Moc inline with new meter alloc

[crusso]

base: new meter; no wasm-opt; selective inlining including allocation cf. #80

[github-actions[bot]]

Note Diffing the performance result against the published result from main branch. Unchanged benchmarks are omitted.

Map

	binary_size	generate 1m	max mem	batch_get 50	batch_put 50	batch_remove 50
hashmap	171_958 ($\textcolor{red}{8.14\%}$)	9_161_393_058 ($\textcolor{green}{-3.53\%}$)	61_987_732	381_086 ($\textcolor{green}{-3.00\%}$)	7_234_859_066 ($\textcolor{green}{-0.91\%}$)	408_538 ($\textcolor{green}{-3.48\%}$)
triemap	177_729 ($\textcolor{red}{9.92\%}$)	15_789_996_466 ($\textcolor{green}{-8.73\%}$)	74_216_052	307_168 ($\textcolor{green}{-11.23\%}$)	760_538 ($\textcolor{green}{-9.59\%}$)	746_976 ($\textcolor{green}{-9.46\%}$)
rbtree	180_851 ($\textcolor{red}{11.63\%}$)	7_554_129_903 ($\textcolor{green}{-10.74\%}$)	57_995_940	137_297 ($\textcolor{green}{-13.45\%}$)	337_114 ($\textcolor{green}{-12.45\%}$)	372_722 ($\textcolor{green}{-12.79\%}$)
splay	172_261 ($\textcolor{red}{9.45\%}$)	14_726_306_490 ($\textcolor{green}{-15.48\%}$)	53_995_876	702_455 ($\textcolor{green}{-16.49\%}$)	737_680 ($\textcolor{green}{-16.62\%}$)	1_034_297 ($\textcolor{green}{-16.24\%}$)
btree	254_044 ($\textcolor{red}{18.79\%}$)	11_474_733_247 ($\textcolor{green}{-13.53\%}$)	31_103_892	398_713 ($\textcolor{green}{-13.55\%}$)	540_188 ($\textcolor{green}{-14.16\%}$)	605_703 ($\textcolor{green}{-14.28\%}$)
zhenya_hashmap	189_062 ($\textcolor{red}{12.25\%}$)	3_502_145_539 ($\textcolor{green}{-9.65\%}$)	65_987_480	92_586 ($\textcolor{green}{-13.17\%}$)	110_925 ($\textcolor{green}{-15.51\%}$)	129_031 ($\textcolor{green}{-17.33\%}$)
btreemap_rs	446_267	1_797_752_179	13_762_560	74_544	126_136	92_839
imrc_hashmap_rs	446_166	2_571_892_333	122_454_016	38_956	179_095	115_561
hashmap_rs	439_346	447_664_894	36_536_320	22_228	27_664	25_290

Priority queue

	binary_size	heapify 1m	max mem	pop_min 50	put 50
heap	161_848 ($\textcolor{red}{6.25\%}$)	6_404_054_262 ($\textcolor{green}{-12.23\%}$)	29_995_836	703_629 ($\textcolor{green}{-13.47\%}$)	258_830 ($\textcolor{green}{-13.02\%}$)
heap_rs	437_278	142_914_793	9_109_504	59_850	23_726

Growable array

	binary_size	generate 5k	max mem	batch_get 500	batch_put 500	batch_remove 500
buffer	175_392 ($\textcolor{red}{8.65\%}$)	2_839_042 ($\textcolor{green}{-12.99\%}$)	65_508	108_785 ($\textcolor{green}{-13.20\%}$)	896_573 ($\textcolor{green}{-14.01\%}$)	186_785 ($\textcolor{green}{-11.19\%}$)
vector	177_651 ($\textcolor{red}{10.50\%}$)	2_430_418 ($\textcolor{green}{-12.12\%}$)	24_764	170_967 ($\textcolor{green}{-13.45\%}$)	232_802 ($\textcolor{green}{-11.92\%}$)	231_608 ($\textcolor{green}{-13.33\%}$)
vec_rs	435_834	290_143	655_360	17_605	31_014	25_400

Statistics

• binary_size: 10.62% [8.41%, 12.83%]
• max_mem: no change
• cycles: -11.89% [-13.02%, -10.77%]

SHA-2

	binary_size	SHA-256	SHA-512	account_id	neuron_id
Motoko	235_173 ($\textcolor{red}{19.88\%}$)	326_935_296 ($\textcolor{green}{-7.34\%}$)	294_476_504 ($\textcolor{green}{-13.15\%}$)	41_239 ($\textcolor{green}{-7.98\%}$)	29_492 ($\textcolor{green}{-7.60\%}$)
Rust	528_234	82_789_387	56_794_263	50_651	53_532

Certified map

	binary_size	generate 10k	max mem	inc	witness
Motoko	243_154 ($\textcolor{red}{18.57\%}$)	5_753_778_755 ($\textcolor{green}{-7.98\%}$)	3_429_924	683_053 ($\textcolor{green}{-8.00\%}$)	445_158 ($\textcolor{green}{-12.10\%}$)
Rust	469_955	6_359_442_714	1_081_344	1_012_174	305_119

Statistics

• binary_size: 19.22% [15.09%, 23.36%]
• max_mem: no change
• cycles: -9.17% [-10.93%, -7.41%]

Basic DAO

	binary_size	init	transfer_token	submit_proposal	vote_proposal
Motoko	311_829 ($\textcolor{red}{12.42\%}$)	49_955 ($\textcolor{green}{-2.69\%}$)	24_257 ($\textcolor{green}{-4.13\%}$)	20_114 ($\textcolor{green}{-3.62\%}$)	21_530 ($\textcolor{green}{-4.08\%}$)
Rust	763_017	552_075	105_203	128_753	139_539

DIP721 NFT

	binary_size	init	mint_token	transfer_token
Motoko	250_746 ($\textcolor{red}{9.01\%}$)	19_068 ($\textcolor{green}{-1.03\%}$)	31_752 ($\textcolor{green}{-1.69\%}$)	9_523 ($\textcolor{green}{-2.10\%}$)
Rust	828_238	146_257	380_260	93_763

Statistics

• binary_size: 10.71% [-0.04%, 21.47%]
• max_mem: no change
• cycles: -2.76% [-3.66%, -1.87%]

Heartbeat

	binary_size	heartbeat
Motoko	147_880 ($\textcolor{red}{4.06\%}$)	23_906 ($\textcolor{green}{-0.68\%}$)
Rust	25_650	1_179 ($\textcolor{red}{114.75\%}$)

Timer

	binary_size	setTimer	cancelTimer
Motoko	157_829 ($\textcolor{red}{5.75\%}$)	53_655 ($\textcolor{green}{-1.56\%}$)	4_918 ($\textcolor{green}{-1.21\%}$)
Rust	470_693	69_727	11_405

Statistics

• binary_size: 5.75%
• max_mem: no change
• cycles: -1.38% [-2.51%, -0.25%]

Garbage Collection

Note Same as main branch, skipping.

Actor class

	binary size	put new bucket	put existing bucket	get
Map	323_539 ($\textcolor{red}{8.67\%}$)	853_878 ($\textcolor{red}{8.97\%}$)	16_824 ($\textcolor{green}{-1.30\%}$)	17_316 ($\textcolor{green}{-1.23\%}$)

Statistics

• binary_size: no change
• max_mem: no change
• cycles: 3.78% [-3.07%, 10.63%]

Publisher & Subscriber

	pub_binary_size	sub_binary_size	subscribe_caller	subscribe_callee	publish_caller	publish_callee
Motoko	175_981 ($\textcolor{red}{5.52\%}$)	158_295 ($\textcolor{red}{4.22\%}$)	29_754 ($\textcolor{green}{-0.67\%}$)	12_468 ($\textcolor{green}{-0.70\%}$)	23_775 ($\textcolor{green}{-1.18\%}$)	6_804 ($\textcolor{green}{-0.99\%}$)
Rust	511_870	565_407	71_728	44_318	95_767	53_941

Statistics

• binary_size: 4.87% [0.76%, 8.97%]

• max_mem: no change

• cycles: -0.89% [-1.17%, -0.60%]

  Overall Statistics

• binary_size: 10.68% [8.58%, 12.79%]

• max_mem: no change

• cycles: -8.32% [-9.67%, -6.97%]

[github-actions[bot]]

Note The flamegraph link only works after you merge. Unchanged benchmarks are omitted.

Collection libraries

Measure different collection libraries written in both Motoko and Rust. The library names with _rs suffix are written in Rust; the rest are written in Motoko.

We use the same random number generator with fixed seed to ensure that all collections contain the same elements, and the queries are exactly the same. Below we explain the measurements of each column in the table:

• generate 1m. Insert 1m Nat64 integers into the collection. For Motoko collections, it usually triggers the GC; the rest of the column are not likely to trigger GC.
• max mem. For Motoko, it reports rts_max_heap_size after generate call; For Rust, it reports the Wasm's memory page * 32Kb.
• batch_get 50. Find 50 elements from the collection.
• batch_put 50. Insert 50 elements to the collection.
• batch_remove 50. Remove 50 elements from the collection.

💎 Takeaways

• The platform only charges for instruction count. Data structures which make use of caching and locality have no impact on the cost.
• We have a limit on the maximal cycles per round. This means asymptotic behavior doesn't matter much. We care more about the performance up to a fixed N. In the extreme cases, you may see an $O(10000 n\log n)$ algorithm hitting the limit, while an $O(n^2)$ algorithm runs just fine.
• Amortized algorithms/GC may need to be more eager to avoid hitting the cycle limit on a particular round.
• Rust costs more cycles to process complicated Candid data, but it is more efficient in performing core computations.

Note

• The Candid interface of the benchmark is minimal, therefore the serialization cost is negligible in this measurement.
• Due to the instrumentation overhead and cycle limit, we cannot profile computations with large collections. Hopefully, when deterministic time slicing is ready, we can measure the performance on larger memory footprint.
• hashmap uses amortized data structure. When the initial capacity is reached, it has to copy the whole array, thus the cost of batch_put 50 is much higher than other data structures.
• btree comes from mops.one/stableheapbtreemap.
• zhenya_hashmap comes from mops.one/map.
• vector comes from mops.one/vector. Compare with buffer, put has better worst case time and space complexity ($O(\sqrt{n})$ vs $O(n)$); get has a slightly larger constant overhead.
• hashmap_rs uses the fxhash crate, which is the same as std::collections::HashMap, but with a deterministic hasher. This ensures reproducible result.
• imrc_hashmap_rs uses the im-rc crate, which is the immutable version hashmap in Rust.

Map

	binary_size	generate 1m	max mem	batch_get 50	batch_put 50	batch_remove 50
hashmap	171_958	9_161_393_058	61_987_732	381_086	7_234_859_066	408_538
triemap	177_729	15_789_996_466	74_216_052	307_168	760_538	746_976
rbtree	180_851	7_554_129_903	57_995_940	137_297	337_114	372_722
splay	172_261	14_726_306_490	53_995_876	702_455	737_680	1_034_297
btree	254_044	11_474_733_247	31_103_892	398_713	540_188	605_703
zhenya_hashmap	189_062	3_502_145_539	65_987_480	92_586	110_925	129_031
btreemap_rs	446_267	1_797_752_179	13_762_560	74_544	126_136	92_839
imrc_hashmap_rs	446_166	2_571_892_333	122_454_016	38_956	179_095	115_561
hashmap_rs	439_346	447_664_894	36_536_320	22_228	27_664	25_290

Priority queue

	binary_size	heapify 1m	max mem	pop_min 50	put 50
heap	161_848	6_404_054_262	29_995_836	703_629	258_830	670_526
heap_rs	437_278	142_914_793	9_109_504	59_850	23_726	60_072

Growable array

	binary_size	generate 5k	max mem	batch_get 500	batch_put 500	batch_remove 500
buffer	175_392	2_839_042	65_508	108_785	896_573	186_785
vector	177_651	2_430_418	24_764	170_967	232_802	231_608
vec_rs	435_834	290_143	655_360	17_605	31_014	25_400

Cryptographic libraries

Measure different cryptographic libraries written in both Motoko and Rust.

• SHA-2 benchmarks
  • SHA-256/SHA-512. Compute the hash of a 1M Wasm binary.
  • account_id. Compute the ledger account id from principal, based on SHA-224.
  • neuron_id. Compute the NNS neuron id from principal, based on SHA-256.
• Certified map. Merkle Tree for storing key-value pairs and generate witness according to the IC Interface Specification.
  • generate 10k. Insert 10k 7-character word as both key and value into the certified map.
  • max mem. For Motoko, it reports rts_max_heap_size after generate call; For Rust, it reports the Wasm's memory page * 32Kb.
  • inc. Increment a counter and insert the counter value into the map.
  • witness. Generate the root hash and a witness for the counter.

SHA-2

	binary_size	SHA-256	SHA-512	account_id	neuron_id
Motoko	235_173	326_935_296	294_476_504	41_239	29_492
Rust	528_234	82_789_387	56_794_263	50_651	53_532

Certified map

	binary_size	generate 10k	max mem	inc	witness
Motoko	243_154	5_753_778_755	3_429_924	683_053	445_158
Rust	469_955	6_359_442_714	1_081_344	1_012_174	305_119

Sample Dapps

Measure the performance of some typical dapps:

• Basic DAO, with heartbeat disabled to make profiling easier. We have a separate benchmark to measure heartbeat performance.
• DIP721 NFT

Note

• The cost difference is mainly due to the Candid serialization cost.
• Motoko statically compiles/specializes the serialization code for each method, whereas in Rust, we use serde to dynamically deserialize data based on data on the wire.
• We could improve the performance on the Rust side by using parser combinators. But it is a challenge to maintain the ergonomics provided by serde.
• For real-world applications, we tend to send small data for each endpoint, which makes the Candid overhead in Rust tolerable.

Basic DAO

	binary_size	init	transfer_token	submit_proposal	vote_proposal
Motoko	311_829	49_955	24_257	20_114	21_530
Rust	763_017	552_075	105_203	128_753	139_539

DIP721 NFT

	binary_size	init	mint_token	transfer_token
Motoko	250_746	19_068	31_752	9_523
Rust	828_238	146_257	380_260	93_763

Heartbeat / Timer

Measure the cost of empty heartbeat and timer job.

• setTimer measures both the setTimer(0) method and the execution of empty job.
• It is not easy to reliably capture the above events in one flamegraph, as the implementation detail of the replica can affect how we measure this. Typically, a correct flamegraph contains both setTimer and canister_global_timer function. If it's not there, we may need to adjust the script.

Heartbeat

	binary_size	heartbeat
Motoko	147_880	23_906
Rust	25_650	1_179

Timer

	binary_size	setTimer	cancelTimer
Motoko	157_829	53_655	4_918
Rust	470_693	69_727	11_405

Motoko Specific Benchmarks

Measure various features only available in Motoko.

• Garbage Collection. Measure Motoko garbage collection cost using the Triemap benchmark. The max mem column reports rts_max_heap_size after generate call. The cycle cost numbers reported here are garbage collection cost only. Some flamegraphs are truncated due to the 2M log size limit. The dfx/ic-wasm optimizer is disabled for the garbage collection test cases due to how the optimizer affects function names, making profiling trickier.

  • default. Compile with the default GC option. With the current GC scheduler, generate will trigger the copying GC. The rest of the methods will not trigger GC.
  • copying. Compile with --force-gc --copying-gc.
  • compacting. Compile with --force-gc --compacting-gc.
  • generational. Compile with --force-gc --generational-gc.
  • incremental. Compile with --force-gc --incremental-gc.

• Actor class. Measure the cost of spawning actor class, using the Actor classes example.

Garbage Collection

	generate 800k	max mem	batch_get 50	batch_put 50	batch_remove 50
default	1_338_231_405	59_396_776	118	118	118
copying	1_338_231_287	59_396_776	1_337_913_569	1_338_002_371	1_337_919_144
compacting	1_911_420_608	59_396_776	1_473_824_186	1_756_485_066	1_787_369_954
generational	2_891_818_643	59_405_240	1_141_865_993	1_217_376	1_117_840
incremental	33_436_719	1_136_155_048	333_734_166	336_829_512	336_860_690

Actor class

	binary size	put new bucket	put existing bucket	get
Map	323_539	853_878	16_824	17_316

Publisher & Subscriber

Measure the cost of inter-canister calls from the Publisher & Subscriber example.

	pub_binary_size	sub_binary_size	subscribe_caller	subscribe_callee	publish_caller	publish_callee
Motoko	175_981	158_295	29_754	12_468	23_775	6_804
Rust	511_870	565_407	71_728	44_318	95_767	53_941