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	133_756 ($\textcolor{green}{-0.05\%}$)	8_456_787_008 ($\textcolor{red}{21.50\%}$)	61_987_732	346_249 ($\textcolor{red}{20.45\%}$)	6_615_757_444 ($\textcolor{red}{19.94\%}$)	373_657 ($\textcolor{red}{20.94\%}$)
triemap	135_161 ($\textcolor{green}{-0.11\%}$)	14_978_845_324 ($\textcolor{red}{31.04\%}$)	74_216_052	297_735 ($\textcolor{red}{33.65\%}$)	722_224 ($\textcolor{red}{31.88\%}$)	709_041 ($\textcolor{red}{31.55\%}$)
rbtree	135_844 ($\textcolor{green}{-0.20\%}$)	7_404_914_600 ($\textcolor{red}{23.84\%}$)	57_995_940	108_390 ($\textcolor{red}{21.92\%}$)	332_553 ($\textcolor{red}{23.82\%}$)	352_845 ($\textcolor{red}{26.77\%}$)
splay	131_680 ($\textcolor{green}{-0.14\%}$)	15_209_722_290 ($\textcolor{red}{31.48\%}$)	53_995_876	729_085 ($\textcolor{red}{32.10\%}$)	768_032 ($\textcolor{red}{32.04\%}$)	1_063_884 ($\textcolor{red}{31.31\%}$)
btree	176_210 ($\textcolor{green}{-0.14\%}$)	10_885_917_477 ($\textcolor{red}{32.36\%}$)	31_103_892	369_256 ($\textcolor{red}{33.05\%}$)	512_113 ($\textcolor{red}{33.31\%}$)	570_228 ($\textcolor{red}{32.91\%}$)
zhenya_hashmap	141_692 ($\textcolor{green}{-0.01\%}$)	3_304_768_283 ($\textcolor{red}{25.51\%}$)	65_987_480	83_813 ($\textcolor{red}{28.27\%}$)	103_431 ($\textcolor{red}{29.04\%}$)	122_290 ($\textcolor{red}{29.06\%}$)
btreemap_rs	419_571 ($\textcolor{red}{1.47\%}$)	1_800_692_107 ($\textcolor{red}{9.15\%}$)	13_762_560	75_532 ($\textcolor{red}{13.05\%}$)	125_504 ($\textcolor{red}{11.79\%}$)	91_329 ($\textcolor{red}{12.39\%}$)
imrc_hashmap_rs	419_256 ($\textcolor{red}{1.37\%}$)	2_566_397_171 ($\textcolor{red}{7.57\%}$)	122_454_016	38_485 ($\textcolor{red}{17.17\%}$)	178_181 ($\textcolor{red}{9.50\%}$)	115_627 ($\textcolor{red}{17.39\%}$)
hashmap_rs	413_039 ($\textcolor{red}{1.71\%}$)	432_766_256 ($\textcolor{red}{10.23\%}$)	36_536_320	21_628 ($\textcolor{red}{31.09\%}$)	26_813 ($\textcolor{red}{28.52\%}$)	24_896 ($\textcolor{red}{24.65\%}$)

Priority queue

	binary_size	heapify 1m	max mem	pop_min 50	put 50
heap	127_692 ($\textcolor{green}{-0.04\%}$)	6_314_372_552 ($\textcolor{red}{34.79\%}$)	29_995_836	692_956 ($\textcolor{red}{35.48\%}$)	255_044 ($\textcolor{red}{36.78\%}$)
heap_rs	410_675 ($\textcolor{red}{1.67\%}$)	140_170_301 ($\textcolor{red}{13.86\%}$)	9_109_504	59_205 ($\textcolor{red}{11.04\%}$)	23_229 ($\textcolor{red}{28.07\%}$)

Growable array

	binary_size	generate 5k	max mem	batch_get 500	batch_put 500	batch_remove 500
buffer	135_403 ($\textcolor{green}{-0.04\%}$)	2_751_546 ($\textcolor{red}{32.12\%}$)	65_508	103_498 ($\textcolor{red}{41.61\%}$)	878_959 ($\textcolor{red}{30.89\%}$)	171_998 ($\textcolor{red}{34.81\%}$)
vector	133_879 ($\textcolor{green}{-0.02\%}$)	2_384_703 ($\textcolor{red}{37.96\%}$)	24_764	165_680 ($\textcolor{red}{36.68\%}$)	227_918 ($\textcolor{red}{39.02\%}$)	218_436 ($\textcolor{red}{35.17\%}$)
vec_rs	409_470 ($\textcolor{red}{1.69\%}$)	289_504 ($\textcolor{red}{8.88\%}$)	655_360	17_114 ($\textcolor{red}{33.45\%}$)	30_524 ($\textcolor{red}{20.87\%}$)	25_660 ($\textcolor{red}{22.10\%}$)

Statistics

binary_size: 0.51% [0.12%, 0.91%]
max_mem: no change
cycles: 26.00% [23.91%, 28.09%]

SHA-2

	binary_size	SHA-256	SHA-512	account_id	neuron_id
Motoko	170_111 ($\textcolor{green}{-0.00\%}$)	307_625_902 ($\textcolor{red}{16.46\%}$)	277_653_343 ($\textcolor{red}{18.10\%}$)	43_219 ($\textcolor{red}{22.98\%}$)	29_620 ($\textcolor{red}{27.40\%}$)
Rust	497_732 ($\textcolor{red}{1.40\%}$)	82_788_057 ($\textcolor{red}{0.33\%}$)	56_793_048 ($\textcolor{red}{0.47\%}$)	49_501 ($\textcolor{red}{16.76\%}$)	52_193 ($\textcolor{red}{25.47\%}$)

Certified map

	binary_size	generate 10k	max mem	inc	witness
Motoko	162_022 ($\textcolor{green}{-0.24\%}$)	23_978_863_464 ($\textcolor{red}{29.06\%}$)	3_429_924	2_853_149 ($\textcolor{red}{29.14\%}$)	425_671 ($\textcolor{red}{29.87\%}$)
Rust	441_297 ($\textcolor{red}{1.72\%}$)	6_312_527_623 ($\textcolor{red}{1.70\%}$)	1_081_344	1_004_499 ($\textcolor{red}{2.00\%}$)	301_497 ($\textcolor{red}{4.38\%}$)

Statistics

binary_size: 0.72% [-0.44%, 1.87%]
max_mem: no change
cycles: 16.01% [10.39%, 21.63%]

Basic DAO

	binary_size	init	transfer_token	submit_proposal	vote_proposal
Motoko	225_863 ($\textcolor{red}{0.03\%}$)	47_763 ($\textcolor{red}{27.39\%}$)	22_599 ($\textcolor{red}{38.51\%}$)	18_949 ($\textcolor{red}{49.75\%}$)	20_115 ($\textcolor{red}{42.62\%}$)
Rust	717_659 ($\textcolor{red}{1.81\%}$)	532_119 ($\textcolor{red}{12.76\%}$)	101_647 ($\textcolor{red}{17.48\%}$)	124_976 ($\textcolor{red}{19.46\%}$)	135_521 ($\textcolor{red}{17.07\%}$)

DIP721 NFT

	binary_size	init	mint_token	transfer_token
Motoko	183_977 ($\textcolor{red}{0.05\%}$)	17_658 ($\textcolor{red}{44.96\%}$)	29_720 ($\textcolor{red}{33.16\%}$)	8_835 ($\textcolor{red}{87.58\%}$)
Rust	777_603 ($\textcolor{red}{1.42\%}$)	141_523 ($\textcolor{red}{13.19\%}$)	368_093 ($\textcolor{red}{13.44\%}$)	91_142 ($\textcolor{red}{18.19\%}$)

Statistics

binary_size: 0.83% [-0.26%, 1.92%]
max_mem: no change
cycles: 31.11% [21.30%, 40.92%]

Heartbeat

	binary_size	heartbeat
Motoko	118_901 ($\textcolor{green}{-0.01\%}$)	23_265 ($\textcolor{red}{214.73\%}$)
Rust	23_573 ($\textcolor{green}{-0.53\%}$)	1_165 ($\textcolor{red}{47.28\%}$)

Timer

	binary_size	setTimer	cancelTimer
Motoko	125_145 ($\textcolor{green}{-0.02\%}$)	51_903 ($\textcolor{red}{241.29\%}$)	4_624 ($\textcolor{red}{175.40\%}$)
Rust	442_822 ($\textcolor{red}{1.83\%}$)	67_944 ($\textcolor{red}{56.05\%}$)	11_048 ($\textcolor{red}{43.80\%}$)

Statistics

binary_size: 0.91% [-4.94%, 6.75%]
max_mem: no change
cycles: 129.13% [16.80%, 241.47%]

Garbage Collection

	generate 800k	max mem	batch_get 50	batch_put 50	batch_remove 50
default	1_338_231_390 ($\textcolor{red}{32.20\%}$)	59_396_776	118 ($\textcolor{red}{136.00\%}$)	118 ($\textcolor{red}{136.00\%}$)	118 ($\textcolor{red}{136.00\%}$)
copying	1_338_231_272 ($\textcolor{red}{32.20\%}$)	59_396_776	1_337_913_554 ($\textcolor{red}{32.17\%}$)	1_338_002_356 ($\textcolor{red}{32.17\%}$)	1_337_919_129 ($\textcolor{red}{32.17\%}$)
compacting	1_911_420_593 ($\textcolor{red}{14.11\%}$)	59_396_776	1_473_824_171 ($\textcolor{red}{13.99\%}$)	1_756_485_051 ($\textcolor{red}{14.63\%}$)	1_787_369_939 ($\textcolor{red}{14.69\%}$)
generational	2_918_248_625 ($\textcolor{red}{15.94\%}$)	59_405_240	1_141_865_950 ($\textcolor{red}{16.81\%}$)	1_228_537 ($\textcolor{red}{16.69\%}$)	1_127_895 ($\textcolor{red}{16.59\%}$)
incremental	33_436_705 ($\textcolor{red}{3.45\%}$)	1_136_153_832	333_734_166 ($\textcolor{red}{14.98\%}$)	336_829_512 ($\textcolor{red}{14.98\%}$)	336_860_690 ($\textcolor{red}{14.98\%}$)

Actor class

	binary size	put new bucket	put existing bucket	get
Map	254_132 ($\textcolor{red}{0.02\%}$)	699_418 ($\textcolor{red}{9.52\%}$)	16_214 ($\textcolor{red}{264.44\%}$)	16_734 ($\textcolor{red}{240.88\%}$)

Statistics

binary_size: no change
max_mem: no change
cycles: 52.32% [26.46%, 78.17%]

Publisher & Subscriber

	pub_binary_size	sub_binary_size	subscribe_caller	subscribe_callee	publish_caller	publish_callee
Motoko	139_898 ($\textcolor{red}{0.01\%}$)	126_836 ($\textcolor{red}{0.01\%}$)	28_710 ($\textcolor{red}{96.09\%}$)	11_932 ($\textcolor{red}{41.19\%}$)	22_995 ($\textcolor{red}{118.38\%}$)	6_405 ($\textcolor{red}{74.90\%}$)
Rust	479_085 ($\textcolor{red}{1.47\%}$)	529_109 ($\textcolor{red}{1.77\%}$)	69_806 ($\textcolor{red}{35.31\%}$)	42_858 ($\textcolor{red}{23.65\%}$)	93_140 ($\textcolor{red}{25.58\%}$)	52_290 ($\textcolor{red}{25.65\%}$)

Statistics

binary_size: 0.81% [-0.29%, 1.92%]
max_mem: no change
cycles: 55.09% [30.56%, 79.63%]

Overall Statistics
binary_size: 0.66% [0.38%, 0.93%]
max_mem: no change
cycles: 36.24% [29.48%, 43.00%]

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	133_756	8_456_787_008	61_987_732	346_249	6_615_757_444	373_657
triemap	135_161	14_978_845_324	74_216_052	297_735	722_224	709_041
rbtree	135_844	7_404_914_600	57_995_940	108_390	332_553	352_845
splay	131_680	15_209_722_290	53_995_876	729_085	768_032	1_063_884
btree	176_210	10_885_917_477	31_103_892	369_256	512_113	570_228
zhenya_hashmap	141_692	3_304_768_283	65_987_480	83_813	103_431	122_290
btreemap_rs	419_571	1_800_692_107	13_762_560	75_532	125_504	91_329
imrc_hashmap_rs	419_256	2_566_397_171	122_454_016	38_485	178_181	115_627
hashmap_rs	413_039	432_766_256	36_536_320	21_628	26_813	24_896

Priority queue

	binary_size	heapify 1m	max mem	pop_min 50	put 50
heap	127_692	6_314_372_552	29_995_836	692_956	255_044	660_190
heap_rs	410_675	140_170_301	9_109_504	59_205	23_229	59_428

Growable array

	binary_size	generate 5k	max mem	batch_get 500	batch_put 500	batch_remove 500
buffer	135_403	2_751_546	65_508	103_498	878_959	171_998
vector	133_879	2_384_703	24_764	165_680	227_918	218_436
vec_rs	409_470	289_504	655_360	17_114	30_524	25_660

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	170_111	307_625_902	277_653_343	43_219	29_620
Rust	497_732	82_788_057	56_793_048	49_501	52_193

Certified map

	binary_size	generate 10k	max mem	inc	witness
Motoko	162_022	23_978_863_464	3_429_924	2_853_149	425_671
Rust	441_297	6_312_527_623	1_081_344	1_004_499	301_497

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	225_863	47_763	22_599	18_949	20_115
Rust	717_659	532_119	101_647	124_976	135_521

DIP721 NFT

	binary_size	init	mint_token	transfer_token
Motoko	183_977	17_658	29_720	8_835
Rust	777_603	141_523	368_093	91_142

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	118_901	23_265
Rust	23_573	1_165

Timer

	binary_size	setTimer	cancelTimer
Motoko	125_145	51_903	4_624
Rust	442_822	67_944	11_048

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_390	59_396_776	118	118	118
copying	1_338_231_272	59_396_776	1_337_913_554	1_338_002_356	1_337_919_129
compacting	1_911_420_593	59_396_776	1_473_824_171	1_756_485_051	1_787_369_939
generational	2_918_248_625	59_405_240	1_141_865_950	1_228_537	1_127_895
incremental	33_436_705	1_136_153_832	333_734_166	336_829_512	336_860_690

Actor class

	binary size	put new bucket	put existing bucket	get
Map	254_132	699_418	16_214	16_734

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	139_898	126_836	28_710	11_932	22_995	6_405
Rust	479_085	529_109	69_806	42_858	93_140	52_290

dfinity / canister-profiling

Function cost set to 5 #76

Map

Priority queue

Growable array

Statistics

SHA-2

Certified map

Statistics

Basic DAO

DIP721 NFT

Statistics

Heartbeat

Timer

Statistics

Garbage Collection

Actor class

Statistics

Publisher & Subscriber

Statistics

Overall Statistics

Collection libraries

💎 Takeaways

Map

Priority queue

Growable array

Cryptographic libraries

SHA-2

Certified map

Sample Dapps

Basic DAO

DIP721 NFT

Heartbeat / Timer

Heartbeat

Timer

Motoko Specific Benchmarks

Garbage Collection

Actor class

Publisher & Subscriber