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Download the artifacts for this pull request:

• reports.zip

[github-actions[bot]]

Heartbeat

	binary_size	heartbeat
Motoko	147_123	8_284 ($\textcolor{green}{-30.61\%}$)
Rust	35_650	1_127

Timer

	binary_size	setTimer	cancelTimer
Motoko	162_940	34_639	1_923
Rust	525_666	55_806	10_541

Map

	binary_size	generate 50k	max mem	batch_get 50	batch_put 50	batch_remove 50
hashmap	195_614	2_387_017_574	9_102_052	1_293_415	689_196_283	1_225_104
triemap	201_396	2_289_521_095	9_716_008	893_026	2_115_311	1_191_446
rbtree	199_580	2_117_694_679	10_102_184	823_600	1_917_436	1_081_941
splay	197_628	2_359_847_674	9_302_108	1_305_478	2_225_547	1_306_435
btree	235_285	2_169_842_886	8_157_968	1_006_805	2_000_086	1_089_548
zhenya_hashmap	189_128	1_855_331_619	9_301_800	746_302	1_651_710	752_598
btreemap_rs	516_125	123_800_095	1_638_400	59_721	140_267	62_087
hashmap_rs	504_191	53_234_034	1_835_008	21_361	63_796	22_778

Priority queue

	binary_size	heapify 50k	mem	pop_min 50	put 50
heap	180_400	796_409_317	1_400_024	420_789	834_415
heap_rs	475_167	5_041_620	819_200	53_561	22_281

MoVM

	binary_size	generate 10k	max mem	batch_get 50	batch_put 50	batch_remove 50
hashmap	195_614	477_464_161	1_820_844	1_291_442	138_877_496	1_222_518
hashmap_rs	504_191	10_964_340	950_272	20_676	63_102	21_668
imrc_hashmap_rs	516_559	19_861_761	1_572_864	31_820	120_208	37_919
movm_rs	2_035_228	1_098_781_054	2_654_208	2_743_966	6_943_650	5_416_733
movm_dynamic_rs	2_251_739	555_576_815	2_129_920	2_186_795	3_010_179	2_166_068

Publisher & Subscriber

	pub_binary_size	sub_binary_size	subscribe_caller	subscribe_callee	publish_caller	publish_callee
Motoko	171_754	156_908	19_642	9_145	15_536	4_001
Rust	564_006	696_272	63_407	43_190	89_378	50_543

Basic DAO

	binary_size	init	transfer_token	submit_proposal	vote_proposal
Motoko	291_066	44_646 ($\textcolor{red}{0.13\%}$)	19_956 ($\textcolor{green}{-0.62\%}$)	14_282 ($\textcolor{red}{0.01\%}$)	16_944 ($\textcolor{red}{0.37\%}$)
Rust	944_429	541_999	102_465	125_877	138_810

DIP721 NFT

	binary_size	init	mint_token	transfer_token
Motoko	235_243	13_379	24_678	5_357
Rust	998_075	147_146	381_217	95_471

Motoko Garbage Collection

	generate 80k	max mem	batch_get 50	batch_put 50	batch_remove 50
default	247_115_881	15_539_984	50	50	50
copying	247_115_831	15_539_984	247_110_319	247_262_382	247_262_501
compacting	409_365_425	15_539_984	308_339_012	348_775_445	352_663_118
generational	624_423_107	15_540_260	57_009	1_390_483	1_060_163

[github-actions[bot]]

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_123	8_284
Rust	35_650	1_127

Timer

	binary_size	setTimer	cancelTimer
Motoko	162_940	34_639	1_923
Rust	525_666	55_806	10_541

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 50k. Insert 50k Nat32 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_live_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 nlogn) 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.
• hashmap_rs uses the fxhash crate, which is the same as std::collections::HashMap, but with a deterministic hasher. This ensures reproducible result.
• btree comes from Byron Becker's stable BTreeMap library.
• zhenya_hashmap comes from Zhenya Usenko's stable HashMap library.
• The MoVM table measures the performance of an experimental implementation of Motoko interpreter. External developers can ignore this table for now.

Map

	binary_size	generate 50k	max mem	batch_get 50	batch_put 50	batch_remove 50
hashmap	195_614	2_387_017_574	9_102_052	1_293_415	689_196_283	1_225_104
triemap	201_396	2_289_521_095	9_716_008	893_026	2_115_311	1_191_446
rbtree	199_580	2_117_694_679	10_102_184	823_600	1_917_436	1_081_941
splay	197_628	2_359_847_674	9_302_108	1_305_478	2_225_547	1_306_435
btree	235_285	2_169_842_886	8_157_968	1_006_805	2_000_086	1_089_548
zhenya_hashmap	189_128	1_855_331_619	9_301_800	746_302	1_651_710	752_598
btreemap_rs	516_125	123_800_095	1_638_400	59_721	140_267	62_087
hashmap_rs	504_191	53_234_034	1_835_008	21_361	63_796	22_778

Priority queue

	binary_size	heapify 50k	mem	pop_min 50	put 50
heap	180_400	796_409_317	1_400_024	420_789	834_415	422_542
heap_rs	475_167	5_041_620	819_200	53_561	22_281	53_738

MoVM

	binary_size	generate 10k	max mem	batch_get 50	batch_put 50	batch_remove 50
hashmap	195_614	477_464_161	1_820_844	1_291_442	138_877_496	1_222_518
hashmap_rs	504_191	10_964_340	950_272	20_676	63_102	21_668
imrc_hashmap_rs	516_559	19_861_761	1_572_864	31_820	120_208	37_919
movm_rs	2_035_228	1_098_781_054	2_654_208	2_743_966	6_943_650	5_416_733
movm_dynamic_rs	2_251_739	555_576_815	2_129_920	2_186_795	3_010_179	2_166_068

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	171_754	156_908	19_642	9_145	15_536	4_001
Rust	564_006	696_272	63_407	43_190	89_378	50_543

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	291_066	44_646	19_956	14_282	16_944
Rust	944_429	541_999	102_465	125_877	138_810

DIP721 NFT

	binary_size	init	mint_token	transfer_token
Motoko	235_243	13_379	24_678	5_357
Rust	998_075	147_146	381_217	95_471

Motoko Garbage Collection

Measure Motoko garbage collection cost using the Triemap benchmark. The max mem column reports rts_max_live_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.

• 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.

	generate 80k	max mem	batch_get 50	batch_put 50	batch_remove 50
default	247_115_881	15_539_984	50	50	50
copying	247_115_831	15_539_984	247_110_319	247_262_382	247_262_501
compacting	409_365_425	15_539_984	308_339_012	348_775_445	352_663_118
generational	624_423_107	15_540_260	57_009	1_390_483	1_060_163

[github-actions[bot]]

Note Diffing the performance result against the published result from main branch

Heartbeat

	binary_size	heartbeat
Motoko	147_123	11_938
Rust	35_650	587 ($\textcolor{green}{-47.91\%}$)

Timer

	binary_size	setTimer	cancelTimer
Motoko	162_940	34_639	1_923
Rust	525_666	55_806	10_541

Map

	binary_size	generate 50k	max mem	batch_get 50	batch_put 50	batch_remove 50
hashmap	195_614	2_387_017_574	9_102_052	1_293_415	689_196_283	1_225_104
triemap	201_396	2_289_521_095	9_716_008	893_026	2_115_311	1_191_446
rbtree	199_580	2_117_694_679	10_102_184	823_600	1_917_436	1_081_941
splay	197_628	2_359_847_674	9_302_108	1_305_478	2_225_547	1_306_435
btree	235_285	2_169_842_886	8_157_968	1_006_805	2_000_086	1_089_548
zhenya_hashmap	189_128	1_855_331_619	9_301_800	746_302	1_651_710	752_598
btreemap_rs	516_125	123_800_095	1_638_400	59_721	140_267	62_087
hashmap_rs	504_191	53_234_034	1_835_008	21_361	63_796	22_778

Priority queue

	binary_size	heapify 50k	mem	pop_min 50	put 50
heap	180_400	796_409_317	1_400_024	420_789	834_415
heap_rs	475_167	5_041_620	819_200	53_561	22_281

MoVM

	binary_size	generate 10k	max mem	batch_get 50	batch_put 50	batch_remove 50
hashmap	195_614	477_464_161	1_820_844	1_291_442	138_877_496	1_222_518
hashmap_rs	504_191	10_964_340	950_272	20_676	63_102	21_668
imrc_hashmap_rs	516_559	19_861_761	1_572_864	31_820	120_208	37_919
movm_rs	2_035_228	1_098_781_054	2_654_208	2_743_966	6_943_650	5_416_733
movm_dynamic_rs	2_251_739	555_576_815	2_129_920	2_186_795	3_010_179	2_166_068

Publisher & Subscriber

	pub_binary_size	sub_binary_size	subscribe_caller	subscribe_callee	publish_caller	publish_callee
Motoko	171_754	156_908	19_642	9_145	15_536	4_001
Rust	564_006	696_272	63_407	43_190	89_378	50_543

Basic DAO

	binary_size	init	transfer_token	submit_proposal	vote_proposal
Motoko	291_066	44_646 ($\textcolor{red}{0.13\%}$)	19_838 ($\textcolor{green}{-1.21\%}$)	14_282 ($\textcolor{red}{0.01\%}$)	16_944 ($\textcolor{red}{0.37\%}$)
Rust	944_429	541_999	102_465	125_877	138_810

DIP721 NFT

	binary_size	init	mint_token	transfer_token
Motoko	235_243	13_379	24_678	5_357
Rust	998_075	147_146	381_217	95_471

Motoko Garbage Collection

	generate 80k	max mem	batch_get 50	batch_put 50	batch_remove 50
default	247_115_881	15_539_984	50	50	50
copying	247_115_831	15_539_984	247_110_319	247_262_382	247_262_501
compacting	409_365_425	15_539_984	308_339_012	348_775_445	352_663_118
generational	624_423_107	15_540_260	57_009	1_390_483	1_060_163

[github-actions[bot]]

Warning The flamegraph link only works after you merge.

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_123	11_938
Rust	35_650	587

Timer

	binary_size	setTimer	cancelTimer
Motoko	162_940	34_639	1_923
Rust	525_666	55_806	10_541

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 50k. Insert 50k Nat32 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_live_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 nlogn) 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.
• hashmap_rs uses the fxhash crate, which is the same as std::collections::HashMap, but with a deterministic hasher. This ensures reproducible result.
• btree comes from Byron Becker's stable BTreeMap library.
• zhenya_hashmap comes from Zhenya Usenko's stable HashMap library.
• The MoVM table measures the performance of an experimental implementation of Motoko interpreter. External developers can ignore this table for now.

Map

	binary_size	generate 50k	max mem	batch_get 50	batch_put 50	batch_remove 50
hashmap	195_614	2_387_017_574	9_102_052	1_293_415	689_196_283	1_225_104
triemap	201_396	2_289_521_095	9_716_008	893_026	2_115_311	1_191_446
rbtree	199_580	2_117_694_679	10_102_184	823_600	1_917_436	1_081_941
splay	197_628	2_359_847_674	9_302_108	1_305_478	2_225_547	1_306_435
btree	235_285	2_169_842_886	8_157_968	1_006_805	2_000_086	1_089_548
zhenya_hashmap	189_128	1_855_331_619	9_301_800	746_302	1_651_710	752_598
btreemap_rs	516_125	123_800_095	1_638_400	59_721	140_267	62_087
hashmap_rs	504_191	53_234_034	1_835_008	21_361	63_796	22_778

Priority queue

	binary_size	heapify 50k	mem	pop_min 50	put 50
heap	180_400	796_409_317	1_400_024	420_789	834_415	422_542
heap_rs	475_167	5_041_620	819_200	53_561	22_281	53_738

MoVM

	binary_size	generate 10k	max mem	batch_get 50	batch_put 50	batch_remove 50
hashmap	195_614	477_464_161	1_820_844	1_291_442	138_877_496	1_222_518
hashmap_rs	504_191	10_964_340	950_272	20_676	63_102	21_668
imrc_hashmap_rs	516_559	19_861_761	1_572_864	31_820	120_208	37_919
movm_rs	2_035_228	1_098_781_054	2_654_208	2_743_966	6_943_650	5_416_733
movm_dynamic_rs	2_251_739	555_576_815	2_129_920	2_186_795	3_010_179	2_166_068

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	171_754	156_908	19_642	9_145	15_536	4_001
Rust	564_006	696_272	63_407	43_190	89_378	50_543

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	291_066	44_646	19_838	14_282	16_944
Rust	944_429	541_999	102_465	125_877	138_810

DIP721 NFT

	binary_size	init	mint_token	transfer_token
Motoko	235_243	13_379	24_678	5_357
Rust	998_075	147_146	381_217	95_471

Motoko Garbage Collection

Measure Motoko garbage collection cost using the Triemap benchmark. The max mem column reports rts_max_live_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.

• 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.

	generate 80k	max mem	batch_get 50	batch_put 50	batch_remove 50
default	247_115_881	15_539_984	50	50	50
copying	247_115_831	15_539_984	247_110_319	247_262_382	247_262_501
compacting	409_365_425	15_539_984	308_339_012	348_775_445	352_663_118
generational	624_423_107	15_540_260	57_009	1_390_483	1_060_163