experiment: try to optimize real time queue by re-ordering tests and avoiding redundant allocations

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No description provided.

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✨ Documentation preview for 898ef8b:

https://dfinity.github.io/new-motoko-base/pull/307 (source code)

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Benchmark Results

bench/ArrayBuilding.bench.mo $({\color{gray}0\%})$

Large known-size array building

Compares performance of different data structures for building arrays of known size.

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Instructions

	1000	100000	1000000
List	612_011 $({\color{gray}0\%})$	53_730_969 $({\color{gray}0\%})$	531_478_624 $({\color{gray}0\%})$
Buffer	367_424 $({\color{gray}0\%})$	36_403_920 $({\color{gray}0\%})$	364_004_168 $({\color{gray}0\%})$
pure/List	318_496 $({\color{gray}0\%})$	31_604_567 $({\color{gray}0\%})$	316_062_532 $({\color{gray}0\%})$
VarArray ?T	201_911 $({\color{gray}0\%})$	19_903_407 $({\color{gray}0\%})$	199_003_655 $({\color{gray}0\%})$
VarArray T	178_245 $({\color{gray}0\%})$	17_503_741 $({\color{gray}0\%})$	175_003_989 $({\color{gray}0\%})$
Array (baseline)	44_088 $({\color{gray}0\%})$	4_103_584 $({\color{gray}0\%})$	41_003_832 $({\color{gray}0\%})$

Heap

	1000	100000	1000000
List	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
Buffer	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
pure/List	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
VarArray ?T	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
VarArray T	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
Array (baseline)	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$

Garbage Collection

	1000	100000	1000000
List	10.05 KiB $({\color{gray}0\%})$	797.56 KiB $({\color{gray}0\%})$	7.67 MiB $({\color{gray}0\%})$
Buffer	8.71 KiB $({\color{gray}0\%})$	782.15 KiB $({\color{gray}0\%})$	7.63 MiB $({\color{gray}0\%})$
pure/List	19.95 KiB $({\color{gray}0\%})$	1.91 MiB $({\color{gray}0\%})$	19.07 MiB $({\color{gray}0\%})$
VarArray ?T	8.24 KiB $({\color{gray}0\%})$	781.68 KiB $({\color{gray}0\%})$	7.63 MiB $({\color{gray}0\%})$
VarArray T	8.23 KiB $({\color{gray}0\%})$	781.67 KiB $({\color{gray}0\%})$	7.63 MiB $({\color{gray}0\%})$
Array (baseline)	4.3 KiB $({\color{gray}0\%})$	391.02 KiB $({\color{gray}0\%})$	3.82 MiB $({\color{gray}0\%})$

bench/FromIters.bench.mo $({\color{gray}0\%})$

Benchmarking the fromIter functions

Columns describe the number of elements in the input iter.

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Instructions

	100	10_000	100_000
Array.fromIter	53_373 $({\color{gray}0\%})$	5_152_334 $({\color{gray}0\%})$	51_503_949 $({\color{gray}0\%})$
List.fromIter	35_436 $({\color{gray}0\%})$	3_421_823 $({\color{gray}0\%})$	34_204_824 $({\color{gray}0\%})$
List.fromIter . Iter.reverse	56_149 $({\color{gray}0\%})$	5_392_962 $({\color{gray}0\%})$	53_907_349 $({\color{gray}0\%})$

Heap

	100	10_000	100_000
Array.fromIter	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
List.fromIter	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
List.fromIter . Iter.reverse	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$

Garbage Collection

	100	10_000	100_000
Array.fromIter	2.76 KiB $({\color{gray}0\%})$	234.79 KiB $({\color{gray}0\%})$	2.29 MiB $({\color{gray}0\%})$
List.fromIter	3.51 KiB $({\color{gray}0\%})$	312.88 KiB $({\color{gray}0\%})$	3.05 MiB $({\color{gray}0\%})$
List.fromIter . Iter.reverse	5.11 KiB $({\color{gray}0\%})$	469.17 KiB $({\color{gray}0\%})$	4.58 MiB $({\color{gray}0\%})$

bench/ListBufferNewArray.bench.mo $({\color{gray}0\%})$

List vs. Buffer for creating known-size arrays

Performance comparison between List and Buffer for creating a new array.

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Instructions

	0 (baseline)	1	5	10	100 (for loop)
List	1_753 $({\color{gray}0\%})$	3_319 $({\color{gray}0\%})$	10_314 $({\color{gray}0\%})$	15_845 $({\color{gray}0\%})$	82_859 $({\color{gray}0\%})$
pure/List	1_450 $({\color{gray}0\%})$	1_564 $({\color{gray}0\%})$	2_738 $({\color{gray}0\%})$	4_205 $({\color{gray}0\%})$	33_746 $({\color{gray}0\%})$
Buffer	2_378 $({\color{gray}0\%})$	2_539 $({\color{gray}0\%})$	3_905 $({\color{gray}0\%})$	5_612 $({\color{gray}0\%})$	39_474 $({\color{gray}0\%})$

Heap

	0 (baseline)	1	5	10	100 (for loop)
List	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
pure/List	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$
Buffer	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$	272 B $({\color{gray}0\%})$

Garbage Collection

	0 (baseline)	1	5	10	100 (for loop)
List	576 B $({\color{gray}0\%})$	616 B $({\color{gray}0\%})$	776 B $({\color{gray}0\%})$	884 B $({\color{gray}0\%})$	1.93 KiB $({\color{gray}0\%})$
pure/List	360 B $({\color{gray}0\%})$	380 B $({\color{gray}0\%})$	460 B $({\color{gray}0\%})$	560 B $({\color{gray}0\%})$	2.3 KiB $({\color{gray}0\%})$
Buffer	856 B $({\color{gray}0\%})$	864 B $({\color{gray}0\%})$	896 B $({\color{gray}0\%})$	936 B $({\color{gray}0\%})$	1.62 KiB $({\color{gray}0\%})$

bench/PureListStackSafety.bench.mo $({\color{gray}0\%})$

List Stack safety

Check stack-safety of the following pure/List-related functions.

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Instructions

pure/List.split	27_403_700 $({\color{gray}0\%})$
pure/List.all	9_301_156 $({\color{gray}0\%})$
pure/List.any	9_401_585 $({\color{gray}0\%})$
pure/List.map	26_005_117 $({\color{gray}0\%})$
pure/List.filter	24_305_592 $({\color{gray}0\%})$
pure/List.filterMap	30_606_216 $({\color{gray}0\%})$
pure/List.partition	24_706_539 $({\color{gray}0\%})$
pure/List.join	38_606_854 $({\color{gray}0\%})$
pure/List.flatten	29_607_262 $({\color{gray}0\%})$
pure/List.take	27_407_282 $({\color{gray}0\%})$
pure/List.drop	11_004_661 $({\color{gray}0\%})$
pure/List.foldRight	21_806_962 $({\color{gray}0\%})$
pure/List.merge	36_411_001 $({\color{gray}0\%})$
pure/List.chunks	61_513_741 $({\color{gray}0\%})$
pure/Queue	161_571_999 $({\color{gray}0\%})$

Heap

pure/List.split	272 B $({\color{gray}0\%})$
pure/List.all	272 B $({\color{gray}0\%})$
pure/List.any	272 B $({\color{gray}0\%})$
pure/List.map	272 B $({\color{gray}0\%})$
pure/List.filter	272 B $({\color{gray}0\%})$
pure/List.filterMap	272 B $({\color{gray}0\%})$
pure/List.partition	272 B $({\color{gray}0\%})$
pure/List.join	272 B $({\color{gray}0\%})$
pure/List.flatten	272 B $({\color{gray}0\%})$
pure/List.take	272 B $({\color{gray}0\%})$
pure/List.drop	272 B $({\color{gray}0\%})$
pure/List.foldRight	272 B $({\color{gray}0\%})$
pure/List.merge	272 B $({\color{gray}0\%})$
pure/List.chunks	272 B $({\color{gray}0\%})$
pure/Queue	272 B $({\color{gray}0\%})$

Garbage Collection

pure/List.split	3.05 MiB $({\color{gray}0\%})$
pure/List.all	328 B $({\color{gray}0\%})$
pure/List.any	328 B $({\color{gray}0\%})$
pure/List.map	3.05 MiB $({\color{gray}0\%})$
pure/List.filter	3.05 MiB $({\color{gray}0\%})$
pure/List.filterMap	3.05 MiB $({\color{gray}0\%})$
pure/List.partition	3.05 MiB $({\color{gray}0\%})$
pure/List.join	3.05 MiB $({\color{gray}0\%})$
pure/List.flatten	3.05 MiB $({\color{gray}0\%})$
pure/List.take	3.05 MiB $({\color{gray}0\%})$
pure/List.drop	328 B $({\color{gray}0\%})$
pure/List.foldRight	1.53 MiB $({\color{gray}0\%})$
pure/List.merge	4.58 MiB $({\color{gray}0\%})$
pure/List.chunks	7.63 MiB $({\color{gray}0\%})$
pure/Queue	18.31 MiB $({\color{gray}0\%})$

No previous results found "/home/runner/work/new-motoko-base/new-motoko-base/.bench/Queues.bench.json"

bench/Queues.bench.mo $({\color{gray}0\%})$

Different queue implementations

Compare the performance of the following queue implementations:

• pure/Queue: The default immutable double-ended queue implementation.
  • Pros: Good amortized performance, meaning that the average cost of operations is low O(1).
  • Cons: In worst case, an operation can take O(size) time rebuilding the queue as demonstrated in the Pop front 2 elements scenario.
• pure/RealTimeQueue
  • Pros: Every operation is guaranteed to take at most O(1) time and space.
  • Cons: Poor amortized performance: Instruction cost is on average 4x for pop and 10x for push compared to pure/Queue.
• mutable Queue
  • Pros: Also O(1) guarantees with a lower constant factor than pure/RealTimeQueue. Amortized performance is comparable to pure/Queue.
  • Cons: It is mutable and cannot be used in shared types (not shareable).

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Instructions

	pure/Queue	pure/RealTimeQueue	mutable Queue
Initialize with 2 elements	3_571	2_934	3_401
Push 500 elements	103_492	966_398	243_585
Pop front 2 elements	98_792	5_009	4_326
Pop 150 front&back	106_545	394_667	140_211

Heap

	pure/Queue	pure/RealTimeQueue	mutable Queue
Initialize with 2 elements	324 B	300 B	352 B
Push 500 elements	8.08 KiB	8.17 KiB	19.8 KiB
Pop front 2 elements	240 B	240 B	192 B
Pop 150 front&back	-4.42 KiB	-492 B	-11.45 KiB

Garbage Collection

	pure/Queue	pure/RealTimeQueue	mutable Queue
Initialize with 2 elements	508 B	472 B	456 B
Push 500 elements	10.1 KiB	166.63 KiB	344 B
Pop front 2 elements	12.19 KiB	528 B	424 B
Pop 150 front&back	15.61 KiB	60.38 KiB	12.1 KiB

Note: Renamed benchmarks cannot be compared. Refer to the current baseline for manual comparison.