2020-09-08 20:30:44 +00:00
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[package]
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name = "benches"
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2021-10-27 00:12:14 +00:00
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edition = "2021"
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2024-04-26 11:55:03 +00:00
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description = "Benchmarks that test Bevy's performance"
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2021-11-13 23:06:48 +00:00
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publish = false
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license = "MIT OR Apache-2.0"
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2024-12-04 16:20:11 +00:00
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# Do not automatically discover benchmarks, we specify them manually instead.
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autobenches = false
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2020-09-08 20:30:44 +00:00
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[dev-dependencies]
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2024-09-23 19:44:02 +00:00
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glam = "0.29"
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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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2024-05-06 20:49:32 +00:00
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bevy_ecs = { path = "../crates/bevy_ecs", features = ["multi_threaded"] }
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2024-07-15 13:39:41 +00:00
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bevy_hierarchy = { path = "../crates/bevy_hierarchy" }
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bevy_math = { path = "../crates/bevy_math" }
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2024-11-22 18:14:16 +00:00
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bevy_picking = { path = "../crates/bevy_picking", features = [
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"bevy_mesh_picking_backend",
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] }
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2024-08-11 03:02:06 +00:00
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bevy_reflect = { path = "../crates/bevy_reflect", features = ["functions"] }
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2024-07-15 13:39:41 +00:00
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bevy_render = { path = "../crates/bevy_render" }
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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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2024-08-14 13:23:00 +00:00
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# make bevy_render compile on linux. x11 vs wayland does not matter here as the benches do not actually use a window
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[target.'cfg(target_os = "linux")'.dev-dependencies]
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bevy_winit = { path = "../crates/bevy_winit", features = ["x11"] }
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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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2022-04-07 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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2024-12-04 16:20:11 +00:00
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path = "benches/bevy_ecs/main.rs"
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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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harness = false
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2023-08-25 14:30:26 +00:00
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[[bench]]
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2024-12-04 16:20:11 +00:00
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name = "math"
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path = "benches/bevy_math/main.rs"
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2023-08-25 14:30:26 +00:00
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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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2024-12-04 16:20:11 +00:00
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name = "picking"
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path = "benches/bevy_picking/main.rs"
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2020-09-10 19:54:24 +00:00
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harness = false
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Add Beziers to `bevy_math` (#7653)
# Objective
- Adds foundational math for Bezier curves, useful for UI/2D/3D animation and smooth paths.
https://user-images.githubusercontent.com/2632925/218883143-e138f994-1795-40da-8c59-21d779666991.mp4
## Solution
- Adds the generic `Bezier` type, and a `Point` trait. The `Point` trait allows us to use control points of any dimension, as long as they support vector math. I've implemented it for `f32`(1D), `Vec2`(2D), and `Vec3`/`Vec3A`(3D).
- Adds `CubicBezierEasing` on top of `Bezier` with the addition of an implementation of cubic Bezier easing, which is a foundational tool for UI animation.
- This involves solving for $t$ in the parametric Bezier function $B(t)$ using the Newton-Raphson method to find a value with error $\leq$ 1e-7, capped at 8 iterations.
- Added type aliases for common Bezier curves: `CubicBezier2d`, `CubicBezier3d`, `QuadraticBezier2d`, and `QuadraticBezier3d`. These types use `Vec3A` to represent control points, as this was found to have an 80-90% speedup over using `Vec3`.
- Benchmarking shows quadratic/cubic Bezier evaluations $B(t)$ take \~1.8/2.4ns respectively. Easing, which requires an iterative solve takes \~50ns for cubic Beziers.
---
## Changelog
- Added `CubicBezier2d`, `CubicBezier3d`, `QuadraticBezier2d`, and `QuadraticBezier3d` types with methods for sampling position, velocity, and acceleration. The generic `Bezier` type is also available, and generic over any degree of Bezier curve.
- Added `CubicBezierEasing`, with additional methods to allow for smooth easing animations.
2023-02-20 18:34:52 +00:00
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[[bench]]
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2024-12-04 16:20:11 +00:00
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name = "reflect"
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path = "benches/bevy_reflect/main.rs"
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Add Beziers to `bevy_math` (#7653)
# Objective
- Adds foundational math for Bezier curves, useful for UI/2D/3D animation and smooth paths.
https://user-images.githubusercontent.com/2632925/218883143-e138f994-1795-40da-8c59-21d779666991.mp4
## Solution
- Adds the generic `Bezier` type, and a `Point` trait. The `Point` trait allows us to use control points of any dimension, as long as they support vector math. I've implemented it for `f32`(1D), `Vec2`(2D), and `Vec3`/`Vec3A`(3D).
- Adds `CubicBezierEasing` on top of `Bezier` with the addition of an implementation of cubic Bezier easing, which is a foundational tool for UI animation.
- This involves solving for $t$ in the parametric Bezier function $B(t)$ using the Newton-Raphson method to find a value with error $\leq$ 1e-7, capped at 8 iterations.
- Added type aliases for common Bezier curves: `CubicBezier2d`, `CubicBezier3d`, `QuadraticBezier2d`, and `QuadraticBezier3d`. These types use `Vec3A` to represent control points, as this was found to have an 80-90% speedup over using `Vec3`.
- Benchmarking shows quadratic/cubic Bezier evaluations $B(t)$ take \~1.8/2.4ns respectively. Easing, which requires an iterative solve takes \~50ns for cubic Beziers.
---
## Changelog
- Added `CubicBezier2d`, `CubicBezier3d`, `QuadraticBezier2d`, and `QuadraticBezier3d` types with methods for sampling position, velocity, and acceleration. The generic `Bezier` type is also available, and generic over any degree of Bezier curve.
- Added `CubicBezierEasing`, with additional methods to allow for smooth easing animations.
2023-02-20 18:34:52 +00:00
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harness = false
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Optimize `Entity::eq` (#10519)
(This is my first PR here, so I've probably missed some things. Please
let me know what else I should do to help you as a reviewer!)
# Objective
Due to https://github.com/rust-lang/rust/issues/117800, the `derive`'d
`PartialEq::eq` on `Entity` isn't as good as it could be. Since that's
used in hashtable lookup, let's improve it.
## Solution
The derived `PartialEq::eq` short-circuits if the generation doesn't
match. However, having a branch there is sub-optimal, especially on
64-bit systems like x64 that could just load the whole `Entity` in one
load anyway.
Due to complications around `poison` in LLVM and the exact details of
what unsafe code is allowed to do with reference in Rust
(https://github.com/rust-lang/unsafe-code-guidelines/issues/346), LLVM
isn't allowed to completely remove the short-circuiting. `&Entity` is
marked `dereferencable(8)` so LLVM knows it's allowed to *load* all 8
bytes -- and does so -- but it has to assume that the `index` might be
undef/poison if the `generation` doesn't match, and thus while it finds
a way to do it without needing a branch, it has to do something slightly
more complicated than optimal to combine the results. (LLVM is allowed
to change non-short-circuiting code to use branches, but not the other
way around.)
Here's a link showing the codegen today:
<https://rust.godbolt.org/z/9WzjxrY7c>
```rust
#[no_mangle]
pub fn demo_eq_ref(a: &Entity, b: &Entity) -> bool {
a == b
}
```
ends up generating the following assembly:
```asm
demo_eq_ref:
movq xmm0, qword ptr [rdi]
movq xmm1, qword ptr [rsi]
pcmpeqd xmm1, xmm0
pshufd xmm0, xmm1, 80
movmskpd eax, xmm0
cmp eax, 3
sete al
ret
```
(It's usually not this bad in real uses after inlining and LTO, but it
makes a strong demo.)
This PR manually implements `PartialEq::eq` *without* short-circuiting,
and because that tells LLVM that neither the generations nor the index
can be poison, it doesn't need to be so careful and can generate the
"just compare the two 64-bit values" code you'd have probably already
expected:
```asm
demo_eq_ref:
mov rax, qword ptr [rsi]
cmp qword ptr [rdi], rax
sete al
ret
```
Since this doesn't change the representation of `Entity`, if it's
instead passed by *value*, then each `Entity` is two `u32` registers,
and the old and the new code do exactly the same thing. (Other
approaches, like changing `Entity` to be `[u32; 2]` or `u64`, affect
this case.)
This should hopefully merge easily with changes like
https://github.com/bevyengine/bevy/pull/9907 that also want to change
`Entity`.
## Benchmarks
I'm not super-confident that I got my machine fully consistent for
benchmarking, but whether I run the old or the new one first I get
reasonably consistent results.
Here's a fairly typical example of the benchmarks I added in this PR:
![image](https://github.com/bevyengine/bevy/assets/18526288/24226308-4616-4082-b0ff-88fc06285ef1)
Building the sets seems to be basically the same. It's usually reported
as noise, but sometimes I see a few percent slower or faster.
But lookup hits in particular -- since a hit checks that the key is
equal -- consistently shows around 10% improvement.
`cargo run --example many_cubes --features bevy/trace_tracy --release --
--benchmark` showed as slightly faster with this change, though if I had
to bet I'd probably say it's more noise than meaningful (but at least
it's not worse either):
![image](https://github.com/bevyengine/bevy/assets/18526288/58bb8c96-9c45-487f-a5ab-544bbfe9fba0)
This is my first PR here -- and my first time running Tracy -- so please
let me know what else I should run, or run things on your own more
reliable machines to double-check.
---
## Changelog
(probably not worth including)
Changed: micro-optimized `Entity::eq` to help LLVM slightly.
## Migration Guide
(I really hope nobody was using this on uninitialized entities where
sufficiently tortured `unsafe` could could technically notice that this
has changed.)
2023-11-14 02:06:21 +00:00
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|
2024-03-29 20:30:26 +00:00
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|
[[bench]]
|
2024-12-04 16:20:11 +00:00
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|
|
name = "render"
|
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path = "benches/bevy_render/main.rs"
|
2024-03-29 20:30:26 +00:00
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harness = false
|
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|
Optimize `Entity::eq` (#10519)
(This is my first PR here, so I've probably missed some things. Please
let me know what else I should do to help you as a reviewer!)
# Objective
Due to https://github.com/rust-lang/rust/issues/117800, the `derive`'d
`PartialEq::eq` on `Entity` isn't as good as it could be. Since that's
used in hashtable lookup, let's improve it.
## Solution
The derived `PartialEq::eq` short-circuits if the generation doesn't
match. However, having a branch there is sub-optimal, especially on
64-bit systems like x64 that could just load the whole `Entity` in one
load anyway.
Due to complications around `poison` in LLVM and the exact details of
what unsafe code is allowed to do with reference in Rust
(https://github.com/rust-lang/unsafe-code-guidelines/issues/346), LLVM
isn't allowed to completely remove the short-circuiting. `&Entity` is
marked `dereferencable(8)` so LLVM knows it's allowed to *load* all 8
bytes -- and does so -- but it has to assume that the `index` might be
undef/poison if the `generation` doesn't match, and thus while it finds
a way to do it without needing a branch, it has to do something slightly
more complicated than optimal to combine the results. (LLVM is allowed
to change non-short-circuiting code to use branches, but not the other
way around.)
Here's a link showing the codegen today:
<https://rust.godbolt.org/z/9WzjxrY7c>
```rust
#[no_mangle]
pub fn demo_eq_ref(a: &Entity, b: &Entity) -> bool {
a == b
}
```
ends up generating the following assembly:
```asm
demo_eq_ref:
movq xmm0, qword ptr [rdi]
movq xmm1, qword ptr [rsi]
pcmpeqd xmm1, xmm0
pshufd xmm0, xmm1, 80
movmskpd eax, xmm0
cmp eax, 3
sete al
ret
```
(It's usually not this bad in real uses after inlining and LTO, but it
makes a strong demo.)
This PR manually implements `PartialEq::eq` *without* short-circuiting,
and because that tells LLVM that neither the generations nor the index
can be poison, it doesn't need to be so careful and can generate the
"just compare the two 64-bit values" code you'd have probably already
expected:
```asm
demo_eq_ref:
mov rax, qword ptr [rsi]
cmp qword ptr [rdi], rax
sete al
ret
```
Since this doesn't change the representation of `Entity`, if it's
instead passed by *value*, then each `Entity` is two `u32` registers,
and the old and the new code do exactly the same thing. (Other
approaches, like changing `Entity` to be `[u32; 2]` or `u64`, affect
this case.)
This should hopefully merge easily with changes like
https://github.com/bevyengine/bevy/pull/9907 that also want to change
`Entity`.
## Benchmarks
I'm not super-confident that I got my machine fully consistent for
benchmarking, but whether I run the old or the new one first I get
reasonably consistent results.
Here's a fairly typical example of the benchmarks I added in this PR:
![image](https://github.com/bevyengine/bevy/assets/18526288/24226308-4616-4082-b0ff-88fc06285ef1)
Building the sets seems to be basically the same. It's usually reported
as noise, but sometimes I see a few percent slower or faster.
But lookup hits in particular -- since a hit checks that the key is
equal -- consistently shows around 10% improvement.
`cargo run --example many_cubes --features bevy/trace_tracy --release --
--benchmark` showed as slightly faster with this change, though if I had
to bet I'd probably say it's more noise than meaningful (but at least
it's not worse either):
![image](https://github.com/bevyengine/bevy/assets/18526288/58bb8c96-9c45-487f-a5ab-544bbfe9fba0)
This is my first PR here -- and my first time running Tracy -- so please
let me know what else I should run, or run things on your own more
reliable machines to double-check.
---
## Changelog
(probably not worth including)
Changed: micro-optimized `Entity::eq` to help LLVM slightly.
## Migration Guide
(I really hope nobody was using this on uninitialized entities where
sufficiently tortured `unsafe` could could technically notice that this
has changed.)
2023-11-14 02:06:21 +00:00
|
|
|
[[bench]]
|
2024-12-04 16:20:11 +00:00
|
|
|
name = "tasks"
|
|
|
|
path = "benches/bevy_tasks/main.rs"
|
Optimize `Entity::eq` (#10519)
(This is my first PR here, so I've probably missed some things. Please
let me know what else I should do to help you as a reviewer!)
# Objective
Due to https://github.com/rust-lang/rust/issues/117800, the `derive`'d
`PartialEq::eq` on `Entity` isn't as good as it could be. Since that's
used in hashtable lookup, let's improve it.
## Solution
The derived `PartialEq::eq` short-circuits if the generation doesn't
match. However, having a branch there is sub-optimal, especially on
64-bit systems like x64 that could just load the whole `Entity` in one
load anyway.
Due to complications around `poison` in LLVM and the exact details of
what unsafe code is allowed to do with reference in Rust
(https://github.com/rust-lang/unsafe-code-guidelines/issues/346), LLVM
isn't allowed to completely remove the short-circuiting. `&Entity` is
marked `dereferencable(8)` so LLVM knows it's allowed to *load* all 8
bytes -- and does so -- but it has to assume that the `index` might be
undef/poison if the `generation` doesn't match, and thus while it finds
a way to do it without needing a branch, it has to do something slightly
more complicated than optimal to combine the results. (LLVM is allowed
to change non-short-circuiting code to use branches, but not the other
way around.)
Here's a link showing the codegen today:
<https://rust.godbolt.org/z/9WzjxrY7c>
```rust
#[no_mangle]
pub fn demo_eq_ref(a: &Entity, b: &Entity) -> bool {
a == b
}
```
ends up generating the following assembly:
```asm
demo_eq_ref:
movq xmm0, qword ptr [rdi]
movq xmm1, qword ptr [rsi]
pcmpeqd xmm1, xmm0
pshufd xmm0, xmm1, 80
movmskpd eax, xmm0
cmp eax, 3
sete al
ret
```
(It's usually not this bad in real uses after inlining and LTO, but it
makes a strong demo.)
This PR manually implements `PartialEq::eq` *without* short-circuiting,
and because that tells LLVM that neither the generations nor the index
can be poison, it doesn't need to be so careful and can generate the
"just compare the two 64-bit values" code you'd have probably already
expected:
```asm
demo_eq_ref:
mov rax, qword ptr [rsi]
cmp qword ptr [rdi], rax
sete al
ret
```
Since this doesn't change the representation of `Entity`, if it's
instead passed by *value*, then each `Entity` is two `u32` registers,
and the old and the new code do exactly the same thing. (Other
approaches, like changing `Entity` to be `[u32; 2]` or `u64`, affect
this case.)
This should hopefully merge easily with changes like
https://github.com/bevyengine/bevy/pull/9907 that also want to change
`Entity`.
## Benchmarks
I'm not super-confident that I got my machine fully consistent for
benchmarking, but whether I run the old or the new one first I get
reasonably consistent results.
Here's a fairly typical example of the benchmarks I added in this PR:
![image](https://github.com/bevyengine/bevy/assets/18526288/24226308-4616-4082-b0ff-88fc06285ef1)
Building the sets seems to be basically the same. It's usually reported
as noise, but sometimes I see a few percent slower or faster.
But lookup hits in particular -- since a hit checks that the key is
equal -- consistently shows around 10% improvement.
`cargo run --example many_cubes --features bevy/trace_tracy --release --
--benchmark` showed as slightly faster with this change, though if I had
to bet I'd probably say it's more noise than meaningful (but at least
it's not worse either):
![image](https://github.com/bevyengine/bevy/assets/18526288/58bb8c96-9c45-487f-a5ab-544bbfe9fba0)
This is my first PR here -- and my first time running Tracy -- so please
let me know what else I should run, or run things on your own more
reliable machines to double-check.
---
## Changelog
(probably not worth including)
Changed: micro-optimized `Entity::eq` to help LLVM slightly.
## Migration Guide
(I really hope nobody was using this on uninitialized entities where
sufficiently tortured `unsafe` could could technically notice that this
has changed.)
2023-11-14 02:06:21 +00:00
|
|
|
harness = false
|