Use 1.5x growth factor for LocalVector #100944

Nazarwadim · 2024-12-30T17:56:34Z

This is explained here godotengine/godot-proposals#7366.

Also, now if we use resize, as much memory as we need will be allocated if we increase the size by a value greater than 1.5 * capacity.

I tested in tps-demo and the memory usage difference is 2-3 megabytes. Did not notice the difference in FPS.

RandomShaper · 2025-01-02T11:46:44Z

Makes sense!

I'd still ask for a couple more things before considering this complete:

Applying 1.5 factor also to CowData::_get_alloc_size().
Benchmarking.

core/templates/local_vector.h

Nazarwadim · 2025-01-03T08:05:31Z

Applying 1.5 factor also to CowData::_get_alloc_size()

Well, there are two ways to do it. The first is to cache capacity. Another is to use a binary search among the Fibonacci numbers and choose a number slightly greater than or equal to the size.

Benchmarking.

What specific benchmarks are needed?

RandomShaper · 2025-01-03T11:22:01Z

Applying 1.5 factor also to CowData::_get_alloc_size()

Well, there are two ways to do it. The first is to cache capacity. Another is to use a binary search among the Fibonacci numbers and choose a number slightly greater than or equal to the size.

Caching capacity sounds fine to me.

Benchmarking.

What specific benchmarks are needed?

Two possibilities (not necessarily exclusive):

Via the Godot benchmarks project, or a relevant subset of it.
Via a game project (Godot TPS demo or one of the platformers), paying attention to improvements in fps (or, well, frame time).

This change would ideally require benchmarking across a number of platforms, but, well, since we already understand why this new approach is theoretically beneficial, we would get enough peace of mind if we verify empircal benefits on a single platform. If only we had the mimalloc PR merged, we would be sure the change is a good for one platform as for the others.

Nazarwadim · 2025-01-03T12:01:11Z

Caching capacity sounds fine to me.

godot/core/templates/cowdata.h

Lines 101 to 103 in bdf625b

    
           static constexpr size_t REF_COUNT_OFFSET = 0; 
        
           static constexpr size_t SIZE_OFFSET = ((REF_COUNT_OFFSET + sizeof(SafeNumeric<USize>)) % alignof(USize) == 0) ? (REF_COUNT_OFFSET + sizeof(SafeNumeric<USize>)) : ((REF_COUNT_OFFSET + sizeof(SafeNumeric<USize>)) + alignof(USize) - ((REF_COUNT_OFFSET + sizeof(SafeNumeric<USize>)) % alignof(USize))); 
        
           static constexpr size_t DATA_OFFSET = ((SIZE_OFFSET + sizeof(USize)) % alignof(max_align_t) == 0) ? (SIZE_OFFSET + sizeof(USize)) : ((SIZE_OFFSET + sizeof(USize)) + alignof(max_align_t) - ((SIZE_OFFSET + sizeof(USize)) % alignof(max_align_t)));

When I tried to add capacity here, the size of the metadata became 32 bytes, not 24. It looks like ptr should be aligned by 16 bytes.

Nazarwadim · 2025-01-04T09:55:53Z

Via a game project (Godot TPS demo or one of the platformers), paying attention to improvements in fps (or, well, frame time).

Tested in tps-demo with minimum graphics settings. Before that, the average was 408 fps, now it is 422. I tested in dynamics, where I used all the mechanics of the game and moved along the path to the reactor.
I did two tests, but kept the last one, because in the first test I did not think that the fps would be higher.

local_vector1.5-tps-demp-fps-test.ods

hpvb · 2025-01-04T22:10:52Z

Same comment as the other PR: https://godbolt.org/z/Phc53zGq1

The floating point calculation isn't necessary

Nazarwadim · 2025-01-05T10:31:05Z

From this comment: #101138 (comment)

So I will correct myself.

Using

capacity = capacity + ((1 + capacity) >> 1);

gives the same sequence https://godbolt.org/z/ssjT35ETW.

RandomShaper · 2025-01-06T17:12:01Z

There are also some changes to inlining. Maybe this PR would better keep that out of its scope.

Calinou

Tested locally (rebased on top of master 6f3cc27), it works as expected.

Benchmark

PC specifications

CPU: Intel Core i9-13900K
GPU: NVIDIA GeForce RTX 4090
RAM: 64 GB (2×32 GB DDR5-5800 C30)
SSD: Solidigm P44 Pro 2 TB
OS: Linux (Fedora 41)

Using a release export template with LTO.

a: master 6f3cc27
b: This PR

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{
	"category": "C++ > String Manipulation",
	"name": "Nocasecmp To",
	"results": {
		"time": {
			"a": 57.66,
			"b": 57.54,
			"a_div_b": 1.0020855057351408
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	}
}
{
	"category": "C++ > String Manipulation",
	"name": "Pad Decimals",
	"results": {
		"time": {
			"a": 302.2,
			"b": 305.5,
			"a_div_b": 0.9891980360065467
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	}
}
{
	"category": "C++ > String Manipulation",
	"name": "Pad Decimals Pre Constructed",
	"results": {
		"time": {
			"a": 57.68,
			"b": 57.52,
			"a_div_b": 1.0027816411682893
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}
{
	"category": "C++ > String Manipulation",
	"name": "Pad Zeros",
	"results": {
		"time": {
			"a": 210.9,
			"b": 218.6,
			"a_div_b": 0.9647758462946021
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}
{
	"category": "C++ > String Manipulation",
	"name": "Pad Zeros Pre Constructed",
	"results": {
		"time": {
			"a": 125.1,
			"b": 124.7,
			"a_div_b": 1.0032076984763432
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	}
}
{
	"category": "C++ > String Manipulation",
	"name": "Rfind",
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		"time": {
			"a": 39.93,
			"b": 40.37,
			"a_div_b": 0.9891008174386922
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}
{
	"category": "C++ > String Manipulation",
	"name": "Rfindn",
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		"time": {
			"a": 151.5,
			"b": 151.1,
			"a_div_b": 1.0026472534745203
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	}
}
{
	"category": "C++ > String Manipulation",
	"name": "Rpad",
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		"time": {
			"a": 124.1,
			"b": 124.5,
			"a_div_b": 0.9967871485943774
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}
{
	"category": "C++ > String Manipulation",
	"name": "Rsplit",
	"results": {
		"time": {
			"a": 245.6,
			"b": 213.5,
			"a_div_b": 1.1503512880562061
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	}
}
{
	"category": "C++ > String Manipulation",
	"name": "Similarity",
	"results": {
		"time": {
			"a": 40.92,
			"b": 42.01,
			"a_div_b": 0.974053796715068
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	}
}
{
	"category": "C++ > String Manipulation",
	"name": "Simplify Path",
	"results": {
		"time": {
			"a": 690.8,
			"b": 691.3,
			"a_div_b": 0.9992767250108491
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	}
}
{
	"category": "C++ > String Manipulation",
	"name": "Split",
	"results": {
		"time": {
			"a": 239.5,
			"b": 206.6,
			"a_div_b": 1.159244917715392
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	}
}
{
	"category": "C++ > String Manipulation",
	"name": "Split Floats",
	"results": {
		"time": {
			"a": 155.6,
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			"a_div_b": 1.0045190445448675
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	}
}
{
	"category": "C++ > String Manipulation",
	"name": "Substr",
	"results": {
		"time": {
			"a": 47.83,
			"b": 48.01,
			"a_div_b": 0.9962507810872735
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}
{
	"category": "C++ > String Manipulation",
	"name": "To Camel Case",
	"results": {
		"time": {
			"a": 416.3,
			"b": 416.0,
			"a_div_b": 1.0007211538461538
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	}
}
{
	"category": "C++ > String Manipulation",
	"name": "To Lower",
	"results": {
		"time": {
			"a": 147.6,
			"b": 147.8,
			"a_div_b": 0.9986468200270635
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}
{
	"category": "C++ > String Manipulation",
	"name": "To Pascal Case",
	"results": {
		"time": {
			"a": 725.8,
			"b": 712.9,
			"a_div_b": 1.0180951045027353
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}
{
	"category": "C++ > String Manipulation",
	"name": "To Snake Case",
	"results": {
		"time": {
			"a": 597.7,
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}
{
	"category": "C++ > String Manipulation",
	"name": "To Utf 16 Buffer",
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		"time": {
			"a": 67.48,
			"b": 67.25,
			"a_div_b": 1.0034200743494424
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	}
}
{
	"category": "C++ > String Manipulation",
	"name": "To Utf 32 Buffer",
	"results": {
		"time": {
			"a": 46.86,
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			"a_div_b": 0.9671826625386997
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	}
}
{
	"category": "C++ > String Manipulation",
	"name": "To Utf 8 Buffer",
	"results": {
		"time": {
			"a": 66.14,
			"b": 66.21,
			"a_div_b": 0.9989427578915573
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	}
}
{
	"category": "C++ > String Manipulation",
	"name": "To Wchar Buffer",
	"results": {
		"time": {
			"a": 46.89,
			"b": 45.94,
			"a_div_b": 1.020679146713104
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	}
}
{
	"category": "C++ > String Manipulation",
	"name": "Uri Decode",
	"results": {
		"time": {
			"a": 220.1,
			"b": 222.9,
			"a_div_b": 0.9874383131449079
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	}
}
{
	"category": "C++ > String Manipulation",
	"name": "Uri Encode",
	"results": {
		"time": {
			"a": 231.7,
			"b": 231.1,
			"a_div_b": 1.0025962786672435
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	}
}
{
	"category": "C++ > String Manipulation",
	"name": "Validate Filename",
	"results": {
		"time": {
			"a": 212.1,
			"b": 211.5,
			"a_div_b": 1.0028368794326241
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	}
}
{
	"category": "C++ > String Manipulation",
	"name": "Validate Node Name",
	"results": {
		"time": {
			"a": 73.13,
			"b": 77.1,
			"a_div_b": 0.9485084306095979
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}
{
	"category": "C++ > String Manipulation",
	"name": "Xml Escape",
	"results": {
		"time": {
			"a": 197.7,
			"b": 202.0,
			"a_div_b": 0.9787128712871287
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}
{
	"category": "C++ > String Manipulation",
	"name": "Xml Unescape",
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		"time": {
			"a": 53.91,
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			"a_div_b": 0.9977790116601887
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}
{
	"category": "Animation > Animated Models",
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			"a": 0.8908,
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			"a": 0.014,
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}
{
	"category": "Animation > Animated Models",
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			"a": 4.349,
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			"a": 0.097,
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			"a_div_b": 5.105263157894737
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}
{
	"category": "Animation > Tween",
	"name": "Animate 1000 Tween Methods",
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			"a": 0.05282,
			"b": 0.0531,
			"a_div_b": 0.9947269303201506
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		"time": {
			"a": 2.171,
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			"a_div_b": 0.9704962002682163
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}
{
	"category": "Animation > Tween",
	"name": "Tween 100 Properties",
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		"render_cpu": {
			"a": 0.009509,
			"b": 0.009756,
			"a_div_b": 0.9746822468224682
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		"time": {
			"a": 0.339,
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			"a_div_b": 1.5270270270270272
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}
{
	"category": "Asset Import Export > Runtime",
	"name": "Export Gltf",
	"results": {
		"time": {
			"a": 53.59,
			"b": 39.03,
			"a_div_b": 1.3730463745836536
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}
{
	"category": "Asset Import Export > Runtime",
	"name": "Import Fbx",
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		"time": {
			"a": 368.6,
			"b": 362.1,
			"a_div_b": 1.0179508423087544
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}
{
	"category": "Asset Import Export > Runtime",
	"name": "Import Gltf",
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			"a": 17.18,
			"b": 21.4,
			"a_div_b": 0.8028037383177571
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}
{
	"category": "Asset Import Export > Runtime",
	"name": "Import Ogg Audio",
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			"a": 2.762,
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			"a_div_b": 1.0485952923310555
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}
{
	"category": "Asset Import Export > Runtime",
	"name": "Import Webp Images",
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			"a": 309.2,
			"b": 305.7,
			"a_div_b": 1.0114491331370625
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}
{
	"category": "Core > Array",
	"name": "Append Array",
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		"time": {
			"a": 152.3,
			"b": 146.2,
			"a_div_b": 1.0417236662106704
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}
{
	"category": "Core > Array",
	"name": "Bsearch",
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			"a": 54.24,
			"b": 54.78,
			"a_div_b": 0.9901423877327492
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}
{
	"category": "Core > Array",
	"name": "Fill",
	"results": {
		"time": {
			"a": 38.05,
			"b": 38.99,
			"a_div_b": 0.9758912541677351
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}
{
	"category": "Core > Array",
	"name": "Reverse",
	"results": {
		"time": {
			"a": 19.23,
			"b": 17.0,
			"a_div_b": 1.1311764705882352
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}
{
	"category": "Core > Callable",
	"name": "Function Callable",
	"results": {
		"time": {
			"a": 194.5,
			"b": 204.1,
			"a_div_b": 0.9529642332190104
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}
{
	"category": "Core > Callable",
	"name": "Lambda Inline Callable",
	"results": {
		"time": {
			"a": 213.9,
			"b": 218.8,
			"a_div_b": 0.9776051188299817
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}
{
	"category": "Core > Callable",
	"name": "Lambda Variable Callable",
	"results": {
		"time": {
			"a": 56.47,
			"b": 55.14,
			"a_div_b": 1.024120420747189
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}
{
	"category": "Core > Config File",
	"name": "Load",
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		"time": {
			"a": 19.23,
			"b": 19.19,
			"a_div_b": 1.0020844189682125
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}
{
	"category": "Core > Config File",
	"name": "Load With Password",
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			"a": 20.65,
			"b": 25.88,
			"a_div_b": 0.7979134466769706
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}
{
	"category": "Core > Config File",
	"name": "Save",
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		"time": {
			"a": 23.11,
			"b": 23.39,
			"a_div_b": 0.9880290722530995
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}
{
	"category": "Core > Config File",
	"name": "Save With Password",
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		"time": {
			"a": 23.04,
			"b": 23.25,
			"a_div_b": 0.9909677419354839
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}
{
	"category": "Core > Crypto",
	"name": "Generate 1g Random Bytes 1k At A Time",
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		"time": {
			"a": 1442.0,
			"b": 1450.0,
			"a_div_b": 0.9944827586206897
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}
{
	"category": "Core > Crypto",
	"name": "Generate 1m Random Bytes 10 At A Time",
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			"a": 6484.0,
			"b": 6499.0,
			"a_div_b": 0.9976919526080935
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}
{
	"category": "Core > Crypto",
	"name": "Generate Rsa 2048",
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			"a": 63.01,
			"b": 42.57,
			"a_div_b": 1.4801503406154568
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}
{
	"category": "Core > Crypto",
	"name": "Generate Rsa 4096",
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			"a": 1076.0,
			"b": 1230.0,
			"a_div_b": 0.8747967479674796
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}
{
	"category": "Core > Node Path",
	"name": "Create",
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			"a": 12.0,
			"b": 13.34,
			"a_div_b": 0.8995502248875562
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}
{
	"category": "Core > Object Db",
	"name": "12 Threads Full Contention",
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		"time": {
			"a": 699.0,
			"b": 886.0,
			"a_div_b": 0.7889390519187359
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}
{
	"category": "Core > Object Db",
	"name": "12 Threads Half Contention",
	"results": {
		"time": {
			"a": 360.1,
			"b": 453.6,
			"a_div_b": 0.7938712522045855
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}
{
	"category": "Core > Object Db",
	"name": "12 Threads Little Contention",
	"results": {
		"time": {
			"a": 77.72,
			"b": 99.05,
			"a_div_b": 0.7846542150429077
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}
{
	"category": "Core > Object Db",
	"name": "12 Threads Slope Contention",
	"results": {
		"time": {
			"a": 386.8,
			"b": 457.6,
			"a_div_b": 0.8452797202797203
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}
{
	"category": "Core > Object Db",
	"name": "2 Threads Full Contention",
	"results": {
		"time": {
			"a": 55.13,
			"b": 54.62,
			"a_div_b": 1.0093372391065545
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	}
}
{
	"category": "Core > Object Db",
	"name": "2 Threads Half Contention",
	"results": {
		"time": {
			"a": 35.21,
			"b": 34.54,
			"a_div_b": 1.0193977996525767
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}
{
	"category": "Core > Object Db",
	"name": "2 Threads Little Contention",
	"results": {
		"time": {
			"a": 17.54,
			"b": 16.91,
			"a_div_b": 1.0372560615020696
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}
{
	"category": "Core > Object Db",
	"name": "4 Threads Full Contention",
	"results": {
		"time": {
			"a": 148.7,
			"b": 170.6,
			"a_div_b": 0.8716295427901524
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}
{
	"category": "Core > Object Db",
	"name": "4 Threads Half Contention",
	"results": {
		"time": {
			"a": 81.81,
			"b": 91.88,
			"a_div_b": 0.8904005224205486
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}
{
	"category": "Core > Object Db",
	"name": "4 Threads Little Contention",
	"results": {
		"time": {
			"a": 28.06,
			"b": 30.14,
			"a_div_b": 0.9309887193098871
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}
{
	"category": "Core > Object Db",
	"name": "4 Threads Slope Contention",
	"results": {
		"time": {
			"a": 112.3,
			"b": 121.4,
			"a_div_b": 0.9250411861614497
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}
{
	"category": "Core > Object Db",
	"name": "8 Threads Full Contention",
	"results": {
		"time": {
			"a": 383.1,
			"b": 435.5,
			"a_div_b": 0.8796785304247992
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}
{
	"category": "Core > Object Db",
	"name": "8 Threads Half Contention",
	"results": {
		"time": {
			"a": 204.8,
			"b": 230.6,
			"a_div_b": 0.8881179531656549
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}
{
	"category": "Core > Object Db",
	"name": "8 Threads Little Contention",
	"results": {
		"time": {
			"a": 47.66,
			"b": 56.71,
			"a_div_b": 0.8404161523540821
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	}
}
{
	"category": "Core > Object Db",
	"name": "8 Threads Slope Contention",
	"results": {
		"time": {
			"a": 232.7,
			"b": 262.6,
			"a_div_b": 0.886138613861386
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}
{
	"category": "Core > Object Db",
	"name": "Single",
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			"a": 16.43,
			"b": 14.76,
			"a_div_b": 1.1131436314363143
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}
{
	"category": "Core > Random Number Generator",
	"name": "Global Scope Randf",
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		"time": {
			"a": 64.11,
			"b": 60.03,
			"a_div_b": 1.0679660169915042
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}
{
	"category": "Core > Random Number Generator",
	"name": "Global Scope Randf Range",
	"results": {
		"time": {
			"a": 96.48,
			"b": 106.6,
			"a_div_b": 0.9050656660412759
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}
{
	"category": "Core > Random Number Generator",
	"name": "Global Scope Randfn",
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		"time": {
			"a": 302.5,
			"b": 304.8,
			"a_div_b": 0.9924540682414698
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}
{
	"category": "Core > Random Number Generator",
	"name": "Global Scope Randi",
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			"a": 59.23,
			"b": 56.33,
			"a_div_b": 1.051482336232913
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}
{
	"category": "Core > Random Number Generator",
	"name": "Global Scope Randi Range",
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			"a": 81.96,
			"b": 82.31,
			"a_div_b": 0.9957477827724456
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}
{
	"category": "Core > Random Number Generator",
	"name": "Global Scope Randomize",
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			"a": 323.7,
			"b": 325.5,
			"a_div_b": 0.9944700460829493
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}
{
	"category": "Core > Random Number Generator",
	"name": "Randf",
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		"time": {
			"a": 71.75,
			"b": 72.56,
			"a_div_b": 0.9888368246968026
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}
{
	"category": "Core > Random Number Generator",
	"name": "Randf Range",
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			"a": 90.6,
			"b": 93.04,
			"a_div_b": 0.9737747205503008
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}
{
	"category": "Core > Random Number Generator",
	"name": "Randfn",
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		"time": {
			"a": 252.3,
			"b": 252.5,
			"a_div_b": 0.9992079207920792
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}
{
	"category": "Core > Random Number Generator",
	"name": "Randi",
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		"time": {
			"a": 47.37,
			"b": 48.09,
			"a_div_b": 0.9850280723643168
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}
{
	"category": "Core > Random Number Generator",
	"name": "Randi Range",
	"results": {
		"time": {
			"a": 67.98,
			"b": 68.21,
			"a_div_b": 0.9966280604017008
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}
{
	"category": "Core > Random Number Generator",
	"name": "Randomize",
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			"a": 67.12,
			"b": 52.8,
			"a_div_b": 1.2712121212121215
		},
		"physics": {
			"a": 37.21,
			"b": 37.62,
			"a_div_b": 0.9891015417331208
		},
		"time": {
			"a": 8.029,
			"b": 8.542,
			"a_div_b": 0.9399438070709436
		}
	}
}
{
	"category": "Physics > Rigid Body 2d",
	"name": "2000 Rigid Body 2d Squares",
	"results": {
		"idle": {
			"a": 55.34,
			"b": 55.89,
			"a_div_b": 0.9901592413669709
		},
		"physics": {
			"a": 38.42,
			"b": 43.84,
			"a_div_b": 0.8763686131386861
		},
		"time": {
			"a": 7.622,
			"b": 11.86,
			"a_div_b": 0.642664418212479
		}
	}
}
{
	"category": "Physics > Rigid Body 2d",
	"name": "2000 Rigid Body 2d Unbound",
	"results": {
		"idle": {
			"a": 67.6,
			"b": 65.72,
			"a_div_b": 1.0286062081558125
		},
		"physics": {
			"a": 39.31,
			"b": 36.81,
			"a_div_b": 1.0679163270850311
		},
		"time": {
			"a": 8.083,
			"b": 7.813,
			"a_div_b": 1.0345577883015487
		}
	}
}
{
	"category": "Physics > Rigid Body 2d",
	"name": "2000 Rigid Body 2d Unique",
	"results": {
		"idle": {
			"a": 14.7,
			"b": 14.68,
			"a_div_b": 1.0013623978201636
		},
		"physics": {
			"a": 35.82,
			"b": 35.05,
			"a_div_b": 1.0219686162624824
		},
		"time": {
			"a": 8.079,
			"b": 8.018,
			"a_div_b": 1.007607882264904
		}
	}
}
{
	"category": "Physics > Rigid Body 3d",
	"name": "2000 Rigid Body 3d Boxes",
	"results": {
		"idle": {
			"a": 54.61,
			"b": 56.24,
			"a_div_b": 0.9710170697012802
		},
		"physics": {
			"a": 46.97,
			"b": 49.9,
			"a_div_b": 0.9412825651302605
		},
		"time": {
			"a": 6.179,
			"b": 5.997,
			"a_div_b": 1.030348507587127
		}
	}
}
{
	"category": "Physics > Rigid Body 3d",
	"name": "2000 Rigid Body 3d Continuous",
	"results": {
		"idle": {
			"a": 27.86,
			"b": 28.1,
			"a_div_b": 0.991459074733096
		},
		"physics": {
			"a": 54.5,
			"b": 53.44,
			"a_div_b": 1.0198353293413174
		},
		"time": {
			"a": 6.553,
			"b": 9.753,
			"a_div_b": 0.6718958269250487
		}
	}
}
{
	"category": "Physics > Rigid Body 3d",
	"name": "2000 Rigid Body 3d Mixed",
	"results": {
		"idle": {
			"a": 22.8,
			"b": 27.16,
			"a_div_b": 0.8394698085419735
		},
		"physics": {
			"a": 50.46,
			"b": 49.76,
			"a_div_b": 1.0140675241157557
		},
		"time": {
			"a": 6.703,
			"b": 6.36,
			"a_div_b": 1.0539308176100628
		}
	}
}
{
	"category": "Physics > Rigid Body 3d",
	"name": "2000 Rigid Body 3d Spheres",
	"results": {
		"idle": {
			"a": 27.75,
			"b": 28.47,
			"a_div_b": 0.9747102212855638
		},
		"physics": {
			"a": 58.45,
			"b": 47.11,
			"a_div_b": 1.2407132243684993
		},
		"time": {
			"a": 6.142,
			"b": 6.284,
			"a_div_b": 0.9774029280712923
		}
	}
}
{
	"category": "Physics > Rigid Body 3d",
	"name": "2000 Rigid Body 3d Unbound",
	"results": {
		"idle": {
			"a": 29.01,
			"b": 24.36,
			"a_div_b": 1.1908866995073892
		},
		"physics": {
			"a": 37.44,
			"b": 35.46,
			"a_div_b": 1.0558375634517765
		},
		"time": {
			"a": 6.358,
			"b": 6.289,
			"a_div_b": 1.0109715376053428
		}
	}
}
{
	"category": "Physics > Rigid Body 3d",
	"name": "2000 Rigid Body 3d Unique",
	"results": {
		"idle": {
			"a": 16.73,
			"b": 26.39,
			"a_div_b": 0.6339522546419099
		},
		"physics": {
			"a": 48.05,
			"b": 51.86,
			"a_div_b": 0.9265329733898958
		},
		"time": {
			"a": 11.36,
			"b": 20.0,
			"a_div_b": 0.568
		}
	}
}
{
	"category": "Physics > Softbody 3d",
	"name": "Softbody 3d 500 Rigidbodies",
	"results": {
		"idle": {
			"a": 43.1,
			"b": 28.77,
			"a_div_b": 1.4980882864094545
		},
		"physics": {
			"a": 41.61,
			"b": 35.81,
			"a_div_b": 1.1619659313041049
		},
		"time": {
			"a": 0.045,
			"b": 0.05,
			"a_div_b": 0.8999999999999999
		}
	}
}
{
	"category": "Physics > Triangle Mesh",
	"name": "Triangle Mesh 3d 1000 Rigidbodies",
	"results": {
		"idle": {
			"a": 11.7,
			"b": 13.44,
			"a_div_b": 0.8705357142857143
		},
		"physics": {
			"a": 11.07,
			"b": 13.38,
			"a_div_b": 0.827354260089686
		},
		"time": {
			"a": 0.059,
			"b": 0.266,
			"a_div_b": 0.22180451127819548
		}
	}
}
{
	"category": "Scene Nodes > Add Children",
	"name": "Add Children With Same Name",
	"results": {
		"time": {
			"a": 43.68,
			"b": 52.04,
			"a_div_b": 0.8393543428132206
		}
	}
}
{
	"category": "Scene Nodes > Add Children",
	"name": "Add Children Without Name",
	"results": {
		"time": {
			"a": 41.57,
			"b": 42.98,
			"a_div_b": 0.967194043741275
		}
	}
}
{
	"category": "Scene Nodes > Delete Children",
	"name": "Delete Children In Order",
	"results": {
		"time": {
			"a": 3.502,
			"b": 3.581,
			"a_div_b": 0.977939123149958
		}
	}
}
{
	"category": "Scene Nodes > Delete Children",
	"name": "Delete Children Random Order",
	"results": {
		"time": {
			"a": 10.93,
			"b": 11.03,
			"a_div_b": 0.9909338168631007
		}
	}
}
{
	"category": "Scene Nodes > Delete Children",
	"name": "Delete Children Reverse Order",
	"results": {
		"time": {
			"a": 3.378,
			"b": 5.226,
			"a_div_b": 0.6463834672789897
		}
	}
}
{
	"category": "Scene Nodes > Get Node",
	"name": "Get Node",
	"results": {
		"time": {
			"a": 22.97,
			"b": 22.12,
			"a_div_b": 1.0384267631103072
		}
	}
}
{
	"category": "Scene Nodes > Move Children",
	"name": "Move Children",
	"results": {
		"time": {
			"a": 342.8,
			"b": 354.4,
			"a_div_b": 0.9672686230248307
		}
	}
}
{
	"category": "Viewport > Activation",
	"name": "Activate 1024 Viewports",
	"results": {
		"time": {
			"a": 70.37,
			"b": 70.0,
			"a_div_b": 1.0052857142857143
		}
	}
}
{
	"category": "Viewport > Activation",
	"name": "Activate 256 Viewports",
	"results": {
		"time": {
			"a": 24.73,
			"b": 25.11,
			"a_div_b": 0.9848665870171247
		}
	}
}
{
	"category": "Viewport > Activation",
	"name": "Activate 64 Viewports",
	"results": {
		"time": {
			"a": 5.934,
			"b": 5.982,
			"a_div_b": 0.9919759277833501
		}
	}
}
{
	"category": "Viewport > Allocation",
	"name": "Create 1024 Viewports",
	"results": {
		"time": {
			"a": 34.34,
			"b": 34.88,
			"a_div_b": 0.9845183486238532
		}
	}
}
{
	"category": "Viewport > Allocation",
	"name": "Create 256 Viewports",
	"results": {
		"time": {
			"a": 17.62,
			"b": 12.85,
			"a_div_b": 1.3712062256809339
		}
	}
}
{
	"category": "Viewport > Allocation",
	"name": "Create 64 Viewports",
	"results": {
		"time": {
			"a": 2.379,
			"b": 1.83,
			"a_div_b": 1.3
		}
	}
}

On all benchmarks, a_div_b averages 1.07, which means this PR is slightly faster overall.

clayjohn · 2025-01-09T00:15:13Z

The physics benchmarks are a little distressing "Physics > Triangle Mesh" has an "a_dev_b" of 0.22. Is that benchmark reproducible?

Calinou · 2025-01-09T01:11:19Z

The physics benchmarks are a little distressing "Physics > Triangle Mesh" has an "a_dev_b" of 0.22. Is that benchmark reproducible?

Unfortunately, no for two reasons:

There is no good way to get the time spent in the last frame processing physics, so I have to get the highest value coming from the Performance singleton (updated every second). This is the largest reason why physics benchmarks vary so much across runs - I only have a handful of values I can sample in the run which lasts 10 seconds for each physics test.
- I might look into tackling this soon.
The physics engine isn't deterministic, so there will always be a small variance as the total number of collisions that occur during the runtime will vary across runs. Doing multiple independent runs of each physics benchmark and averaging them can help against that, but it's not implemented in godot-benchmarks yet.
- We can improve the physics engine to be more deterministic than it is now (like this), even if it won't be 100% deterministic. Note that godot-benchmarks doesn't use Jolt yet, so I don't know if it's more or less deterministic than GodotPhysics. It should be possible to set up a test project to check this.

However, this large difference was seen on the time¹ part of that benchmark, which tracks scene loading time, not actual physics processing time. This part is actually decently reproducible, but scene loading on this test is rather quick so it's hard to tell whether it's a definitive regression or not.

This is displayed as Main Thread Time in the godot-benchmarks UI. ↩

core/templates/local_vector.h

Ivorforce

This is looking a lot better now! I'm lifting my NAK since the regression was fixed. I still have some style comments though.

Ivorforce · 2025-01-10T14:07:39Z

core/templates/local_vector.h

+				capacity = p_size;
+			} else {
+				capacity = MAX((U)2, capacity + ((1 + capacity) >> 1));
+				if (p_size > capacity) {


This is always true since you already tested this above. If it weren't true after the new capacity was found you'd get memory issues anyway, lol.

No. If we expand from 12 to 13 elements, then this condition will be false because 12 * 1.5 = 18; 12 > 18 == false.

Ah... I'm just now seeing that your implementation is different than I had thought it is.
The previous implementation used to grow to the nearest power of 2 of the requested size.
You implementation grows to 1.5x the previous capacity, and grows tightly to exactly the requested size if this exceeds the 1.5x growth.

I'm not sure which is better, tbh. I can imagine arguments for either implementation. But this is definitely a change of behavior that goes beyond the 1.5x growth factor instead of 2x.

@Nazarwadim This is the one I meant, it definitely warrants discussion before a merge.

So what's wrong with my implementation?

It's a change of behavior, as explained above.
Specifically, your implementation will be slower if reserve is called and more elements are added than reserved for, and slightly faster otherwise.
This may be a trade-off worth making, but I'm not sure it's appropriate for this PR. If it's possible with a 1.5x growth factor, I would keep it agnostic and use the same behavior as before.

Specifically, your implementation will be slower if reserve is called and more elements are added than reserved for.

In the engine this behavior is almost nowhere and this situation is unlikely.
Due to the fact that less memory is used, the memory is more compact next to each other, which increases the probability that during the reserve we will allocate cached memory, and not cold from the OS.

In the engine this behavior is almost nowhere

I assume you went through the codebase and checked all reserve calls to see if this claim is actually true? :)

Anyway, I have thought about the problem some more. Unfortunately, finding the 'next power of 1.5x' is a little bit harder than finding the next of 2x, so it would be a bit more complex to implement than what you have (probably with a little loop). The speed difference would likely be negligible for most scenarios.
I think it would be ok to just cap it at the requested size, as you have proposed. But I'd like to hear one or two more voices before I feel comfortable giving this PR a final thumbs up.

core/templates/local_vector.h

Ivorforce · 2025-01-10T15:36:30Z

@Nazarwadim Please stop resolving discussions without me actively acknowledging it's been resolved.
Specifically, I consider the one about tight growth to not be resolved yet.

Nazarwadim · 2025-01-10T16:33:16Z

Sorry, ok. I thought it was resolved.

Nazarwadim requested a review from a team as a code owner December 30, 2024 17:56

Chaosus added enhancement topic:core labels Dec 30, 2024

Chaosus added this to the 4.x milestone Dec 30, 2024

YYF233333 mentioned this pull request Dec 31, 2024

Reserve space for LocalVector if size is known #100269

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Use 1.5 growth factor for CowData #101138

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Add comments to protect against regressions of accidental memory overwrites. #101385

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Use 1.5x growth factor for LocalVector

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Use 1.5x growth factor for LocalVector #100944

Use 1.5x growth factor for LocalVector #100944

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Use 1.5x growth factor for LocalVector #100944

Are you sure you want to change the base?

Use 1.5x growth factor for LocalVector #100944

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