,73 +208,6 @@ void BLI_task_parallel_range(int start,
TaskParallelRangeFunc func,
const TaskParallelSettings *settings);
/**
* This data is shared between all tasks, its access needs thread lock or similar protection.
*/
typedef struct TaskParallelIteratorStateShared {
/* Maximum amount of items to acquire at once. */
int chunk_size;
/* Next item to be acquired. */
void *next_item;
/* Index of the next item to be acquired. */
int next_index;
/* Indicates that end of iteration has been reached. */
bool is_finished;
/* Helper lock to protect access to this data in iterator getter callback,
* can be ignored (if the callback implements its own protection system, using atomics e.g.).
* Will be NULL when iterator is actually processed in a single thread. */
SpinLock *spin_lock;
} TaskParallelIteratorStateShared;
typedef void (*TaskParallelIteratorIterFunc)(void *__restrict userdata,
const TaskParallelTLS *__restrict tls,
void **r_next_item,
int *r_next_index,
bool *r_do_abort);
typedef void (*TaskParallelIteratorFunc)(void *__restrict userdata,
void *item,
int index,
const TaskParallelTLS *__restrict tls);
/**
* 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 items_num: 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 items_num is >= 0.
*/
void BLI_task_parallel_iterator(void *userdata,
TaskParallelIteratorIterFunc iter_func,
void *init_item,
int init_index,
int items_num,
TaskParallelIteratorFunc func,
const TaskParallelSettings *settings);
/**
* 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(struct ListBase *listbase,
void *userdata,
TaskParallelIteratorFunc func,
const TaskParallelSettings *settings);
typedef struct MempoolIterData MempoolIterData;
typedef void (*TaskParallelMempoolFunc)(void *userdata,
source/blender/blenlib/intern/task_iterator.c
-292
View File
@@ -37,298 +37,6 @@
/** \} */
/* -------------------------------------------------------------------- */
/** \name Generic Iteration
* \{ */
BLI_INLINE void task_parallel_calc_chunk_size(const TaskParallelSettings *settings,
const int items_num,
int tasks_num,
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;
tasks_num = 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 tasks_num < 16;
* else 2 if tasks_num < 32;
* else 3 if tasks_num < 48;
* else 4 if tasks_num < 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(tasks_num) - 3)
*/
const int tasks_num_factor = max_ii(1, tasks_num >> 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 * tasks_num_factor;
/* Basic heuristic to avoid threading on low amount of items.
* We could make that limit configurable in settings too. */
if (items_num > 0 && items_num < max_ii(256, chunk_size * 2)) {
chunk_size = items_num;
}
}
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 items_num;
} 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;
if (userdata_chunk) {
if (settings->func_init != NULL) {
settings->func_init(state->userdata, userdata_chunk);
}
}
/* Also marking it as non-threaded for the iterator callback. */
state->iter_shared.spin_lock = NULL;
parallel_iterator_func_do(state, userdata_chunk);
if (userdata_chunk) {
if (settings->func_free != NULL) {
/* `func_free` should only free data that was created during execution of `func`. */
settings->func_free(state->userdata, userdata_chunk);
}
}
}
static void task_parallel_iterator_do(const TaskParallelSettings *settings,
TaskParallelIteratorState *state)
{
const int threads_num = BLI_task_scheduler_num_threads();
task_parallel_calc_chunk_size(
settings, state->items_num, threads_num, &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 items_num = state->items_num;
const size_t tasks_num = items_num >= 0 ?
(size_t)min_ii(threads_num, state->items_num / chunk_size) :
(size_t)threads_num;
BLI_assert(tasks_num > 0);
if (tasks_num == 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 * tasks_num);
}
for (size_t i = 0; i < tasks_num; 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);
if (settings->func_init != NULL) {
settings->func_init(state->userdata, userdata_chunk_local);
}
}
/* 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) {
if (settings->func_reduce != NULL || settings->func_free != NULL) {
for (size_t i = 0; i < tasks_num; 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 * tasks_num);
}
BLI_spin_end(&spin_lock);
state->iter_shared.spin_lock = NULL;
}
void BLI_task_parallel_iterator(void *userdata,
TaskParallelIteratorIterFunc iter_func,
void *init_item,
const int init_index,
const int items_num,
TaskParallelIteratorFunc func,
const TaskParallelSettings *settings)
{
TaskParallelIteratorState state = {0};
state.items_num = items_num;
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);
}
/** \} */
/* -------------------------------------------------------------------- */
/** \name ListBase Iteration
* \{ */
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)++;
}
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.items_num = 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);
}
/** \} */
/* -------------------------------------------------------------------- */
/** \name MemPool Iteration
* \{ */
source/blender/blenlib/tests/BLI_task_test.cc
-49
View File
@@ -234,55 +234,6 @@ TEST(task, MempoolIterTLS)
BLI_threadapi_exit();
}
/* *** Parallel iterations over double-linked list items. *** */
static void task_listbase_iter_func(void *userdata,
void *item,
int index,
const TaskParallelTLS *__restrict /*tls*/)
{
LinkData *data = (LinkData *)item;
int *count = (int *)userdata;
data->data = POINTER_FROM_INT(POINTER_AS_INT(data->data) + index);
atomic_sub_and_fetch_uint32((uint32_t *)count, 1);
}
TEST(task, ListBaseIter)
{
ListBase list = {nullptr, nullptr};
LinkData *items_buffer = (LinkData *)MEM_calloc_arrayN(
ITEMS_NUM, sizeof(*items_buffer), __func__);
BLI_threadapi_init();
int i;
int items_num = 0;
for (i = 0; i < ITEMS_NUM; i++) {
BLI_addtail(&list, &items_buffer[i]);
items_num++;
}
TaskParallelSettings settings;
BLI_parallel_range_settings_defaults(&settings);
BLI_task_parallel_listbase(&list, &items_num, task_listbase_iter_func, &settings);
/* Those checks should ensure us all items of the listbase were processed once, and only once -
* as expected. */
EXPECT_EQ(items_num, 0);
LinkData *item;
for (i = 0, item = (LinkData *)list.first; i < ITEMS_NUM && item != nullptr;
i++, item = item->next)
{
EXPECT_EQ(POINTER_AS_INT(item->data), i);
}
EXPECT_EQ(ITEMS_NUM, i);
MEM_freeN(items_buffer);
BLI_threadapi_exit();
}
TEST(task, ParallelInvoke)
{
std::atomic<int> counter = 0;
source/blender/blenlib/tests/performance/BLI_task_performance_test.cc
-213
View File
@@ -1,213 +0,0 @@
/* SPDX-FileCopyrightText: 2023 Blender Authors
*
* SPDX-License-Identifier: Apache-2.0 */
#include "BLI_ressource_strings.h"
#include "testing/testing.h"
#include "atomic_ops.h"
#define GHASH_INTERNAL_API
#include "MEM_guardedalloc.h"
#include "BLI_utildefines.h"
#include "BLI_listbase.h"
#include "BLI_mempool.h"
#include "BLI_task.h"
#include "PIL_time.h"
#define NUM_RUN_AVERAGED 100
static uint gen_pseudo_random_number(uint num)
{
/* NOTE: this is taken from BLI_ghashutil_uinthash(), don't want to depend on external code that
* might change here... */
num += ~(num << 16);
num ^= (num >> 5);
num += (num << 3);
num ^= (num >> 13);
num += ~(num << 9);
num ^= (num >> 17);
/* Make final number in [65 - 16385] range. */
return ((num & 255) << 6) + 1;
}
/* *** Parallel iterations over double-linked list items. *** */
static void task_listbase_light_iter_func(void * /*userdata*/,
void *item,
int index,
const TaskParallelTLS *__restrict /*tls*/)
{
LinkData *data = (LinkData *)item;
data->data = POINTER_FROM_INT(POINTER_AS_INT(data->data) + index);
}
static void task_listbase_light_membarrier_iter_func(void *userdata,
void *item,
int index,
const TaskParallelTLS *__restrict /*tls*/)
{
LinkData *data = (LinkData *)item;
int *count = (int *)userdata;
data->data = POINTER_FROM_INT(POINTER_AS_INT(data->data) + index);
atomic_sub_and_fetch_uint32((uint32_t *)count, 1);
}
static void task_listbase_heavy_iter_func(void * /*userdata*/,
void *item,
int index,
const TaskParallelTLS *__restrict /*tls*/)
{
LinkData *data = (LinkData *)item;
/* 'Random' number of iterations. */
const uint num = gen_pseudo_random_number(uint(index));
for (uint i = 0; i < num; i++) {
data->data = POINTER_FROM_INT(POINTER_AS_INT(data->data) + ((i % 2) ? -index : index));
}
}
static void task_listbase_heavy_membarrier_iter_func(void *userdata,
void *item,
int index,
const TaskParallelTLS *__restrict /*tls*/)
{
LinkData *data = (LinkData *)item;
int *count = (int *)userdata;
/* 'Random' number of iterations. */
const uint num = gen_pseudo_random_number(uint(index));
for (uint i = 0; i < num; i++) {
data->data = POINTER_FROM_INT(POINTER_AS_INT(data->data) + ((i % 2) ? -index : index));
}
atomic_sub_and_fetch_uint32((uint32_t *)count, 1);
}
static void task_listbase_test_do(ListBase *list,
const int items_num,
int *items_tmp_num,
const char *id,
TaskParallelIteratorFunc func,
const bool use_threads,
const bool check_items_tmp_num)
{
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();
BLI_task_parallel_listbase(list, items_tmp_num, func, &settings);
averaged_timing += PIL_check_seconds_timer() - init_time;
/* Those checks should ensure us all items of the listbase were processed once, and only once -
* as expected. */
if (check_items_tmp_num) {
EXPECT_EQ(*items_tmp_num, 0);
}
LinkData *item;
int j;
for (j = 0, item = (LinkData *)list->first; j < items_num && item != nullptr;
j++, item = item->next)
{
EXPECT_EQ(POINTER_AS_INT(item->data), j);
item->data = POINTER_FROM_INT(0);
}
EXPECT_EQ(items_num, j);
*items_tmp_num = items_num;
}
printf("\t%s: done in %fs on average over %d runs\n",
id,
averaged_timing / NUM_RUN_AVERAGED,
NUM_RUN_AVERAGED);
}
static void task_listbase_test(const char *id, const int count, const bool use_threads)
{
printf("\n========== STARTING %s ==========\n", id);
ListBase list = {nullptr, nullptr};
LinkData *items_buffer = (LinkData *)MEM_calloc_arrayN(count, sizeof(*items_buffer), __func__);
BLI_threadapi_init();
int items_num = 0;
for (int i = 0; i < count; i++) {
BLI_addtail(&list, &items_buffer[i]);
items_num++;
}
int items_tmp_num = items_num;
task_listbase_test_do(&list,
items_num,
&items_tmp_num,
"Light iter",
task_listbase_light_iter_func,
use_threads,
false);
task_listbase_test_do(&list,
items_num,
&items_tmp_num,
"Light iter with mem barrier",
task_listbase_light_membarrier_iter_func,
use_threads,
true);
task_listbase_test_do(&list,
items_num,
&items_tmp_num,
"Heavy iter",
task_listbase_heavy_iter_func,
use_threads,
false);
task_listbase_test_do(&list,
items_num,
&items_tmp_num,
"Heavy iter with mem barrier",
task_listbase_heavy_membarrier_iter_func,
use_threads,
true);
MEM_freeN(items_buffer);
BLI_threadapi_exit();
printf("========== ENDED %s ==========\n\n", id);
}
TEST(task, ListBaseIterNoThread10k)
{
task_listbase_test("ListBase parallel iteration - Single thread - 10000 items", 10000, false);
}
TEST(task, ListBaseIter10k)
{
task_listbase_test("ListBase parallel iteration - Threaded - 10000 items", 10000, true);
}
TEST(task, ListBaseIterNoThread100k)
{
task_listbase_test("ListBase parallel iteration - Single thread - 100000 items", 100000, false);
}
TEST(task, ListBaseIter100k)
{
task_listbase_test("ListBase parallel iteration - Threaded - 100000 items", 100000, true);
}
source/blender/blenlib/tests/performance/CMakeLists.txt
-1
View File
@@ -19,4 +19,3 @@ set(LIB
)
blender_add_performancetest_executable(BLI_ghash_performance "BLI_ghash_performance_test.cc" "${INC}" "${INC_SYS}" "${LIB}")
blender_add_performancetest_executable(BLI_task_performance "BLI_task_performance_test.cc" "${INC}" "${INC_SYS}" "${LIB}")