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	upgrade
hashmap	160_221 ($\textcolor{red}{0.12\%}$)	6_984_044_999 ($\textcolor{red}{0.00\%}$)	61_987_852 ($\textcolor{red}{0.00\%}$)	288_670	5_536_856_410 ($\textcolor{green}{-0.00\%}$)	310_195	9_128_784_003 ($\textcolor{red}{0.00\%}$)
triemap	163_474 ($\textcolor{red}{0.12\%}$)	11_463_655_150 ($\textcolor{green}{-0.00\%}$)	74_216_172 ($\textcolor{red}{0.00\%}$)	222_926	549_435	540_205	13_075_158_546 ($\textcolor{red}{0.00\%}$)
rbtree	158_149 ($\textcolor{red}{0.12\%}$)	5_979_229_900 ($\textcolor{green}{-0.00\%}$)	57_996_060 ($\textcolor{red}{0.00\%}$)	88_905	268_573	278_352 ($\textcolor{red}{0.00\%}$)	5_771_880_608 ($\textcolor{red}{0.00\%}$)
splay	159_956 ($\textcolor{red}{0.12\%}$)	11_568_250_103 ($\textcolor{green}{-0.00\%}$)	53_995_996 ($\textcolor{red}{0.00\%}$)	552_014	581_765	810_321	3_722_474_749 ($\textcolor{red}{0.00\%}$)
btree	187_897 ($\textcolor{red}{0.10\%}$)	8_224_242_789 ($\textcolor{red}{0.00\%}$)	31_104_012 ($\textcolor{red}{0.00\%}$)	277_542	384_171	429_041	2_517_941_583 ($\textcolor{red}{0.00\%}$)
zhenya_hashmap	160_509 ($\textcolor{red}{0.12\%}$)	2_201_622_562 ($\textcolor{red}{0.00\%}$)	22_773_100 ($\textcolor{red}{0.00\%}$)	48_627	61_839	70_872	2_695_448_620 ($\textcolor{red}{0.00\%}$)
btreemap_rs	477_612 ($\textcolor{green}{-3.37\%}$)	1_651_590_463 ($\textcolor{green}{-0.15\%}$)	27_590_656	66_862 ($\textcolor{green}{-0.04\%}$)	112_477 ($\textcolor{green}{-0.11\%}$)	76_234 ($\textcolor{green}{-6.17\%}$)	2_660_975_747 ($\textcolor{red}{10.82\%}$)
imrc_hashmap_rs	479_773 ($\textcolor{green}{-4.08\%}$)	2_392_906_831 ($\textcolor{green}{-0.59\%}$)	244_973_568	32_763 ($\textcolor{green}{-0.60\%}$)	163_245 ($\textcolor{green}{-0.41\%}$)	98_394 ($\textcolor{green}{-0.20\%}$)	5_191_575_323 ($\textcolor{green}{-0.35\%}$)
hashmap_rs	467_997 ($\textcolor{green}{-4.10\%}$)	403_296_648 ($\textcolor{red}{0.00\%}$)	73_138_176	16_851 ($\textcolor{green}{-2.88\%}$)	21_680 ($\textcolor{red}{0.15\%}$)	20_263 ($\textcolor{green}{-1.71\%}$)	1_144_828_025 ($\textcolor{red}{19.55\%}$)

Priority queue

	binary_size	heapify 1m	max mem	pop_min 50	put 50	pop_min 50.1	upgrade
heap	147_638 ($\textcolor{red}{0.13\%}$)	4_684_519_403 ($\textcolor{red}{0.00\%}$)	29_995_956 ($\textcolor{red}{0.00\%}$)	511_505	186_471	487_225 ($\textcolor{red}{0.00\%}$)	2_655_609_909 ($\textcolor{red}{0.00\%}$)
heap_rs	463_840 ($\textcolor{green}{-3.72\%}$)	121_602_221 ($\textcolor{green}{-1.22\%}$)	18_284_544	51_661 ($\textcolor{green}{-3.40\%}$)	18_245 ($\textcolor{green}{-0.10\%}$)	51_802 ($\textcolor{green}{-3.39\%}$)	440_739_988 ($\textcolor{red}{26.28\%}$)

Growable array

	binary_size	generate 5k	max mem	batch_get 500	batch_put 500	batch_remove 500	upgrade
buffer	151_004 ($\textcolor{red}{0.12\%}$)	2_082_623	65_644 ($\textcolor{red}{0.09\%}$)	73_092	671_517	127_592	2_474_639 ($\textcolor{red}{0.26\%}$)
vector	152_551 ($\textcolor{red}{0.12\%}$)	1_588_260	24_580 ($\textcolor{red}{0.24\%}$)	105_191	149_932	148_094	3_844_445 ($\textcolor{red}{0.17\%}$)
vec_rs	459_655 ($\textcolor{green}{-4.40\%}$)	265_683 ($\textcolor{red}{0.02\%}$)	1_310_720 ($\textcolor{green}{-4.76\%}$)	13_014 ($\textcolor{red}{0.22\%}$)	25_363 ($\textcolor{red}{0.13\%}$)	21_247 ($\textcolor{red}{0.15\%}$)	2_743_831 ($\textcolor{green}{-3.88\%}$)

Stable structures

	binary_size	generate 50k	max mem	batch_get 50	batch_put 50	batch_remove 50	upgrade
btreemap_rs	477_612 ($\textcolor{green}{-3.37\%}$)	70_026_986 ($\textcolor{green}{-0.29\%}$)	2_555_904	57_181 ($\textcolor{green}{-0.05\%}$)	86_494 ($\textcolor{green}{-0.25\%}$)	75_309 ($\textcolor{green}{-5.56\%}$)	113_837_931 ($\textcolor{red}{13.30\%}$)
btreemap_stable_rs	478_668 ($\textcolor{green}{-3.97\%}$)	4_224_209_849 ($\textcolor{red}{14.91\%}$)	2_621_440	2_528_769 ($\textcolor{red}{15.43\%}$)	4_605_548 ($\textcolor{red}{14.75\%}$)	7_817_380 ($\textcolor{red}{15.35\%}$)	653_359 ($\textcolor{green}{-8.56\%}$)
heap_rs	463_840 ($\textcolor{green}{-3.72\%}$)	6_139_838 ($\textcolor{green}{-1.21\%}$)	2_293_760	44_362 ($\textcolor{green}{-3.06\%}$)	18_477 ($\textcolor{green}{-0.10\%}$)	44_345 ($\textcolor{green}{-3.03\%}$)	23_149_372 ($\textcolor{red}{26.03\%}$)
heap_stable_rs	451_018 ($\textcolor{green}{-3.99\%}$)	279_422_369 ($\textcolor{red}{16.24\%}$)	458_752	2_346_843 ($\textcolor{red}{15.12\%}$)	241_158 ($\textcolor{red}{15.36\%}$)	2_329_183 ($\textcolor{red}{15.11\%}$)	653_433 ($\textcolor{green}{-8.54\%}$)
vec_rs	459_655 ($\textcolor{green}{-4.40\%}$)	2_866_886 ($\textcolor{red}{0.00\%}$)	2_228_224 ($\textcolor{green}{-2.86\%}$)	13_014 ($\textcolor{red}{0.22\%}$)	14_113 ($\textcolor{red}{0.23\%}$)	13_710 ($\textcolor{red}{0.23\%}$)	21_249_908 ($\textcolor{red}{28.20\%}$)
vec_stable_rs	446_031 ($\textcolor{green}{-4.16\%}$)	65_186_210 ($\textcolor{red}{17.27\%}$)	458_752	58_992 ($\textcolor{red}{12.05\%}$)	77_387 ($\textcolor{red}{14.23\%}$)	79_383 ($\textcolor{red}{13.99\%}$)	653_447 ($\textcolor{green}{-8.54\%}$)

Statistics

binary_size: -2.11% [-2.92%, -1.31%]
max_mem: -0.66% [-1.54%, 0.22%]
cycles: 3.26% [1.64%, 4.88%]

SHA-2

	binary_size	SHA-256	SHA-512	account_id	neuron_id
Motoko	173_220 ($\textcolor{red}{0.11\%}$)	247_480_401	228_033_044	30_017	20_760
Rust	477_934 ($\textcolor{green}{-4.07\%}$)	82_511_961 ($\textcolor{red}{0.00\%}$)	56_525_962 ($\textcolor{green}{-0.00\%}$)	42_432 ($\textcolor{green}{-0.11\%}$)	44_430 ($\textcolor{green}{-0.02\%}$)

Certified map

	binary_size	generate 10k	max mem	inc	witness	upgrade
Motoko	206_484 ($\textcolor{red}{0.09\%}$)	4_390_019_361 ($\textcolor{red}{0.00\%}$)	3_430_044 ($\textcolor{red}{0.00\%}$)	519_711	327_767	225_153_243 ($\textcolor{red}{0.00\%}$)
Rust	500_582 ($\textcolor{green}{-4.10\%}$)	6_199_155_471 ($\textcolor{green}{-0.05\%}$)	2_228_224	983_538 ($\textcolor{green}{-0.05\%}$)	288_338 ($\textcolor{green}{-0.07\%}$)	5_816_678_045 ($\textcolor{red}{0.09\%}$)

Statistics

binary_size: -1.99% [-4.84%, 0.85%]
max_mem: 0.00%
cycles: -0.02% [-0.05%, 0.01%]

Basic DAO

	binary_size	init	transfer_token	submit_proposal	vote_proposal	upgrade
Motoko	236_862 ($\textcolor{red}{0.08\%}$)	497_594 ($\textcolor{red}{1.18\%}$)	16_248 ($\textcolor{green}{-0.26\%}$)	12_674 ($\textcolor{red}{0.02\%}$)	14_115 ($\textcolor{green}{-0.15\%}$)	128_956 ($\textcolor{red}{5.32\%}$)
Rust	779_543 ($\textcolor{green}{-3.35\%}$)	548_099 ($\textcolor{red}{1.26\%}$)	86_604 ($\textcolor{red}{0.64\%}$)	105_959 ($\textcolor{green}{-1.24\%}$)	117_903 ($\textcolor{red}{0.72\%}$)	1_624_526 ($\textcolor{green}{-3.68\%}$)

DIP721 NFT

	binary_size	init	mint_token	transfer_token	upgrade
Motoko	195_127 ($\textcolor{red}{0.10\%}$)	472_267 ($\textcolor{red}{1.25\%}$)	22_357	4_729	71_602 ($\textcolor{red}{9.13\%}$)
Rust	799_705 ($\textcolor{green}{-2.58\%}$)	217_270 ($\textcolor{red}{3.42\%}$)	325_723 ($\textcolor{red}{0.42\%}$)	78_145 ($\textcolor{green}{-3.55\%}$)	1_797_952 ($\textcolor{green}{-3.36\%}$)

Statistics

binary_size: -1.44% [-3.54%, 0.67%]
max_mem: no change
cycles: 0.70% [-0.74%, 2.13%]

Heartbeat

	binary_size	heartbeat
Motoko	123_696 ($\textcolor{red}{0.15\%}$)	7_399 ($\textcolor{red}{96.89\%}$)
Rust	23_839 ($\textcolor{red}{0.05\%}$)	785

Timer

	binary_size	setTimer	cancelTimer
Motoko	129_966 ($\textcolor{red}{0.14\%}$)	15_227	1_684
Rust	422_512 ($\textcolor{green}{-4.29\%}$)	43_471 ($\textcolor{red}{0.01\%}$)	7_569 ($\textcolor{green}{-0.33\%}$)

Statistics

binary_size: -2.08% [-16.08%, 11.93%]
max_mem: no change
cycles: -0.16% [-1.24%, 0.93%]

Garbage Collection

	generate 700k	max mem	batch_get 50	batch_put 50	batch_remove 50
default	886_042_039 ($\textcolor{red}{0.00\%}$)	51_991_392 ($\textcolor{red}{0.00\%}$)	50	50	50
copying	886_041_989 ($\textcolor{red}{0.00\%}$)	51_991_392 ($\textcolor{red}{0.00\%}$)	886_022_376 ($\textcolor{red}{0.00\%}$)	886_091_464 ($\textcolor{red}{0.00\%}$)	886_024_532 ($\textcolor{red}{0.00\%}$)
compacting	1_465_238_570 ($\textcolor{green}{-0.00\%}$)	51_991_392 ($\textcolor{red}{0.00\%}$)	1_131_731_091 ($\textcolor{green}{-0.00\%}$)	1_337_770_735 ($\textcolor{red}{0.00\%}$)	1_364_176_230 ($\textcolor{red}{0.00\%}$)
generational	2_184_682_993 ($\textcolor{green}{-0.00\%}$)	51_999_856 ($\textcolor{red}{0.00\%}$)	855_707_553	1_057_808 ($\textcolor{red}{0.00\%}$)	947_924 ($\textcolor{red}{0.01\%}$)
incremental	28_518_613	985_885_652 ($\textcolor{red}{0.00\%}$)	290_276_212	292_998_697	292_988_797

Actor class

	binary size	put new bucket	put existing bucket	get
Map	261_665 ($\textcolor{red}{0.07\%}$)	654_501	4_459	4_919

Statistics

binary_size: no change
max_mem: 0.00% [0.00%, 0.00%]
cycles: 0.01% [-0.00%, 0.02%]

Publisher & Subscriber

	pub_binary_size	sub_binary_size	subscribe_caller	subscribe_callee	publish_caller	publish_callee
Motoko	144_769 ($\textcolor{red}{0.13\%}$)	131_630 ($\textcolor{red}{0.14\%}$)	14_651	8_456	10_539	3_669
Rust	457_215 ($\textcolor{green}{-4.23\%}$)	510_788 ($\textcolor{green}{-3.10\%}$)	51_624 ($\textcolor{red}{0.25\%}$)	34_412 ($\textcolor{green}{-0.21\%}$)	74_396 ($\textcolor{red}{0.24\%}$)	44_011 ($\textcolor{green}{-0.27\%}$)

Statistics

binary_size: -1.76% [-4.40%, 0.87%]
max_mem: no change
cycles: 0.00% [-0.33%, 0.33%]

Overall Statistics
binary_size: -1.98% [-2.57%, -1.39%]
max_mem: -0.43% [-0.99%, 0.13%]
cycles: 2.12% [1.08%, 3.16%]

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. The _stable and _stable_rs suffix represents that the library directly writes the state to stable memory using Region in Motoko and ic-stable-stuctures in Rust.

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.
upgrade. Upgrade the canister with the same Wasm module. For non-stable benchmarks, the map state is persisted by serializing and deserializing states into stable memory. For stable benchmarks, the upgrade takes no cycles, as the state is already in the stable memory.

💎 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 very large collections.

The upgrade column uses Candid for serializing stable data. In Rust, you may get better cycle cost by using a different serialization format. Another slowdown in Rust is that ic-stable-structures tends to be slower than the region memory in Motoko.

Different library has different ways for persisting data during upgrades, there are mainly three categories:

Use stable variable directly in Motoko: zhenya_hashmap, btree, vector

Expose and serialize external state (share/unshare in Motoko, candid::Encode in Rust): rbtree, heap, btreemap_rs, hashmap_rs, heap_rs, vector_rs

Use pre/post-upgrade hooks to convert data into an array: hashmap, splay, triemap, buffer, imrc_hashmap_rs

The stable benchmarks are much more expensive than their non-stable counterpart, because the stable memory API is much more expensive. The benefit is that they get fast upgrade. The upgrade still needs to parse the metadata when initializing the upgraded Wasm module.

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	upgrade
hashmap	160_221	6_984_044_999	61_987_852	288_670	5_536_856_410	310_195	9_128_784_003
triemap	163_474	11_463_655_150	74_216_172	222_926	549_435	540_205	13_075_158_546
rbtree	158_149	5_979_229_900	57_996_060	88_905	268_573	278_352	5_771_880_608
splay	159_956	11_568_250_103	53_995_996	552_014	581_765	810_321	3_722_474_749
btree	187_897	8_224_242_789	31_104_012	277_542	384_171	429_041	2_517_941_583
zhenya_hashmap	160_509	2_201_622_562	22_773_100	48_627	61_839	70_872	2_695_448_620
btreemap_rs	477_612	1_651_590_463	27_590_656	66_862	112_477	76_234	2_660_975_747
imrc_hashmap_rs	479_773	2_392_906_831	244_973_568	32_763	163_245	98_394	5_191_575_323
hashmap_rs	467_997	403_296_648	73_138_176	16_851	21_680	20_263	1_144_828_025

Priority queue

	binary_size	heapify 1m	max mem	pop_min 50	put 50	pop_min 50	upgrade
heap	147_638	4_684_519_403	29_995_956	511_505	186_471	487_225	2_655_609_909
heap_rs	463_840	121_602_221	18_284_544	51_661	18_245	51_802	440_739_988

Growable array

	binary_size	generate 5k	max mem	batch_get 500	batch_put 500	batch_remove 500	upgrade
buffer	151_004	2_082_623	65_644	73_092	671_517	127_592	2_474_639
vector	152_551	1_588_260	24_580	105_191	149_932	148_094	3_844_445
vec_rs	459_655	265_683	1_310_720	13_014	25_363	21_247	2_743_831

Stable structures

	binary_size	generate 50k	max mem	batch_get 50	batch_put 50	batch_remove 50	upgrade
btreemap_rs	477_612	70_026_986	2_555_904	57_181	86_494	75_309	113_837_931
btreemap_stable_rs	478_668	4_224_209_849	2_621_440	2_528_769	4_605_548	7_817_380	653_359
heap_rs	463_840	6_139_838	2_293_760	44_362	18_477	44_345	23_149_372
heap_stable_rs	451_018	279_422_369	458_752	2_346_843	241_158	2_329_183	653_433
vec_rs	459_655	2_866_886	2_228_224	13_014	14_113	13_710	21_249_908
vec_stable_rs	446_031	65_186_210	458_752	58_992	77_387	79_383	653_447

Environment

dfx 0.15.1

Motoko compiler 0.10.0 (source a3ywvw0a-p5a03qy6-vscbl9j8-qxszbxa6)

rustc 1.73.0 (cc66ad468 2023-10-03)

ic-repl 0.5.1

ic-wasm 0.6.0
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.
- upgrade. Upgrade the canister with the same Wasm. In Motoko, we use stable variable. In Rust, we convert the tree to a vector before serialization.

SHA-2

	binary_size	SHA-256	SHA-512	account_id	neuron_id
Motoko	173_220	247_480_401	228_033_044	30_017	20_760
Rust	477_934	82_511_961	56_525_962	42_432	44_430

Certified map

	binary_size	generate 10k	max mem	inc	witness	upgrade
Motoko	206_484	4_390_019_361	3_430_044	519_711	327_767	225_153_243
Rust	500_582	6_199_155_471	2_228_224	983_538	288_338	5_816_678_045

Environment

dfx 0.15.1

Motoko compiler 0.10.0 (source a3ywvw0a-p5a03qy6-vscbl9j8-qxszbxa6)

rustc 1.73.0 (cc66ad468 2023-10-03)

ic-repl 0.5.1

ic-wasm 0.6.0
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	upgrade
Motoko	236_862	497_594	16_248	12_674	14_115	128_956
Rust	779_543	548_099	86_604	105_959	117_903	1_624_526

DIP721 NFT

	binary_size	init	mint_token	transfer_token	upgrade
Motoko	195_127	472_267	22_357	4_729	71_602
Rust	799_705	217_270	325_723	78_145	1_797_952

Environment

dfx 0.15.1

Motoko compiler 0.10.0 (source a3ywvw0a-p5a03qy6-vscbl9j8-qxszbxa6)

rustc 1.73.0 (cc66ad468 2023-10-03)

ic-repl 0.5.1

ic-wasm 0.6.0
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_696	7_399
Rust	23_839	785

Timer

	binary_size	setTimer	cancelTimer
Motoko	129_966	15_227	1_684
Rust	422_512	43_471	7_569

Environment

dfx 0.15.1

Motoko compiler 0.10.0 (source a3ywvw0a-p5a03qy6-vscbl9j8-qxszbxa6)

rustc 1.73.0 (cc66ad468 2023-10-03)

ic-repl 0.5.1

ic-wasm 0.6.0
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 700k	max mem	batch_get 50	batch_put 50	batch_remove 50
default	886_042_039	51_991_392	50	50	50
copying	886_041_989	51_991_392	886_022_376	886_091_464	886_024_532
compacting	1_465_238_570	51_991_392	1_131_731_091	1_337_770_735	1_364_176_230
generational	2_184_682_993	51_999_856	855_707_553	1_057_808	947_924
incremental	28_518_613	985_885_652	290_276_212	292_998_697	292_988_797

Actor class

	binary size	put new bucket	put existing bucket	get
Map	261_665	654_501	4_459	4_919

Environment

dfx 0.15.1

Motoko compiler 0.10.0 (source a3ywvw0a-p5a03qy6-vscbl9j8-qxszbxa6)

rustc 1.73.0 (cc66ad468 2023-10-03)

ic-repl 0.5.1

ic-wasm 0.6.0
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_769	131_630	14_651	8_456	10_539	3_669
Rust	457_215	510_788	51_624	34_412	74_396	44_011

Environment

dfx 0.15.1

Motoko compiler 0.10.0 (source a3ywvw0a-p5a03qy6-vscbl9j8-qxszbxa6)

rustc 1.73.0 (cc66ad468 2023-10-03)

ic-repl 0.5.1

ic-wasm 0.6.0

dfinity / canister-profiling

bump ic-repl #96

Map

Priority queue

Growable array

Stable structures

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

Stable structures

Environment

Cryptographic libraries

SHA-2

Certified map

Environment

Sample Dapps

Basic DAO

DIP721 NFT

Environment

Heartbeat / Timer

Heartbeat

Timer

Environment

Motoko Specific Benchmarks

Garbage Collection

Actor class

Environment

Publisher & Subscriber

Environment