A suite of benchmarks designed to test and compare Rust ECS library performance across a variety of challenging circumstances.
The full benchmark report is available here.
| legion (*) | legion 0.2.4 | bevy | hecs | shipyard (*) | specs | |
|---|---|---|---|---|---|---|
| simple_insert | 0.434ms | 1.06ms | 0.865ms | 0.645ms | 2.49ms | 2.32ms |
| simple_iter | 13.4us (16.6us) | 13.4us | 14.4us | 26.7us | 110us (45.6us) | 34.3ms |
| frag_iter | 0.509us | 1.78us | 1.76us | 1.79us | 1.04us | 1.67us |
| heavy_compute | 0.701ms (0.723ms) | 4.34ms | 1.06ms | 1.02ms | 0.778ms (0.700ms) | 0.995ms |
| schedule | 52.3us (53.7us) | 151us | 94.9us | - | 580us (307us) | 244us |
| add_remove | 5.50ms | 3.07ms | - | 18.2ms | 2.90ms | 0.148ms |
| serialize_text | 17.9ms | - | - | - | - | - |
| serialize_binary | 6.42ms | - | - | - | - | - |
(*): The values in parentheses are results where per-benchmark storage optimizations were applied. Some of these are mutually exclusive, so with and without "packing" typically represent best and worst-case performance for the ECS.
The best result for each benchmark is marked in bold text. Note that run to run variance for these benchmarks is typically 2-3%, with outliers as much as 10%. All micro-benchmarks should be taken with a grain of salt, and any benchmarks within a few percent of each other should be considered "effectively equal".
This benchmark is designed to test the base cost of constructing entities and moving components into the ECS.
Inserts 10,000 entities, each with 4 components: Transform(mat4x4), Position(vec3), Rotation(vec3) and Velocity(vec3).
This benchmark is designed to test the core overheads involved in component iteration in best-case conditions. The iteration should occur on a single CPU core.
Dataset: 10,000 entities, each with 4 components: Transform(mat4x4), Position(vec3), Rotation(vec3) and Velocity(vec3).
Test: Iterate through all entities with Position and Velocity, and add velocity onto position.
This benchmark is designed to test how the ECS handles iteration through a fragmented dataset. The iteration should occur on a single CPU core.
Dataset: 26 component types (A(f32) through Z(f32)), each with 20 entities plus a Data(f32) component.
Test: Iterate through all entities with a Data component and double its value.
This benchmark is designed to test how efficiently the ECS can schedule multiple independent systems on a multi-core CPU. This is primarily an outer-parallelism test. Each system should execute on a single CPU core.
Dataset:
- 10,000 entities with
(A, B)components. - 10,000 entities with
(A, B, C)components. - 10,000 entities with
(A, B, C, D)components. - 10,000 entities with
(A, B, C, E)components.
Test:
Three systems accessing the following components mutably, where each system swaps the values stored in each component:
(A, B)(C, D)(C, E)
This benchmark is designed to test the ECS's ability to scale when it is allowed to run a system over multiple CPU cores. This is primarily an inner-parallelism test.
Dataset: 10,000 entities with a mat4x4 component.
Test: Iterate through all mat4x4 components, and invert the matrix 10 times.
This benchmark is designed to test how quickly the ECS can add and then remove a component from an existing entity.
Dataset: 1,000 entities with a single A component.
Test: Iterate through all entities, adding a B component. Then iterate through all entities again, removing their B component.
This benchmark is designed to test how quickly the ECS and serialize and deserialize its entities in both text (RON) and binary (bincode) formats.
Dataset: 1000 entities with Transform(mat4x4), Position(vec3), Rotation(vec3) and Velocity(vec3) components.
Test: Serialize all entities to RON and bincode formats in-memory. Then deserialize back into the ECS. The RON and bincode formats should be separate benchmark tests.