Avoid taking vm barrier in heap_prepare() #14425
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gc/default/default.c
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| bool needs_gc = is_incremental_marking(objspace) || (heap->free_pages == NULL && is_lazy_sweeping(objspace)); | ||
| if (needs_gc) { | ||
| gc_enter(objspace, gc_enter_event_continue, &lock_lev); // takes vm barrier, try to avoid | ||
| } |
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What about an early return instead?
Suggested change
| bool needs_gc = is_incremental_marking(objspace) || (heap->free_pages == NULL && is_lazy_sweeping(objspace)); | |
| if (needs_gc) { | |
| gc_enter(objspace, gc_enter_event_continue, &lock_lev); // takes vm barrier, try to avoid | |
| } | |
| bool needs_gc = is_incremental_marking(objspace) || (heap->free_pages == NULL && is_lazy_sweeping(objspace)); | |
| if (!needs_gc) { | |
| return; | |
| } |
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nobu
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Sep 14, 2025
gc/default/default.c
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| @@ -2012,7 +2012,10 @@ static void | |||
| gc_continue(rb_objspace_t *objspace, rb_heap_t *heap) | |||
| { | |||
| unsigned int lock_lev; | |||
| gc_enter(objspace, gc_enter_event_continue, &lock_lev); | |||
| bool needs_gc = is_incremental_marking(objspace) || (heap->free_pages == NULL && is_lazy_sweeping(objspace)); | |||
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How about extracting a macro:
/* In lazy sweeping or the previous incremental marking finished and
* did not yield a free page. */
#define needs_continue_sweeping(objspace, heap) \
((heap)->free_pages == NULL && is_lazy_sweeping(objspace))There was a problem hiding this comment.
@nobu Thanks, done!
We can avoid taking this barrier if we're not incremental marking or lazy sweeping. I found this was taking a significant amount of samples when profiling `Psych.load` in multiple ractors due to the vm barrier. With this change, we get significant improvements in ractor benchmarks that allocate lots of objects. -- Psych.load benchmark -- ``` Before: After: r: itr: time r: itr: time 0 ruby#1: 960ms 0 ruby#1: 943ms 0 ruby#2: 979ms 0 ruby#2: 939ms 0 ruby#3: 968ms 0 ruby#3: 948ms 0 ruby#4: 963ms 0 ruby#4: 946ms 0 ruby#5: 964ms 0 ruby#5: 944ms 1 ruby#1: 947ms 1 ruby#1: 940ms 1 ruby#2: 950ms 1 ruby#2: 947ms 1 ruby#3: 962ms 1 ruby#3: 950ms 1 ruby#4: 947ms 1 ruby#4: 945ms 1 ruby#5: 947ms 1 ruby#5: 943ms 2 ruby#1: 1131ms 2 ruby#1: 1005ms 2 ruby#2: 1153ms 2 ruby#2: 996ms 2 ruby#3: 1155ms 2 ruby#3: 1003ms 2 ruby#4: 1205ms 2 ruby#4: 1012ms 2 ruby#5: 1179ms 2 ruby#5: 1012ms 4 ruby#1: 1555ms 4 ruby#1: 1209ms 4 ruby#2: 1509ms 4 ruby#2: 1244ms 4 ruby#3: 1529ms 4 ruby#3: 1254ms 4 ruby#4: 1512ms 4 ruby#4: 1267ms 4 ruby#5: 1513ms 4 ruby#5: 1245ms 6 ruby#1: 2122ms 6 ruby#1: 1584ms 6 ruby#2: 2080ms 6 ruby#2: 1532ms 6 ruby#3: 2079ms 6 ruby#3: 1476ms 6 ruby#4: 2021ms 6 ruby#4: 1463ms 6 ruby#5: 1999ms 6 ruby#5: 1461ms 8 ruby#1: 2741ms 8 ruby#1: 1630ms 8 ruby#2: 2711ms 8 ruby#2: 1632ms 8 ruby#3: 2688ms 8 ruby#3: 1654ms 8 ruby#4: 2641ms 8 ruby#4: 1684ms 8 ruby#5: 2656ms 8 ruby#5: 1752ms ```
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❌ Tests Failed✖️no tests failed ✔️62253 tests passed(6 flakes) |
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We can avoid taking this barrier if we're not incremental marking or lazy sweeping. I found this was taking a significant amount of samples when profiling
Psych.loadin multiple ractors due to the vm barrier. With this change, we get significant improvements in ractor benchmarks that allocate lots of objects.-- Psych.load benchmark --