Experiment: concatSlices benchmark and alternatives

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✨ Documentation preview for 35f6501:

https://dfinity.github.io/new-motoko-base/pull/320 (source code)

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Benchmark Results

bench/ArrayBuilding.bench.mo $({\color{red}+0.02\%})$

Large known-size array building

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

Instructions: ${\color{red}+0.00\%}$
Heap: ${\color{gray}0\%}$
Stable Memory: ${\color{gray}0\%}$
Garbage Collection: ${\color{red}+0.02\%}$

Instructions

	1000	100000	1000000
List	612_133 $({\color{red}+0.02\%})$	53_731_091 $({\color{red}+0.00\%})$	531_478_746 $({\color{red}+0.00\%})$
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	10.09 KiB $({\color{red}+0.31\%})$	797.59 KiB $({\color{red}+0.00\%})$	7.67 MiB $({\color{red}+0.00\%})$
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 $({\color{gray}0\%})$

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

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

Create list from two slices

Create two lists of size given in the column, create a new list by concatenating middle halves of each list. Each row is a different concatenation method.

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Instructions

	1000	100000	1000000
List.concatSlices	509_826	43_595_666	430_519_514
List.fromIter . Iter.concat . List.range	824_088	71_354_642	705_147_137
List.addCount_ . List.values_from_	420_084	33_036_731	323_901_212
List.addCount . List.valuesFrom	436_475	34_636_730	339_900_539

Heap

	1000	100000	1000000
List.concatSlices	272 B	272 B	272 B
List.fromIter . Iter.concat . List.range	272 B	272 B	272 B
List.addCount_ . List.values_from_	272 B	272 B	272 B
List.addCount . List.valuesFrom	272 B	272 B	272 B

Garbage Collection

	1000	100000	1000000
List.concatSlices	15.45 KiB	1.17 MiB	11.52 MiB
List.fromIter . Iter.concat . List.range	16.11 KiB	1.18 MiB	11.54 MiB
List.addCount_ . List.values_from_	16.38 KiB	1.18 MiB	11.56 MiB
List.addCount . List.valuesFrom	16.38 KiB	1.18 MiB	11.56 MiB

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

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

List iteration

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Heap: ${\color{gray}0\%}$
Stable Memory: ${\color{gray}0\%}$
Garbage Collection: ${\color{gray}0\%}$

Instructions

	1000	100000	1000000
while get	270_722	25_107_346	249_653_754
List.values_ while unsafe_next	279_953	25_009_697	247_954_745
while put	364_930	34_420_986	342_689_330
List.values_ while next_set	299_643	26_910_387	266_955_435

Heap

	1000	100000	1000000
while get	272 B	272 B	272 B
List.values_ while unsafe_next	272 B	272 B	272 B
while put	272 B	272 B	272 B
List.values_ while next_set	272 B	272 B	272 B

Garbage Collection

	1000	100000	1000000
while get	5.19 KiB	399.5 KiB	3.84 MiB
List.values_ while unsafe_next	5.39 KiB	399.7 KiB	3.84 MiB
while put	5.19 KiB	399.5 KiB	3.84 MiB
List.values_ while next_set	5.39 KiB	399.7 KiB	3.84 MiB

bench/ListBufferNewArray.bench.mo $({\color{red}+2.19\%})$

List vs. Buffer for creating known-size arrays

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

Instructions: ${\color{red}+0.85\%}$
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Garbage Collection: ${\color{red}+1.34\%}$

Instructions

	0 (baseline)	1	5	10	100 (for loop)
List	1_875 $({\color{red}+6.96\%})$	3_441 $({\color{red}+3.68\%})$	10_436 $({\color{red}+1.18\%})$	15_967 $({\color{red}+0.77\%})$	82_981 $({\color{red}+0.15\%})$
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	608 B $({\color{red}+5.56\%})$	648 B $({\color{red}+5.19\%})$	808 B $({\color{red}+4.12\%})$	916 B $({\color{red}+3.62\%})$	1.96 KiB $({\color{red}+1.62\%})$
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 $({\color{gray}0\%})$

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 3x for pop and 8x 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 $({\color{gray}0\%})$	2_592 $({\color{gray}0\%})$	3_401 $({\color{gray}0\%})$
Push 500 elements	103_492 $({\color{gray}0\%})$	867_120 $({\color{gray}0\%})$	243_585 $({\color{gray}0\%})$
Pop front 2 elements	98_792 $({\color{gray}0\%})$	5_009 $({\color{gray}0\%})$	4_326 $({\color{gray}0\%})$
Pop 150 front&back	106_545 $({\color{gray}0\%})$	350_417 $({\color{gray}0\%})$	140_211 $({\color{gray}0\%})$

Heap

	pure/Queue	pure/RealTimeQueue	mutable Queue
Initialize with 2 elements	324 B $({\color{gray}0\%})$	300 B $({\color{gray}0\%})$	352 B $({\color{gray}0\%})$
Push 500 elements	8.08 KiB $({\color{gray}0\%})$	8.17 KiB $({\color{gray}0\%})$	19.8 KiB $({\color{gray}0\%})$
Pop front 2 elements	240 B $({\color{gray}0\%})$	240 B $({\color{gray}0\%})$	192 B $({\color{gray}0\%})$
Pop 150 front&back	-4.42 KiB $({\color{gray}0\%})$	-492 B $({\color{gray}0\%})$	-11.45 KiB $({\color{gray}0\%})$

Garbage Collection

	pure/Queue	pure/RealTimeQueue	mutable Queue
Initialize with 2 elements	508 B $({\color{gray}0\%})$	444 B $({\color{gray}0\%})$	456 B $({\color{gray}0\%})$
Push 500 elements	10.1 KiB $({\color{gray}0\%})$	137.84 KiB $({\color{gray}0\%})$	344 B $({\color{gray}0\%})$
Pop front 2 elements	12.19 KiB $({\color{gray}0\%})$	528 B $({\color{gray}0\%})$	424 B $({\color{gray}0\%})$
Pop 150 front&back	15.61 KiB $({\color{gray}0\%})$	49.66 KiB $({\color{gray}0\%})$	12.1 KiB $({\color{gray}0\%})$

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