bump moc to 0.10.0

[chenyan-dfinity]

No description provided.

[github-actions]

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	138_275 ($\textcolor{red}{3.32\%}$)	6_974_058_129 ($\textcolor{red}{0.20\%}$)	61_987_732	288_202 ($\textcolor{red}{0.25\%}$)	5_527_868_856 ($\textcolor{red}{0.22\%}$)	309_728 ($\textcolor{red}{0.24\%}$)
triemap	139_765 ($\textcolor{red}{3.29\%}$)	11_432_083_637 ($\textcolor{red}{0.01\%}$)	74_216_052	222_825 ($\textcolor{red}{0.03\%}$)	547_701 ($\textcolor{red}{0.01\%}$)	539_052 ($\textcolor{red}{0.01\%}$)
rbtree	140_562 ($\textcolor{red}{3.27\%}$)	5_979_229_508 ($\textcolor{green}{-0.00\%}$)	57_995_940	88_905 ($\textcolor{red}{0.01\%}$)	268_573 ($\textcolor{red}{0.00\%}$)	278_352 ($\textcolor{red}{0.01\%}$)
splay	136_342 ($\textcolor{red}{3.39\%}$)	11_568_250_621 ($\textcolor{red}{0.00\%}$)	53_995_876	551_926 ($\textcolor{red}{0.00\%}$)	581_651 ($\textcolor{green}{-0.00\%}$)	810_220 ($\textcolor{red}{0.00\%}$)
btree	181_449 ($\textcolor{red}{2.83\%}$)	8_224_241_444 ($\textcolor{green}{-0.00\%}$)	31_103_892	277_542 ($\textcolor{red}{0.00\%}$)	384_171 ($\textcolor{red}{0.00\%}$)	429_041 ($\textcolor{red}{0.00\%}$)
zhenya_hashmap	146_485 ($\textcolor{red}{3.37\%}$)	2_634_116_314 ($\textcolor{red}{0.04\%}$)	65_987_480	65_396 ($\textcolor{red}{0.09\%}$)	80_204 ($\textcolor{red}{0.06\%}$)	94_825 ($\textcolor{red}{0.07\%}$)
btreemap_rs	420_066 ($\textcolor{red}{1.59\%}$)	1_654_114_188 ($\textcolor{red}{0.27\%}$)	13_762_560	66_890 ($\textcolor{red}{0.11\%}$)	112_562 ($\textcolor{red}{0.27\%}$)	81_308 ($\textcolor{red}{0.06\%}$)
imrc_hashmap_rs	419_753 ($\textcolor{red}{1.49\%}$)	2_386_381_104 ($\textcolor{red}{0.03\%}$)	122_454_016	32_903 ($\textcolor{red}{0.17\%}$)	162_822 ($\textcolor{red}{0.07\%}$)	98_526 ($\textcolor{red}{0.03\%}$)
hashmap_rs	413_537 ($\textcolor{red}{1.83\%}$)	402_296_850 ($\textcolor{red}{2.47\%}$)	36_536_320	16_697 ($\textcolor{red}{1.21\%}$)	21_601 ($\textcolor{red}{3.54\%}$)	20_052 ($\textcolor{red}{0.40\%}$)

Priority queue

	binary_size	heapify 1m	max mem	pop_min 50	put 50
heap	132_227 ($\textcolor{red}{3.51\%}$)	4_684_517_324 ($\textcolor{green}{-0.00\%}$)	29_995_836	511_499 ($\textcolor{red}{0.00\%}$)	186_465 ($\textcolor{red}{0.00\%}$)
heap_rs	411_165 ($\textcolor{red}{1.79\%}$)	123_102_416 ($\textcolor{green}{-0.00\%}$)	9_109_504	53_382 ($\textcolor{red}{0.12\%}$)	18_202 ($\textcolor{red}{0.35\%}$)

Growable array

	binary_size	generate 5k	max mem	batch_get 500	batch_put 500	batch_remove 500
buffer	139_908 ($\textcolor{red}{3.28\%}$)	2_082_623 ($\textcolor{red}{0.00\%}$)	65_508	73_092 ($\textcolor{red}{0.01\%}$)	671_517 ($\textcolor{red}{0.00\%}$)	127_592 ($\textcolor{red}{0.00\%}$)
vector	138_344 ($\textcolor{red}{3.32\%}$)	1_728_571 ($\textcolor{red}{0.00\%}$)	24_764	121_219 ($\textcolor{red}{0.00\%}$)	163_947 ($\textcolor{red}{0.00\%}$)	161_609 ($\textcolor{red}{0.00\%}$)
vec_rs	409_961 ($\textcolor{red}{1.81\%}$)	265_856 ($\textcolor{green}{-0.02\%}$)	655_360	12_902 ($\textcolor{red}{0.61\%}$)	25_331 ($\textcolor{red}{0.31\%}$)	21_215 ($\textcolor{red}{0.95\%}$)

Statistics

• binary_size: 2.72% [2.34%, 3.10%]
• max_mem: no change
• cycles: 0.23% [0.09%, 0.36%]

SHA-2

	binary_size	SHA-256	SHA-512	account_id	neuron_id
Motoko	174_597 ($\textcolor{red}{2.64\%}$)	264_156_360 ($\textcolor{red}{0.00\%}$)	235_099_580 ($\textcolor{red}{0.00\%}$)	35_153 ($\textcolor{red}{0.03\%}$)	23_262 ($\textcolor{red}{0.05\%}$)
Rust	498_272 ($\textcolor{red}{1.51\%}$)	82_512_024 ($\textcolor{green}{-0.00\%}$)	56_525_977 ($\textcolor{green}{-0.00\%}$)	42_551 ($\textcolor{red}{0.36\%}$)	44_574 ($\textcolor{red}{7.16\%}$)

Certified map

	binary_size	generate 10k	max mem	inc	witness
Motoko	166_880 ($\textcolor{red}{2.75\%}$)	18_581_618_617 ($\textcolor{red}{0.01\%}$)	3_429_924	2_209_513 ($\textcolor{red}{0.01\%}$)	327_767 ($\textcolor{red}{0.00\%}$)
Rust	441_794 ($\textcolor{red}{1.83\%}$)	6_202_163_062 ($\textcolor{green}{-0.07\%}$)	1_081_344	983_904 ($\textcolor{green}{-0.09\%}$)	288_469 ($\textcolor{green}{-0.13\%}$)

Statistics

• binary_size: 2.18% [1.47%, 2.90%]
• max_mem: no change
• cycles: 0.52% [-0.38%, 1.43%]

Basic DAO

	binary_size	init	transfer_token	submit_proposal	vote_proposal
Motoko	230_242 ($\textcolor{red}{1.96\%}$)	37_590 ($\textcolor{red}{0.26\%}$)	16_248 ($\textcolor{green}{-0.42\%}$)	12_674 ($\textcolor{red}{0.16\%}$)	14_137 ($\textcolor{red}{0.23\%}$)
Rust	718_379 ($\textcolor{red}{1.91\%}$)	472_438 ($\textcolor{red}{0.11\%}$)	86_786 ($\textcolor{red}{0.30\%}$)	105_263 ($\textcolor{red}{0.62\%}$)	116_229 ($\textcolor{red}{0.40\%}$)

DIP721 NFT

	binary_size	init	mint_token	transfer_token
Motoko	188_321 ($\textcolor{red}{2.41\%}$)	12_267 ($\textcolor{red}{0.71\%}$)	22_357 ($\textcolor{red}{0.17\%}$)	4_729 ($\textcolor{red}{0.40\%}$)
Rust	778_280 ($\textcolor{red}{1.51\%}$)	125_293 ($\textcolor{red}{0.21\%}$)	325_017 ($\textcolor{red}{0.16\%}$)	77_500 ($\textcolor{red}{0.50\%}$)

Statistics

• binary_size: 1.95% [1.51%, 2.39%]
• max_mem: no change
• cycles: 0.27% [0.15%, 0.40%]

Heartbeat

	binary_size	heartbeat
Motoko	123_357 ($\textcolor{red}{3.74\%}$)	3_758 ($\textcolor{green}{-49.16\%}$)
Rust	23_625 ($\textcolor{green}{-0.31\%}$)	469 ($\textcolor{green}{-40.71\%}$)

Timer

	binary_size	setTimer	cancelTimer
Motoko	129_636 ($\textcolor{red}{3.57\%}$)	15_227 ($\textcolor{red}{0.12\%}$)	1_684 ($\textcolor{red}{0.30\%}$)
Rust	443_367 ($\textcolor{red}{1.96\%}$)	43_417 ($\textcolor{green}{-0.28\%}$)	7_497 ($\textcolor{green}{-2.42\%}$)

Statistics

• binary_size: 2.76% [-2.32%, 7.85%]
• max_mem: no change
• cycles: -0.57% [-2.05%, 0.91%]

Garbage Collection

	generate 800k	max mem	batch_get 50	batch_put 50	batch_remove 50
default	1_012_258_537 ($\textcolor{red}{0.00\%}$)	59_396_776	50	50	50
copying	1_012_258_487 ($\textcolor{red}{0.00\%}$)	59_396_776	1_012_236_046 ($\textcolor{red}{0.00\%}$)	1_012_303_056 ($\textcolor{red}{0.00\%}$)	1_012_240_283 ($\textcolor{red}{0.00\%}$)
compacting	1_675_009_925 ($\textcolor{red}{0.00\%}$)	59_396_776	1_292_955_500 ($\textcolor{red}{0.00\%}$)	1_532_273_641 ($\textcolor{red}{0.00\%}$)	1_558_502_986 ($\textcolor{red}{0.00\%}$)
generational	2_498_146_508 ($\textcolor{green}{-0.75\%}$)	59_405_240	977_578_983 ($\textcolor{red}{0.00\%}$)	1_044_991 ($\textcolor{green}{-0.74\%}$)	960_405 ($\textcolor{green}{-0.72\%}$)
incremental	32_320_754 ($\textcolor{red}{0.00\%}$)	1_136_155_048 ($\textcolor{red}{0.00\%}$)	290_257_785	292_951_006	292_977_552

Actor class

	binary size	put new bucket	put existing bucket	get
Map	261_606 ($\textcolor{red}{2.96\%}$)	656_047 ($\textcolor{red}{2.73\%}$)	4_459 ($\textcolor{red}{0.22\%}$)	4_919 ($\textcolor{red}{0.20\%}$)

Statistics

• binary_size: no change
• max_mem: 0.00%
• cycles: 0.22% [-0.19%, 0.63%]

Publisher & Subscriber

	pub_binary_size	sub_binary_size	subscribe_caller	subscribe_callee	publish_caller	publish_callee
Motoko	144_439 ($\textcolor{red}{3.25\%}$)	131_299 ($\textcolor{red}{3.53\%}$)	14_651 ($\textcolor{red}{0.07\%}$)	8_456 ($\textcolor{red}{0.06\%}$)	10_539 ($\textcolor{red}{0.09\%}$)	3_669 ($\textcolor{red}{0.19\%}$)
Rust	479_599 ($\textcolor{red}{1.58\%}$)	529_662 ($\textcolor{red}{1.87\%}$)	51_729 ($\textcolor{red}{0.27\%}$)	34_594 ($\textcolor{green}{-0.19\%}$)	74_841 ($\textcolor{red}{0.91\%}$)	44_359 ($\textcolor{red}{6.59\%}$)

Statistics

• binary_size: 2.56% [1.41%, 3.70%]
• max_mem: no change
• cycles: 1.00% [-0.53%, 2.53%]

Overall Statistics

• binary_size: 2.51% [2.26%, 2.77%]
• max_mem: 0.00%
• cycles: 0.29% [0.12%, 0.47%]

[github-actions]

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	138_275	6_974_058_129	61_987_732	288_202	5_527_868_856	309_728
triemap	139_765	11_432_083_637	74_216_052	222_825	547_701	539_052
rbtree	140_562	5_979_229_508	57_995_940	88_905	268_573	278_352
splay	136_342	11_568_250_621	53_995_876	551_926	581_651	810_220
btree	181_449	8_224_241_444	31_103_892	277_542	384_171	429_041
zhenya_hashmap	146_485	2_634_116_314	65_987_480	65_396	80_204	94_825
btreemap_rs	420_066	1_654_114_188	13_762_560	66_890	112_562	81_308
imrc_hashmap_rs	419_753	2_386_381_104	122_454_016	32_903	162_822	98_526
hashmap_rs	413_537	402_296_850	36_536_320	16_697	21_601	20_052

Priority queue

	binary_size	heapify 1m	max mem	pop_min 50	put 50
heap	132_227	4_684_517_324	29_995_836	511_499	186_465
heap_rs	411_165	123_102_416	9_109_504	53_382	18_202

Growable array

	binary_size	generate 5k	max mem	batch_get 500	batch_put 500	batch_remove 500
buffer	139_908	2_082_623	65_508	73_092	671_517	127_592
vector	138_344	1_728_571	24_764	121_219	163_947	161_609
vec_rs	409_961	265_856	655_360	12_902	25_331	21_215

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	174_597	264_156_360	235_099_580	35_153	23_262
Rust	498_272	82_512_024	56_525_977	42_551	44_574

Certified map

	binary_size	generate 10k	max mem	inc	witness
Motoko	166_880	18_581_618_617	3_429_924	2_209_513	327_767
Rust	441_794	6_202_163_062	1_081_344	983_904	288_469

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	230_242	37_590	16_248	12_674	14_137
Rust	718_379	472_438	86_786	105_263	116_229

DIP721 NFT

	binary_size	init	mint_token	transfer_token
Motoko	188_321	12_267	22_357	4_729
Rust	778_280	125_293	325_017	77_500

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	123_357	3_758
Rust	23_625	469

Timer

	binary_size	setTimer	cancelTimer
Motoko	129_636	15_227	1_684
Rust	443_367	43_417	7_497

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_012_258_537	59_396_776	50	50	50
copying	1_012_258_487	59_396_776	1_012_236_046	1_012_303_056	1_012_240_283
compacting	1_675_009_925	59_396_776	1_292_955_500	1_532_273_641	1_558_502_986
generational	2_498_146_508	59_405_240	977_578_983	1_044_991	960_405
incremental	32_320_754	1_136_155_048	290_257_785	292_951_006	292_977_552

Actor class

	binary size	put new bucket	put existing bucket	get
Map	261_606	656_047	4_459	4_919

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	144_439	131_299	14_651	8_456	10_539	3_669
Rust	479_599	529_662	51_729	34_594	74_841	44_359