2020-09-08 20:30:44 +00:00
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[package]
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name = "benches"
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version = "0.1.0"
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2021-10-27 00:12:14 +00:00
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edition = "2021"
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2021-11-13 23:06:48 +00:00
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description = "Benchmarks for Bevy engine"
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publish = false
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license = "MIT OR Apache-2.0"
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2020-09-08 20:30:44 +00:00
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[dev-dependencies]
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2022-11-07 19:44:13 +00:00
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glam = "0.22"
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2022-04-26 21:20:13 +00:00
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rand = "0.8"
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rand_chacha = "0.3"
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2022-04-07 20:50:43 +00:00
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criterion = { version = "0.3", features = ["html_reports"] }
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2022-06-20 17:35:54 +00:00
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bevy_app = { path = "../crates/bevy_app" }
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2022-03-01 00:51:07 +00:00
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bevy_ecs = { path = "../crates/bevy_ecs" }
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bench: add `bevy_reflect::{List, Map, Struct}` benchmarks (#3690)
# Objective
Partially addresses #3594.
## Solution
This adds basic benchmarks for `List`, `Map`, and `Struct` implementors, both concrete (`Vec`, `HashMap`, and defined struct types) and dynamic (`DynamicList`, `DynamicMap` and `DynamicStruct`).
A few insights from the benchmarks (all measurements are local on my machine):
- Applying a list with many elements to a list with no elements is slower than applying to a list of the same length:
- 3-4x slower when applying to a `Vec`
- 5-6x slower when applying to a `DynamicList`
I suspect this could be improved by `reserve()`ing the correct length up front, but haven't tested.
- Applying a `DynamicMap` to another `Map` is linear in the number of elements, but applying a `HashMap` seems to be at least quadratic. No intuition on this one.
- Applying like structs (concrete -> concrete, `DynamicStruct` -> `DynamicStruct`) seems to be faster than applying unlike structs.
2022-05-17 04:16:53 +00:00
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bevy_reflect = { path = "../crates/bevy_reflect" }
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2022-03-01 00:51:07 +00:00
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bevy_tasks = { path = "../crates/bevy_tasks" }
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bench: add `bevy_reflect::{List, Map, Struct}` benchmarks (#3690)
# Objective
Partially addresses #3594.
## Solution
This adds basic benchmarks for `List`, `Map`, and `Struct` implementors, both concrete (`Vec`, `HashMap`, and defined struct types) and dynamic (`DynamicList`, `DynamicMap` and `DynamicStruct`).
A few insights from the benchmarks (all measurements are local on my machine):
- Applying a list with many elements to a list with no elements is slower than applying to a list of the same length:
- 3-4x slower when applying to a `Vec`
- 5-6x slower when applying to a `DynamicList`
I suspect this could be improved by `reserve()`ing the correct length up front, but haven't tested.
- Applying a `DynamicMap` to another `Map` is linear in the number of elements, but applying a `HashMap` seems to be at least quadratic. No intuition on this one.
- Applying like structs (concrete -> concrete, `DynamicStruct` -> `DynamicStruct`) seems to be faster than applying unlike structs.
2022-05-17 04:16:53 +00:00
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bevy_utils = { path = "../crates/bevy_utils" }
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2020-09-08 20:30:44 +00:00
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Remove unnecesary branches/panics from Query accesses (#6461)
# Objective
Supercedes #6452. Upon inspection of the [generated assembly](https://gist.github.com/james7132/c2740c6941b80d7912f1e8888e223cbb#file-original-s) of a [simple Bevy binary](https://gist.github.com/james7132/c2740c6941b80d7912f1e8888e223cbb#file-source-rs) compiled with `cargo rustc --release -- --emit asm`, it's apparent that there are multiple unnecessary branches in the generated assembly:
```assembly
.LBB5_5:
cmpq %r10, %r11
je .LBB5_15
movq (%r11), %rcx
movq 328(%r15), %rdx
cmpq %rdx, %rcx
jae .LBB5_14
movq 312(%r15), %rdi
leaq (%rcx,%rcx,2), %rcx
shlq $5, %rcx
movq 336(%r12), %rdx
movq 64(%rdi,%rcx), %rax
cmpq %rdx, %rax
jbe .LBB5_4
leaq (%rdi,%rcx), %rsi
movq 48(%rsi), %rbp
shlq $4, %rdx
cmpq $0, (%rbp,%rdx)
je .LBB5_4
movq 344(%r12), %rbx
cmpq %rbx, %rax
jbe .LBB5_4
shlq $4, %rbx
cmpq $0, (%rbp,%rbx)
je .LBB5_4
addq $8, %r11
movq 88(%rdi,%rcx), %rcx
testq %rcx, %rcx
je .LBB5_5
movq (%rsi), %rax
movq 8(%rbp,%rdx), %rdx
leaq (%rdx,%rdx,4), %rdi
shlq $4, %rdi
movq 32(%rax,%rdi), %rdx
movq 56(%rax,%rdi), %r8
movq 8(%rbp,%rbx), %rbp
leaq (%rbp,%rbp,4), %rbp
shlq $4, %rbp
movq 32(%rax,%rbp), %r9
xorl %ebp, %ebp
jmp .LBB5_13
.p2align 4, 0x90
```
Almost every one of the instructions starting with `j` is a potential branch, which can significantly slow down accesses. Of these, two labels are both common and never used:
```asm
.LBB5_14:
leaq __unnamed_2(%rip), %r8
callq _ZN4core9panicking18panic_bounds_check17h70367088e72af65aE
ud2
.LBB5_4:
callq _ZN8bevy_ecs5query25debug_checked_unreachable17h0855ff520ceaea77E
ud2
.seh_endproc
```
These correpsond to subprocedure calls to panicking due to out of bounds from indexing `Tables` and `debug_checked_unreadable`. Both of which should be inlined and optimized out, but are not.
## Solution
Make `debug_checked_unreachable` a macro to forcibly inline either `unreachable!()` in debug builds, and `std::hint::unreachable_unchecked()` in release mode. Replace the `Tables` and `Archetype` index access with `get(id).unwrap_or_else(|| debug_checked_unreachable!())` to assume that the table or archetype provided exists.
This has no external breaking change of any kind.
The equivalent section of code with these changes removes most of the conditional jump instructions:
```asm
.LBB5_5:
movss (%rbx,%rbp,4), %xmm0
movl %r14d, 4(%r8,%rbp,8)
addss (%rdi,%rbp,4), %xmm0
movss %xmm0, (%rdi,%rbp,4)
incq %rbp
.LBB5_1:
cmpq %rdx, %rbp
jne .LBB5_5
.p2align 4, 0x90
.LBB5_2:
cmpq %rcx, %rax
je .LBB5_6
movq (%rax), %rdx
addq $8, %rax
movq 312(%rsi), %rbp
leaq (%rdx,%rdx,2), %rbx
shlq $5, %rbx
movq 88(%rbp,%rbx), %rdx
testq %rdx, %rdx
je .LBB5_2
leaq (%rbx,%rbp), %r8
movq 336(%r15), %rdi
movq 344(%r15), %r9
movq 48(%rbp,%rbx), %r10
shlq $4, %rdi
movq (%r8), %rbx
movq 8(%r10,%rdi), %rdi
leaq (%rdi,%rdi,4), %rbp
shlq $4, %rbp
movq 32(%rbx,%rbp), %rdi
movq 56(%rbx,%rbp), %r8
shlq $4, %r9
movq 8(%r10,%r9), %rbp
leaq (%rbp,%rbp,4), %rbp
shlq $4, %rbp
movq 32(%rbx,%rbp), %rbx
xorl %ebp, %ebp
jmp .LBB5_5
.LBB5_6:
addq $40, %rsp
popq %rbx
popq %rbp
popq %rdi
popq %rsi
popq %r14
popq %r15
retq
.seh_endproc
```
## Performance
Microbenchmarks results:
<details>
```
group main no-panic-query
----- ---- --------------
busy_systems/01x_entities_03_systems 1.20 42.4±2.66µs ? ?/sec 1.00 35.3±1.68µs ? ?/sec
busy_systems/01x_entities_06_systems 1.32 83.8±3.50µs ? ?/sec 1.00 63.6±1.72µs ? ?/sec
busy_systems/01x_entities_09_systems 1.15 113.3±8.90µs ? ?/sec 1.00 98.2±6.15µs ? ?/sec
busy_systems/01x_entities_12_systems 1.27 160.8±32.44µs ? ?/sec 1.00 126.6±4.70µs ? ?/sec
busy_systems/01x_entities_15_systems 1.12 179.6±3.71µs ? ?/sec 1.00 160.3±11.03µs ? ?/sec
busy_systems/02x_entities_03_systems 1.18 76.8±3.14µs ? ?/sec 1.00 65.2±3.17µs ? ?/sec
busy_systems/02x_entities_06_systems 1.16 144.6±6.10µs ? ?/sec 1.00 124.5±5.14µs ? ?/sec
busy_systems/02x_entities_09_systems 1.19 215.3±9.18µs ? ?/sec 1.00 181.5±5.67µs ? ?/sec
busy_systems/02x_entities_12_systems 1.20 266.7±8.33µs ? ?/sec 1.00 222.0±9.53µs ? ?/sec
busy_systems/02x_entities_15_systems 1.23 338.8±10.53µs ? ?/sec 1.00 276.3±6.94µs ? ?/sec
busy_systems/03x_entities_03_systems 1.43 113.5±5.06µs ? ?/sec 1.00 79.6±1.49µs ? ?/sec
busy_systems/03x_entities_06_systems 1.38 217.3±12.67µs ? ?/sec 1.00 157.5±3.07µs ? ?/sec
busy_systems/03x_entities_09_systems 1.23 308.8±24.75µs ? ?/sec 1.00 251.6±8.93µs ? ?/sec
busy_systems/03x_entities_12_systems 1.05 347.7±12.43µs ? ?/sec 1.00 330.6±11.43µs ? ?/sec
busy_systems/03x_entities_15_systems 1.13 455.5±13.88µs ? ?/sec 1.00 401.7±17.29µs ? ?/sec
busy_systems/04x_entities_03_systems 1.24 144.7±5.89µs ? ?/sec 1.00 116.9±6.29µs ? ?/sec
busy_systems/04x_entities_06_systems 1.24 282.8±21.40µs ? ?/sec 1.00 228.6±21.31µs ? ?/sec
busy_systems/04x_entities_09_systems 1.35 431.8±14.10µs ? ?/sec 1.00 319.6±9.83µs ? ?/sec
busy_systems/04x_entities_12_systems 1.16 493.8±22.87µs ? ?/sec 1.00 424.9±15.24µs ? ?/sec
busy_systems/04x_entities_15_systems 1.10 587.5±23.25µs ? ?/sec 1.00 531.7±16.32µs ? ?/sec
busy_systems/05x_entities_03_systems 1.14 148.2±9.61µs ? ?/sec 1.00 129.5±4.32µs ? ?/sec
busy_systems/05x_entities_06_systems 1.31 359.7±17.46µs ? ?/sec 1.00 273.6±10.55µs ? ?/sec
busy_systems/05x_entities_09_systems 1.22 473.5±23.11µs ? ?/sec 1.00 389.3±13.62µs ? ?/sec
busy_systems/05x_entities_12_systems 1.05 562.9±20.76µs ? ?/sec 1.00 536.5±24.35µs ? ?/sec
busy_systems/05x_entities_15_systems 1.23 818.5±28.70µs ? ?/sec 1.00 666.6±45.87µs ? ?/sec
contrived/01x_entities_03_systems 1.27 27.5±0.49µs ? ?/sec 1.00 21.6±1.71µs ? ?/sec
contrived/01x_entities_06_systems 1.22 49.9±1.18µs ? ?/sec 1.00 40.7±2.62µs ? ?/sec
contrived/01x_entities_09_systems 1.30 72.3±2.39µs ? ?/sec 1.00 55.4±2.60µs ? ?/sec
contrived/01x_entities_12_systems 1.28 94.3±9.44µs ? ?/sec 1.00 73.7±3.62µs ? ?/sec
contrived/01x_entities_15_systems 1.25 118.0±2.43µs ? ?/sec 1.00 94.1±3.99µs ? ?/sec
contrived/02x_entities_03_systems 1.23 41.6±1.71µs ? ?/sec 1.00 33.7±2.30µs ? ?/sec
contrived/02x_entities_06_systems 1.19 78.6±2.63µs ? ?/sec 1.00 65.9±2.35µs ? ?/sec
contrived/02x_entities_09_systems 1.28 113.6±3.60µs ? ?/sec 1.00 88.6±3.60µs ? ?/sec
contrived/02x_entities_12_systems 1.20 146.4±5.75µs ? ?/sec 1.00 121.7±3.35µs ? ?/sec
contrived/02x_entities_15_systems 1.23 178.5±4.86µs ? ?/sec 1.00 145.7±4.00µs ? ?/sec
contrived/03x_entities_03_systems 1.42 58.3±2.77µs ? ?/sec 1.00 41.1±1.54µs ? ?/sec
contrived/03x_entities_06_systems 1.32 108.5±7.30µs ? ?/sec 1.00 82.4±4.86µs ? ?/sec
contrived/03x_entities_09_systems 1.23 153.7±4.61µs ? ?/sec 1.00 125.0±4.76µs ? ?/sec
contrived/03x_entities_12_systems 1.18 197.5±5.12µs ? ?/sec 1.00 166.8±8.14µs ? ?/sec
contrived/03x_entities_15_systems 1.23 238.8±6.38µs ? ?/sec 1.00 194.6±4.55µs ? ?/sec
contrived/04x_entities_03_systems 1.34 66.4±3.42µs ? ?/sec 1.00 49.5±1.98µs ? ?/sec
contrived/04x_entities_06_systems 1.27 134.3±4.86µs ? ?/sec 1.00 105.8±3.58µs ? ?/sec
contrived/04x_entities_09_systems 1.26 193.2±3.83µs ? ?/sec 1.00 153.0±5.60µs ? ?/sec
contrived/04x_entities_12_systems 1.16 237.1±5.78µs ? ?/sec 1.00 204.9±18.77µs ? ?/sec
contrived/04x_entities_15_systems 1.17 289.2±4.76µs ? ?/sec 1.00 246.3±8.57µs ? ?/sec
contrived/05x_entities_03_systems 1.26 80.4±2.90µs ? ?/sec 1.00 63.7±3.07µs ? ?/sec
contrived/05x_entities_06_systems 1.27 161.6±13.47µs ? ?/sec 1.00 127.2±5.59µs ? ?/sec
contrived/05x_entities_09_systems 1.22 228.0±7.76µs ? ?/sec 1.00 186.2±7.68µs ? ?/sec
contrived/05x_entities_12_systems 1.20 289.5±6.21µs ? ?/sec 1.00 241.8±7.52µs ? ?/sec
contrived/05x_entities_15_systems 1.18 357.3±11.24µs ? ?/sec 1.00 302.7±7.21µs ? ?/sec
heavy_compute/base 1.01 302.4±3.52µs ? ?/sec 1.00 300.2±3.40µs ? ?/sec
iter_fragmented/base 1.00 348.1±7.51ns ? ?/sec 1.01 351.9±8.32ns ? ?/sec
iter_fragmented/foreach 1.03 239.8±23.78ns ? ?/sec 1.00 233.8±18.12ns ? ?/sec
iter_fragmented/foreach_wide 1.00 3.9±0.13µs ? ?/sec 1.02 4.0±0.22µs ? ?/sec
iter_fragmented/wide 1.18 4.6±0.15µs ? ?/sec 1.00 3.9±0.10µs ? ?/sec
iter_fragmented_sparse/base 1.02 8.1±0.15ns ? ?/sec 1.00 7.9±0.56ns ? ?/sec
iter_fragmented_sparse/foreach 1.00 7.8±0.22ns ? ?/sec 1.01 7.9±0.62ns ? ?/sec
iter_fragmented_sparse/foreach_wide 1.00 37.2±1.17ns ? ?/sec 1.10 40.9±0.95ns ? ?/sec
iter_fragmented_sparse/wide 1.09 48.4±2.13ns ? ?/sec 1.00 44.5±18.34ns ? ?/sec
iter_simple/base 1.02 8.4±0.10µs ? ?/sec 1.00 8.2±0.14µs ? ?/sec
iter_simple/foreach 1.01 8.3±0.07µs ? ?/sec 1.00 8.2±0.09µs ? ?/sec
iter_simple/foreach_sparse_set 1.00 25.3±0.32µs ? ?/sec 1.02 25.7±0.42µs ? ?/sec
iter_simple/foreach_wide 1.03 41.1±0.94µs ? ?/sec 1.00 39.9±0.41µs ? ?/sec
iter_simple/foreach_wide_sparse_set 1.05 123.6±2.05µs ? ?/sec 1.00 118.1±2.78µs ? ?/sec
iter_simple/sparse_set 1.14 30.5±1.40µs ? ?/sec 1.00 26.9±0.64µs ? ?/sec
iter_simple/system 1.01 8.4±0.25µs ? ?/sec 1.00 8.4±0.11µs ? ?/sec
iter_simple/wide 1.18 48.2±0.62µs ? ?/sec 1.00 40.7±0.38µs ? ?/sec
iter_simple/wide_sparse_set 1.12 140.8±21.56µs ? ?/sec 1.00 126.0±2.30µs ? ?/sec
query_get/50000_entities_sparse 1.17 378.6±7.60µs ? ?/sec 1.00 324.1±23.17µs ? ?/sec
query_get/50000_entities_table 1.08 330.9±10.90µs ? ?/sec 1.00 306.8±4.98µs ? ?/sec
query_get_component/50000_entities_sparse 1.00 976.7±19.55µs ? ?/sec 1.00 979.8±35.87µs ? ?/sec
query_get_component/50000_entities_table 1.00 1029.0±15.11µs ? ?/sec 1.05 1080.0±59.18µs ? ?/sec
query_get_component_simple/system 1.13 839.7±14.18µs ? ?/sec 1.00 742.8±10.72µs ? ?/sec
query_get_component_simple/unchecked 1.01 909.0±15.17µs ? ?/sec 1.00 898.0±13.56µs ? ?/sec
query_get_many_10/50000_calls_sparse 1.04 5.5±0.54ms ? ?/sec 1.00 5.3±0.67ms ? ?/sec
query_get_many_10/50000_calls_table 1.01 4.9±0.49ms ? ?/sec 1.00 4.8±0.45ms ? ?/sec
query_get_many_2/50000_calls_sparse 1.28 848.4±210.89µs ? ?/sec 1.00 664.8±47.69µs ? ?/sec
query_get_many_2/50000_calls_table 1.05 779.0±73.85µs ? ?/sec 1.00 739.2±83.02µs ? ?/sec
query_get_many_5/50000_calls_sparse 1.05 2.4±0.37ms ? ?/sec 1.00 2.3±0.33ms ? ?/sec
query_get_many_5/50000_calls_table 1.00 1939.9±75.22µs ? ?/sec 1.04 2.0±0.19ms ? ?/sec
run_criteria/yes_using_query/001_systems 1.00 3.7±0.38µs ? ?/sec 1.30 4.9±0.14µs ? ?/sec
run_criteria/yes_using_query/006_systems 1.00 8.9±0.40µs ? ?/sec 1.17 10.3±0.57µs ? ?/sec
run_criteria/yes_using_query/011_systems 1.00 13.9±0.49µs ? ?/sec 1.08 15.0±0.89µs ? ?/sec
run_criteria/yes_using_query/016_systems 1.00 18.8±0.74µs ? ?/sec 1.00 18.8±1.43µs ? ?/sec
run_criteria/yes_using_query/021_systems 1.07 24.1±0.87µs ? ?/sec 1.00 22.6±1.58µs ? ?/sec
run_criteria/yes_using_query/026_systems 1.04 27.9±0.62µs ? ?/sec 1.00 26.8±1.71µs ? ?/sec
run_criteria/yes_using_query/031_systems 1.09 33.3±1.03µs ? ?/sec 1.00 30.5±2.18µs ? ?/sec
run_criteria/yes_using_query/036_systems 1.14 38.7±0.80µs ? ?/sec 1.00 33.9±1.75µs ? ?/sec
run_criteria/yes_using_query/041_systems 1.18 43.7±1.07µs ? ?/sec 1.00 37.0±2.39µs ? ?/sec
run_criteria/yes_using_query/046_systems 1.14 47.6±1.16µs ? ?/sec 1.00 41.9±2.09µs ? ?/sec
run_criteria/yes_using_query/051_systems 1.17 52.9±2.04µs ? ?/sec 1.00 45.3±1.75µs ? ?/sec
run_criteria/yes_using_query/056_systems 1.25 59.2±2.38µs ? ?/sec 1.00 47.2±2.01µs ? ?/sec
run_criteria/yes_using_query/061_systems 1.28 66.1±15.84µs ? ?/sec 1.00 51.5±2.47µs ? ?/sec
run_criteria/yes_using_query/066_systems 1.28 70.2±2.57µs ? ?/sec 1.00 54.7±2.58µs ? ?/sec
run_criteria/yes_using_query/071_systems 1.30 75.5±2.27µs ? ?/sec 1.00 58.2±3.31µs ? ?/sec
run_criteria/yes_using_query/076_systems 1.26 81.5±2.66µs ? ?/sec 1.00 64.5±3.13µs ? ?/sec
run_criteria/yes_using_query/081_systems 1.29 89.7±2.58µs ? ?/sec 1.00 69.3±3.47µs ? ?/sec
run_criteria/yes_using_query/086_systems 1.33 95.6±3.39µs ? ?/sec 1.00 71.8±3.48µs ? ?/sec
run_criteria/yes_using_query/091_systems 1.25 102.0±3.67µs ? ?/sec 1.00 81.4±4.82µs ? ?/sec
run_criteria/yes_using_query/096_systems 1.33 111.7±3.29µs ? ?/sec 1.00 83.8±4.15µs ? ?/sec
run_criteria/yes_using_query/101_systems 1.29 113.2±12.04µs ? ?/sec 1.00 87.7±5.15µs ? ?/sec
world_query_for_each/50000_entities_sparse 1.00 47.4±0.51µs ? ?/sec 1.00 47.3±0.33µs ? ?/sec
world_query_for_each/50000_entities_table 1.00 27.2±0.50µs ? ?/sec 1.00 27.2±0.17µs ? ?/sec
world_query_get/50000_entities_sparse_wide 1.09 210.5±1.78µs ? ?/sec 1.00 192.5±2.61µs ? ?/sec
world_query_get/50000_entities_table 1.00 127.7±2.09µs ? ?/sec 1.07 136.2±5.95µs ? ?/sec
world_query_get/50000_entities_table_wide 1.00 209.8±2.37µs ? ?/sec 1.15 240.6±2.04µs ? ?/sec
world_query_iter/50000_entities_sparse 1.00 54.2±0.36µs ? ?/sec 1.01 54.7±0.61µs ? ?/sec
world_query_iter/50000_entities_table 1.00 27.2±0.31µs ? ?/sec 1.00 27.3±0.64µs ? ?/sec
```
</details>
NOTE: This PR includes a change to enable LTO on our benchmarks to get a "fully optimized" baseline for our benchmarks. Both the main and the current PR's results were with LTO enabled.
2022-11-04 06:04:55 +00:00
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[profile.release]
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opt-level = 3
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lto = true
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2022-11-04 17:53:54 +00:00
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[[bench]]
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name = "change_detection"
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path = "benches/bevy_ecs/change_detection.rs"
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harness = false
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2022-04-07 20:50:43 +00:00
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[[bench]]
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2022-07-04 14:17:45 +00:00
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name = "ecs"
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path = "benches/bevy_ecs/benches.rs"
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2022-06-20 17:35:54 +00:00
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harness = false
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bench: add `bevy_reflect::{List, Map, Struct}` benchmarks (#3690)
# Objective
Partially addresses #3594.
## Solution
This adds basic benchmarks for `List`, `Map`, and `Struct` implementors, both concrete (`Vec`, `HashMap`, and defined struct types) and dynamic (`DynamicList`, `DynamicMap` and `DynamicStruct`).
A few insights from the benchmarks (all measurements are local on my machine):
- Applying a list with many elements to a list with no elements is slower than applying to a list of the same length:
- 3-4x slower when applying to a `Vec`
- 5-6x slower when applying to a `DynamicList`
I suspect this could be improved by `reserve()`ing the correct length up front, but haven't tested.
- Applying a `DynamicMap` to another `Map` is linear in the number of elements, but applying a `HashMap` seems to be at least quadratic. No intuition on this one.
- Applying like structs (concrete -> concrete, `DynamicStruct` -> `DynamicStruct`) seems to be faster than applying unlike structs.
2022-05-17 04:16:53 +00:00
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[[bench]]
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name = "reflect_list"
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path = "benches/bevy_reflect/list.rs"
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harness = false
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[[bench]]
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name = "reflect_map"
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path = "benches/bevy_reflect/map.rs"
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harness = false
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[[bench]]
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name = "reflect_struct"
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path = "benches/bevy_reflect/struct.rs"
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harness = false
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2020-09-08 20:30:44 +00:00
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[[bench]]
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name = "iter"
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path = "benches/bevy_tasks/iter.rs"
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2020-09-10 19:54:24 +00:00
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harness = false
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