2019-06-04 23:23:55 +02:00
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/* Apache License, Version 2.0 */
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#include "testing/testing.h"
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#include "BLI_ressource_strings.h"
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#include "atomic_ops.h"
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#define GHASH_INTERNAL_API
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2020-01-26 16:38:18 +01:00
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#include "MEM_guardedalloc.h"
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2019-06-04 23:23:55 +02:00
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extern "C" {
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#include "BLI_utildefines.h"
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#include "BLI_listbase.h"
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#include "BLI_mempool.h"
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#include "BLI_task.h"
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#include "PIL_time.h"
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}
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#define NUM_RUN_AVERAGED 100
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static uint gen_pseudo_random_number(uint num)
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{
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/* Note: this is taken from BLI_ghashutil_uinthash(), don't want to depend on external code that
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* might change here... */
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num += ~(num << 16);
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num ^= (num >> 5);
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num += (num << 3);
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num ^= (num >> 13);
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num += ~(num << 9);
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num ^= (num >> 17);
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/* Make final number in [65 - 16385] range. */
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return ((num & 255) << 6) + 1;
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}
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BLI_task: Add pooled threaded index range iterator, Take II.
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
Note: Compared to previous commit yesterday, this reworks atomic handling in
parallel iter code, and fixes a dummy double-free bug.
Now we should only use the two critical values for synchronization from
atomic calls results, which is the proper way to do things.
Reading a value after an atomic operation does not guarantee you will
get the latest value in all cases (especially on Windows release builds
it seems).
2019-11-26 14:26:47 +01:00
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/* *** Parallel iterations over range of indices. *** */
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static void task_parallel_range_func(void *UNUSED(userdata),
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int index,
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const TaskParallelTLS *__restrict UNUSED(tls))
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{
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const uint limit = gen_pseudo_random_number((uint)index);
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for (uint i = (uint)index; i < limit;) {
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i += gen_pseudo_random_number(i);
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}
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}
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static void task_parallel_range_test_do(const char *id,
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const int num_items,
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const bool use_threads)
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{
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TaskParallelSettings settings;
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BLI_parallel_range_settings_defaults(&settings);
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settings.use_threading = use_threads;
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double averaged_timing = 0.0;
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for (int i = 0; i < NUM_RUN_AVERAGED; i++) {
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const double init_time = PIL_check_seconds_timer();
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for (int j = 0; j < 10; j++) {
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BLI_task_parallel_range(i + j, i + j + num_items, NULL, task_parallel_range_func, &settings);
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}
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averaged_timing += PIL_check_seconds_timer() - init_time;
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}
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printf("\t%s: non-pooled done in %fs on average over %d runs\n",
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id,
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averaged_timing / NUM_RUN_AVERAGED,
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NUM_RUN_AVERAGED);
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averaged_timing = 0.0;
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for (int i = 0; i < NUM_RUN_AVERAGED; i++) {
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const double init_time = PIL_check_seconds_timer();
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TaskParallelRangePool *range_pool = BLI_task_parallel_range_pool_init(&settings);
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for (int j = 0; j < 10; j++) {
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BLI_task_parallel_range_pool_push(
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range_pool, i + j, i + j + num_items, NULL, task_parallel_range_func, &settings);
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}
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BLI_task_parallel_range_pool_work_and_wait(range_pool);
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BLI_task_parallel_range_pool_free(range_pool);
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averaged_timing += PIL_check_seconds_timer() - init_time;
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}
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printf("\t%s: pooled done in %fs on average over %d runs\n",
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id,
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averaged_timing / NUM_RUN_AVERAGED,
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NUM_RUN_AVERAGED);
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}
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TEST(task, RangeIter10KNoThread)
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{
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task_parallel_range_test_do(
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"Range parallel iteration - Single thread - 10K items", 10000, false);
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}
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TEST(task, RangeIter10k)
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{
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task_parallel_range_test_do("Range parallel iteration - Threaded - 10K items", 10000, true);
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}
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TEST(task, RangeIter100KNoThread)
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{
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task_parallel_range_test_do(
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"Range parallel iteration - Single thread - 100K items", 100000, false);
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}
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TEST(task, RangeIter100k)
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{
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task_parallel_range_test_do("Range parallel iteration - Threaded - 100K items", 100000, true);
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}
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TEST(task, RangeIter1000KNoThread)
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{
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task_parallel_range_test_do(
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"Range parallel iteration - Single thread - 1000K items", 1000000, false);
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}
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TEST(task, RangeIter1000k)
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{
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task_parallel_range_test_do("Range parallel iteration - Threaded - 1000K items", 1000000, true);
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}
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/* *** Parallel iterations over double-linked list items. *** */
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BLI_task: Add new generic `BLI_task_parallel_iterator()`.
This new function is part of the 'parallel for loops' functions. It
takes an iterator callback to generate items to be processed, in
addition to the usual 'process' func callback.
This allows to use common code from BLI_task for a wide range of custom
iteratiors, whithout having to re-invent the wheel of the whole tasks &
data chuncks handling.
This supports all settings features from `BLI_task_parallel_range()`,
including dynamic and static (if total number of items is knwon)
scheduling, TLS data and its finalize callback, etc.
One question here is whether we should provide usercode with a spinlock
by default, or enforce it to always handle its own sync mechanism.
I kept it, since imho it will be needed very often, and generating one
is pretty cheap even if unused...
----------
Additionaly, this commit converts (currently unused)
`BLI_task_parallel_listbase()` to use that generic code. This was done
mostly as proof of concept, but performance-wise it shows some
interesting data, roughly:
- Very light processing (that should not be threaded anyway) is several
times slower, which is expected due to more overhead in loop management
code.
- Heavier processing can be up to 10% quicker (probably thanks to the
switch from dynamic to static scheduling, which reduces a lot locking
to fill-in the per-tasks chunks of data). Similar speed-up in
non-threaded case comes as a surprise though, not sure what can
explain that.
While this conversion is not really needed, imho we should keep it
(instead of existing code for that function), it's easier to have
complex handling logic in as few places as possible, for maintaining and
for improving it.
Note: That work was initially done to allow for D5372 to be possible... Unfortunately that one proved to be not better than orig code on performances point of view.
Reviewed By: sergey
Differential Revision: https://developer.blender.org/D5371
2019-10-30 12:23:45 +01:00
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static void task_listbase_light_iter_func(void *UNUSED(userdata),
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void *item,
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int index,
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const TaskParallelTLS *__restrict UNUSED(tls))
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2019-06-04 23:23:55 +02:00
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{
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LinkData *data = (LinkData *)item;
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data->data = POINTER_FROM_INT(POINTER_AS_INT(data->data) + index);
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}
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BLI_task: Add new generic `BLI_task_parallel_iterator()`.
This new function is part of the 'parallel for loops' functions. It
takes an iterator callback to generate items to be processed, in
addition to the usual 'process' func callback.
This allows to use common code from BLI_task for a wide range of custom
iteratiors, whithout having to re-invent the wheel of the whole tasks &
data chuncks handling.
This supports all settings features from `BLI_task_parallel_range()`,
including dynamic and static (if total number of items is knwon)
scheduling, TLS data and its finalize callback, etc.
One question here is whether we should provide usercode with a spinlock
by default, or enforce it to always handle its own sync mechanism.
I kept it, since imho it will be needed very often, and generating one
is pretty cheap even if unused...
----------
Additionaly, this commit converts (currently unused)
`BLI_task_parallel_listbase()` to use that generic code. This was done
mostly as proof of concept, but performance-wise it shows some
interesting data, roughly:
- Very light processing (that should not be threaded anyway) is several
times slower, which is expected due to more overhead in loop management
code.
- Heavier processing can be up to 10% quicker (probably thanks to the
switch from dynamic to static scheduling, which reduces a lot locking
to fill-in the per-tasks chunks of data). Similar speed-up in
non-threaded case comes as a surprise though, not sure what can
explain that.
While this conversion is not really needed, imho we should keep it
(instead of existing code for that function), it's easier to have
complex handling logic in as few places as possible, for maintaining and
for improving it.
Note: That work was initially done to allow for D5372 to be possible... Unfortunately that one proved to be not better than orig code on performances point of view.
Reviewed By: sergey
Differential Revision: https://developer.blender.org/D5371
2019-10-30 12:23:45 +01:00
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static void task_listbase_light_membarrier_iter_func(void *userdata,
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void *item,
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int index,
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const TaskParallelTLS *__restrict UNUSED(tls))
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2019-06-04 23:23:55 +02:00
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{
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LinkData *data = (LinkData *)item;
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int *count = (int *)userdata;
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data->data = POINTER_FROM_INT(POINTER_AS_INT(data->data) + index);
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atomic_sub_and_fetch_uint32((uint32_t *)count, 1);
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}
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BLI_task: Add new generic `BLI_task_parallel_iterator()`.
This new function is part of the 'parallel for loops' functions. It
takes an iterator callback to generate items to be processed, in
addition to the usual 'process' func callback.
This allows to use common code from BLI_task for a wide range of custom
iteratiors, whithout having to re-invent the wheel of the whole tasks &
data chuncks handling.
This supports all settings features from `BLI_task_parallel_range()`,
including dynamic and static (if total number of items is knwon)
scheduling, TLS data and its finalize callback, etc.
One question here is whether we should provide usercode with a spinlock
by default, or enforce it to always handle its own sync mechanism.
I kept it, since imho it will be needed very often, and generating one
is pretty cheap even if unused...
----------
Additionaly, this commit converts (currently unused)
`BLI_task_parallel_listbase()` to use that generic code. This was done
mostly as proof of concept, but performance-wise it shows some
interesting data, roughly:
- Very light processing (that should not be threaded anyway) is several
times slower, which is expected due to more overhead in loop management
code.
- Heavier processing can be up to 10% quicker (probably thanks to the
switch from dynamic to static scheduling, which reduces a lot locking
to fill-in the per-tasks chunks of data). Similar speed-up in
non-threaded case comes as a surprise though, not sure what can
explain that.
While this conversion is not really needed, imho we should keep it
(instead of existing code for that function), it's easier to have
complex handling logic in as few places as possible, for maintaining and
for improving it.
Note: That work was initially done to allow for D5372 to be possible... Unfortunately that one proved to be not better than orig code on performances point of view.
Reviewed By: sergey
Differential Revision: https://developer.blender.org/D5371
2019-10-30 12:23:45 +01:00
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static void task_listbase_heavy_iter_func(void *UNUSED(userdata),
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void *item,
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int index,
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const TaskParallelTLS *__restrict UNUSED(tls))
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2019-06-04 23:23:55 +02:00
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{
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LinkData *data = (LinkData *)item;
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/* 'Random' number of iterations. */
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const uint num = gen_pseudo_random_number((uint)index);
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for (uint i = 0; i < num; i++) {
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data->data = POINTER_FROM_INT(POINTER_AS_INT(data->data) + ((i % 2) ? -index : index));
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}
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}
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BLI_task: Add new generic `BLI_task_parallel_iterator()`.
This new function is part of the 'parallel for loops' functions. It
takes an iterator callback to generate items to be processed, in
addition to the usual 'process' func callback.
This allows to use common code from BLI_task for a wide range of custom
iteratiors, whithout having to re-invent the wheel of the whole tasks &
data chuncks handling.
This supports all settings features from `BLI_task_parallel_range()`,
including dynamic and static (if total number of items is knwon)
scheduling, TLS data and its finalize callback, etc.
One question here is whether we should provide usercode with a spinlock
by default, or enforce it to always handle its own sync mechanism.
I kept it, since imho it will be needed very often, and generating one
is pretty cheap even if unused...
----------
Additionaly, this commit converts (currently unused)
`BLI_task_parallel_listbase()` to use that generic code. This was done
mostly as proof of concept, but performance-wise it shows some
interesting data, roughly:
- Very light processing (that should not be threaded anyway) is several
times slower, which is expected due to more overhead in loop management
code.
- Heavier processing can be up to 10% quicker (probably thanks to the
switch from dynamic to static scheduling, which reduces a lot locking
to fill-in the per-tasks chunks of data). Similar speed-up in
non-threaded case comes as a surprise though, not sure what can
explain that.
While this conversion is not really needed, imho we should keep it
(instead of existing code for that function), it's easier to have
complex handling logic in as few places as possible, for maintaining and
for improving it.
Note: That work was initially done to allow for D5372 to be possible... Unfortunately that one proved to be not better than orig code on performances point of view.
Reviewed By: sergey
Differential Revision: https://developer.blender.org/D5371
2019-10-30 12:23:45 +01:00
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static void task_listbase_heavy_membarrier_iter_func(void *userdata,
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void *item,
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int index,
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const TaskParallelTLS *__restrict UNUSED(tls))
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2019-06-04 23:23:55 +02:00
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{
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LinkData *data = (LinkData *)item;
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int *count = (int *)userdata;
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/* 'Random' number of iterations. */
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const uint num = gen_pseudo_random_number((uint)index);
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for (uint i = 0; i < num; i++) {
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data->data = POINTER_FROM_INT(POINTER_AS_INT(data->data) + ((i % 2) ? -index : index));
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}
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atomic_sub_and_fetch_uint32((uint32_t *)count, 1);
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}
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static void task_listbase_test_do(ListBase *list,
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const int num_items,
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int *num_items_tmp,
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const char *id,
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BLI_task: Add new generic `BLI_task_parallel_iterator()`.
This new function is part of the 'parallel for loops' functions. It
takes an iterator callback to generate items to be processed, in
addition to the usual 'process' func callback.
This allows to use common code from BLI_task for a wide range of custom
iteratiors, whithout having to re-invent the wheel of the whole tasks &
data chuncks handling.
This supports all settings features from `BLI_task_parallel_range()`,
including dynamic and static (if total number of items is knwon)
scheduling, TLS data and its finalize callback, etc.
One question here is whether we should provide usercode with a spinlock
by default, or enforce it to always handle its own sync mechanism.
I kept it, since imho it will be needed very often, and generating one
is pretty cheap even if unused...
----------
Additionaly, this commit converts (currently unused)
`BLI_task_parallel_listbase()` to use that generic code. This was done
mostly as proof of concept, but performance-wise it shows some
interesting data, roughly:
- Very light processing (that should not be threaded anyway) is several
times slower, which is expected due to more overhead in loop management
code.
- Heavier processing can be up to 10% quicker (probably thanks to the
switch from dynamic to static scheduling, which reduces a lot locking
to fill-in the per-tasks chunks of data). Similar speed-up in
non-threaded case comes as a surprise though, not sure what can
explain that.
While this conversion is not really needed, imho we should keep it
(instead of existing code for that function), it's easier to have
complex handling logic in as few places as possible, for maintaining and
for improving it.
Note: That work was initially done to allow for D5372 to be possible... Unfortunately that one proved to be not better than orig code on performances point of view.
Reviewed By: sergey
Differential Revision: https://developer.blender.org/D5371
2019-10-30 12:23:45 +01:00
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TaskParallelIteratorFunc func,
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2019-06-04 23:23:55 +02:00
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const bool use_threads,
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const bool check_num_items_tmp)
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{
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BLI_task: Add new generic `BLI_task_parallel_iterator()`.
This new function is part of the 'parallel for loops' functions. It
takes an iterator callback to generate items to be processed, in
addition to the usual 'process' func callback.
This allows to use common code from BLI_task for a wide range of custom
iteratiors, whithout having to re-invent the wheel of the whole tasks &
data chuncks handling.
This supports all settings features from `BLI_task_parallel_range()`,
including dynamic and static (if total number of items is knwon)
scheduling, TLS data and its finalize callback, etc.
One question here is whether we should provide usercode with a spinlock
by default, or enforce it to always handle its own sync mechanism.
I kept it, since imho it will be needed very often, and generating one
is pretty cheap even if unused...
----------
Additionaly, this commit converts (currently unused)
`BLI_task_parallel_listbase()` to use that generic code. This was done
mostly as proof of concept, but performance-wise it shows some
interesting data, roughly:
- Very light processing (that should not be threaded anyway) is several
times slower, which is expected due to more overhead in loop management
code.
- Heavier processing can be up to 10% quicker (probably thanks to the
switch from dynamic to static scheduling, which reduces a lot locking
to fill-in the per-tasks chunks of data). Similar speed-up in
non-threaded case comes as a surprise though, not sure what can
explain that.
While this conversion is not really needed, imho we should keep it
(instead of existing code for that function), it's easier to have
complex handling logic in as few places as possible, for maintaining and
for improving it.
Note: That work was initially done to allow for D5372 to be possible... Unfortunately that one proved to be not better than orig code on performances point of view.
Reviewed By: sergey
Differential Revision: https://developer.blender.org/D5371
2019-10-30 12:23:45 +01:00
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TaskParallelSettings settings;
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BLI_parallel_range_settings_defaults(&settings);
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settings.use_threading = use_threads;
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2019-06-04 23:23:55 +02:00
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double averaged_timing = 0.0;
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for (int i = 0; i < NUM_RUN_AVERAGED; i++) {
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const double init_time = PIL_check_seconds_timer();
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BLI_task: Add new generic `BLI_task_parallel_iterator()`.
This new function is part of the 'parallel for loops' functions. It
takes an iterator callback to generate items to be processed, in
addition to the usual 'process' func callback.
This allows to use common code from BLI_task for a wide range of custom
iteratiors, whithout having to re-invent the wheel of the whole tasks &
data chuncks handling.
This supports all settings features from `BLI_task_parallel_range()`,
including dynamic and static (if total number of items is knwon)
scheduling, TLS data and its finalize callback, etc.
One question here is whether we should provide usercode with a spinlock
by default, or enforce it to always handle its own sync mechanism.
I kept it, since imho it will be needed very often, and generating one
is pretty cheap even if unused...
----------
Additionaly, this commit converts (currently unused)
`BLI_task_parallel_listbase()` to use that generic code. This was done
mostly as proof of concept, but performance-wise it shows some
interesting data, roughly:
- Very light processing (that should not be threaded anyway) is several
times slower, which is expected due to more overhead in loop management
code.
- Heavier processing can be up to 10% quicker (probably thanks to the
switch from dynamic to static scheduling, which reduces a lot locking
to fill-in the per-tasks chunks of data). Similar speed-up in
non-threaded case comes as a surprise though, not sure what can
explain that.
While this conversion is not really needed, imho we should keep it
(instead of existing code for that function), it's easier to have
complex handling logic in as few places as possible, for maintaining and
for improving it.
Note: That work was initially done to allow for D5372 to be possible... Unfortunately that one proved to be not better than orig code on performances point of view.
Reviewed By: sergey
Differential Revision: https://developer.blender.org/D5371
2019-10-30 12:23:45 +01:00
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BLI_task_parallel_listbase(list, num_items_tmp, func, &settings);
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2019-06-04 23:23:55 +02:00
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averaged_timing += PIL_check_seconds_timer() - init_time;
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/* Those checks should ensure us all items of the listbase were processed once, and only once -
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* as expected. */
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if (check_num_items_tmp) {
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EXPECT_EQ(*num_items_tmp, 0);
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}
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LinkData *item;
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int j;
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for (j = 0, item = (LinkData *)list->first; j < num_items && item != NULL;
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j++, item = item->next) {
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EXPECT_EQ(POINTER_AS_INT(item->data), j);
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item->data = POINTER_FROM_INT(0);
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}
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EXPECT_EQ(num_items, j);
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*num_items_tmp = num_items;
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}
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printf("\t%s: done in %fs on average over %d runs\n",
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id,
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averaged_timing / NUM_RUN_AVERAGED,
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NUM_RUN_AVERAGED);
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}
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static void task_listbase_test(const char *id, const int nbr, const bool use_threads)
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{
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printf("\n========== STARTING %s ==========\n", id);
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ListBase list = {NULL, NULL};
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LinkData *items_buffer = (LinkData *)MEM_calloc_arrayN(nbr, sizeof(*items_buffer), __func__);
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BLI_threadapi_init();
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int num_items = 0;
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for (int i = 0; i < nbr; i++) {
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BLI_addtail(&list, &items_buffer[i]);
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num_items++;
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}
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int num_items_tmp = num_items;
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task_listbase_test_do(&list,
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num_items,
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&num_items_tmp,
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"Light iter",
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task_listbase_light_iter_func,
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use_threads,
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false);
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task_listbase_test_do(&list,
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num_items,
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&num_items_tmp,
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"Light iter with mem barrier",
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task_listbase_light_membarrier_iter_func,
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use_threads,
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true);
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task_listbase_test_do(&list,
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num_items,
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&num_items_tmp,
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"Heavy iter",
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task_listbase_heavy_iter_func,
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use_threads,
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false);
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task_listbase_test_do(&list,
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num_items,
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&num_items_tmp,
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"Heavy iter with mem barrier",
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task_listbase_heavy_membarrier_iter_func,
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use_threads,
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true);
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MEM_freeN(items_buffer);
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BLI_threadapi_exit();
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printf("========== ENDED %s ==========\n\n", id);
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}
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TEST(task, ListBaseIterNoThread10k)
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{
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task_listbase_test("ListBase parallel iteration - Single thread - 10000 items", 10000, false);
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}
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TEST(task, ListBaseIter10k)
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{
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task_listbase_test("ListBase parallel iteration - Threaded - 10000 items", 10000, true);
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}
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TEST(task, ListBaseIterNoThread100k)
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{
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task_listbase_test("ListBase parallel iteration - Single thread - 100000 items", 100000, false);
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}
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TEST(task, ListBaseIter100k)
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{
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task_listbase_test("ListBase parallel iteration - Threaded - 100000 items", 100000, true);
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}
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