A fork of bevy to implement some features for forestia
6121e5f933
# Problem Definition The current change tracking (via flags for both components and resources) fails to detect changes made by systems that are scheduled to run earlier in the frame than they are. This issue is discussed at length in [#68](https://github.com/bevyengine/bevy/issues/68) and [#54](https://github.com/bevyengine/bevy/issues/54). This is very much a draft PR, and contributions are welcome and needed. # Criteria 1. Each change is detected at least once, no matter the ordering. 2. Each change is detected at most once, no matter the ordering. 3. Changes should be detected the same frame that they are made. 4. Competitive ergonomics. Ideally does not require opting-in. 5. Low CPU overhead of computation. 6. Memory efficient. This must not increase over time, except where the number of entities / resources does. 7. Changes should not be lost for systems that don't run. 8. A frame needs to act as a pure function. Given the same set of entities / components it needs to produce the same end state without side-effects. **Exact** change-tracking proposals satisfy criteria 1 and 2. **Conservative** change-tracking proposals satisfy criteria 1 but not 2. **Flaky** change tracking proposals satisfy criteria 2 but not 1. # Code Base Navigation There are three types of flags: - `Added`: A piece of data was added to an entity / `Resources`. - `Mutated`: A piece of data was able to be modified, because its `DerefMut` was accessed - `Changed`: The bitwise OR of `Added` and `Changed` The special behavior of `ChangedRes`, with respect to the scheduler is being removed in [#1313](https://github.com/bevyengine/bevy/pull/1313) and does not need to be reproduced. `ChangedRes` and friends can be found in "bevy_ecs/core/resources/resource_query.rs". The `Flags` trait for Components can be found in "bevy_ecs/core/query.rs". `ComponentFlags` are stored in "bevy_ecs/core/archetypes.rs", defined on line 446. # Proposals **Proposal 5 was selected for implementation.** ## Proposal 0: No Change Detection The baseline, where computations are performed on everything regardless of whether it changed. **Type:** Conservative **Pros:** - already implemented - will never miss events - no overhead **Cons:** - tons of repeated work - doesn't allow users to avoid repeating work (or monitoring for other changes) ## Proposal 1: Earlier-This-Tick Change Detection The current approach as of Bevy 0.4. Flags are set, and then flushed at the end of each frame. **Type:** Flaky **Pros:** - already implemented - simple to understand - low memory overhead (2 bits per component) - low time overhead (clear every flag once per frame) **Cons:** - misses systems based on ordering - systems that don't run every frame miss changes - duplicates detection when looping - can lead to unresolvable circular dependencies ## Proposal 2: Two-Tick Change Detection Flags persist for two frames, using a double-buffer system identical to that used in events. A change is observed if it is found in either the current frame's list of changes or the previous frame's. **Type:** Conservative **Pros:** - easy to understand - easy to implement - low memory overhead (4 bits per component) - low time overhead (bit mask and shift every flag once per frame) **Cons:** - can result in a great deal of duplicated work - systems that don't run every frame miss changes - duplicates detection when looping ## Proposal 3: Last-Tick Change Detection Flags persist for two frames, using a double-buffer system identical to that used in events. A change is observed if it is found in the previous frame's list of changes. **Type:** Exact **Pros:** - exact - easy to understand - easy to implement - low memory overhead (4 bits per component) - low time overhead (bit mask and shift every flag once per frame) **Cons:** - change detection is always delayed, possibly causing painful chained delays - systems that don't run every frame miss changes - duplicates detection when looping ## Proposal 4: Flag-Doubling Change Detection Combine Proposal 2 and Proposal 3. Differentiate between `JustChanged` (current behavior) and `Changed` (Proposal 3). Pack this data into the flags according to [this implementation proposal](https://github.com/bevyengine/bevy/issues/68#issuecomment-769174804). **Type:** Flaky + Exact **Pros:** - allows users to acc - easy to implement - low memory overhead (4 bits per component) - low time overhead (bit mask and shift every flag once per frame) **Cons:** - users must specify the type of change detection required - still quite fragile to system ordering effects when using the flaky `JustChanged` form - cannot get immediate + exact results - systems that don't run every frame miss changes - duplicates detection when looping ## [SELECTED] Proposal 5: Generation-Counter Change Detection A global counter is increased after each system is run. Each component saves the time of last mutation, and each system saves the time of last execution. Mutation is detected when the component's counter is greater than the system's counter. Discussed [here](https://github.com/bevyengine/bevy/issues/68#issuecomment-769174804). How to handle addition detection is unsolved; the current proposal is to use the highest bit of the counter as in proposal 1. **Type:** Exact (for mutations), flaky (for additions) **Pros:** - low time overhead (set component counter on access, set system counter after execution) - robust to systems that don't run every frame - robust to systems that loop **Cons:** - moderately complex implementation - must be modified as systems are inserted dynamically - medium memory overhead (4 bytes per component + system) - unsolved addition detection ## Proposal 6: System-Data Change Detection For each system, track which system's changes it has seen. This approach is only worth fully designing and implementing if Proposal 5 fails in some way. **Type:** Exact **Pros:** - exact - conceptually simple **Cons:** - requires storing data on each system - implementation is complex - must be modified as systems are inserted dynamically ## Proposal 7: Total-Order Change Detection Discussed [here](https://github.com/bevyengine/bevy/issues/68#issuecomment-754326523). This proposal is somewhat complicated by the new scheduler, but I believe it should still be conceptually feasible. This approach is only worth fully designing and implementing if Proposal 5 fails in some way. **Type:** Exact **Pros:** - exact - efficient data storage relative to other exact proposals **Cons:** - requires access to the scheduler - complex implementation and difficulty grokking - must be modified as systems are inserted dynamically # Tests - We will need to verify properties 1, 2, 3, 7 and 8. Priority: 1 > 2 = 3 > 8 > 7 - Ideally we can use identical user-facing syntax for all proposals, allowing us to re-use the same syntax for each. - When writing tests, we need to carefully specify order using explicit dependencies. - These tests will need to be duplicated for both components and resources. - We need to be sure to handle cases where ambiguous system orders exist. `changing_system` is always the system that makes the changes, and `detecting_system` always detects the changes. The component / resource changed will be simple boolean wrapper structs. ## Basic Added / Mutated / Changed 2 x 3 design: - Resources vs. Components - Added vs. Changed vs. Mutated - `changing_system` runs before `detecting_system` - verify at the end of tick 2 ## At Least Once 2 x 3 design: - Resources vs. Components - Added vs. Changed vs. Mutated - `changing_system` runs after `detecting_system` - verify at the end of tick 2 ## At Most Once 2 x 3 design: - Resources vs. Components - Added vs. Changed vs. Mutated - `changing_system` runs once before `detecting_system` - increment a counter based on the number of changes detected - verify at the end of tick 2 ## Fast Detection 2 x 3 design: - Resources vs. Components - Added vs. Changed vs. Mutated - `changing_system` runs before `detecting_system` - verify at the end of tick 1 ## Ambiguous System Ordering Robustness 2 x 3 x 2 design: - Resources vs. Components - Added vs. Changed vs. Mutated - `changing_system` runs [before/after] `detecting_system` in tick 1 - `changing_system` runs [after/before] `detecting_system` in tick 2 ## System Pausing 2 x 3 design: - Resources vs. Components - Added vs. Changed vs. Mutated - `changing_system` runs in tick 1, then is disabled by run criteria - `detecting_system` is disabled by run criteria until it is run once during tick 3 - verify at the end of tick 3 ## Addition Causes Mutation 2 design: - Resources vs. Components - `adding_system_1` adds a component / resource - `adding system_2` adds the same component / resource - verify the `Mutated` flag at the end of the tick - verify the `Added` flag at the end of the tick First check tests for: https://github.com/bevyengine/bevy/issues/333 Second check tests for: https://github.com/bevyengine/bevy/issues/1443 ## Changes Made By Commands - `adding_system` runs in Update in tick 1, and sends a command to add a component - `detecting_system` runs in Update in tick 1 and 2, after `adding_system` - We can't detect the changes in tick 1, since they haven't been processed yet - If we were to track these changes as being emitted by `adding_system`, we can't detect the changes in tick 2 either, since `detecting_system` has already run once after `adding_system` :( # Benchmarks See: [general advice](https://github.com/bevyengine/bevy/blob/master/docs/profiling.md), [Criterion crate](https://github.com/bheisler/criterion.rs) There are several critical parameters to vary: 1. entity count (1 to 10^9) 2. fraction of entities that are changed (0% to 100%) 3. cost to perform work on changed entities, i.e. workload (1 ns to 1s) 1 and 2 should be varied between benchmark runs. 3 can be added on computationally. We want to measure: - memory cost - run time We should collect these measurements across several frames (100?) to reduce bootup effects and accurately measure the mean, variance and drift. Entity-component change detection is much more important to benchmark than resource change detection, due to the orders of magnitude higher number of pieces of data. No change detection at all should be included in benchmarks as a second control for cases where missing changes is unacceptable. ## Graphs 1. y: performance, x: log_10(entity count), color: proposal, facet: performance metric. Set cost to perform work to 0. 2. y: run time, x: cost to perform work, color: proposal, facet: fraction changed. Set number of entities to 10^6 3. y: memory, x: frames, color: proposal # Conclusions 1. Is the theoretical categorization of the proposals correct according to our tests? 2. How does the performance of the proposals compare without any load? 3. How does the performance of the proposals compare with realistic loads? 4. At what workload does more exact change tracking become worth the (presumably) higher overhead? 5. When does adding change-detection to save on work become worthwhile? 6. Is there enough divergence in performance between the best solutions in each class to ship more than one change-tracking solution? # Implementation Plan 1. Write a test suite. 2. Verify that tests fail for existing approach. 3. Write a benchmark suite. 4. Get performance numbers for existing approach. 5. Implement, test and benchmark various solutions using a Git branch per proposal. 6. Create a draft PR with all solutions and present results to team. 7. Select a solution and replace existing change detection. Co-authored-by: Brice DAVIER <bricedavier@gmail.com> Co-authored-by: Carter Anderson <mcanders1@gmail.com> |
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What is Bevy?
Bevy is a refreshingly simple data-driven game engine built in Rust. It is free and open-source forever!
WARNING
Bevy is still in the very early stages of development. APIs can and will change (now is the time to make suggestions!). Important features are missing. Documentation is sparse. Please don't build any serious projects in Bevy unless you are prepared to be broken by API changes constantly.
Design Goals
- Capable: Offer a complete 2D and 3D feature set
- Simple: Easy for newbies to pick up, but infinitely flexible for power users
- Data Focused: Data-oriented architecture using the Entity Component System paradigm
- Modular: Use only what you need. Replace what you don't like
- Fast: App logic should run quickly, and when possible, in parallel
- Productive: Changes should compile quickly ... waiting isn't fun
About
- Features: A quick overview of Bevy's features.
- Roadmap: The Bevy team's development plan.
- Introducing Bevy: A blog post covering some of Bevy's features
Docs
- The Bevy Book: Bevy's official documentation. The best place to start learning Bevy.
- Bevy Rust API Docs: Bevy's Rust API docs, which are automatically generated from the doc comments in this repo.
- Community-Made Learning Resources: Tutorials, documentation, and examples made by the Bevy community.
Community
Before contributing or participating in discussions with the community, you should familiarize yourself with our Code of Conduct and How to Contribute
- Discord: Bevy's official discord server.
- Reddit: Bevy's official subreddit.
- Stack Overflow: Questions tagged Bevy on Stack Overflow.
- Awesome Bevy: A collection of awesome Bevy projects.
Getting Started
We recommend checking out The Bevy Book for a full tutorial.
Follow the Setup guide to ensure your development environment is set up correctly. Once set up, you can quickly try out the examples by cloning this repo and running the following commands:
# Switch to the correct version (latest release, default is main development branch)
git checkout latest
# Runs the "breakout" example
cargo run --example breakout
Fast Compiles
Bevy can be built just fine using default configuration on stable Rust. However for really fast iterative compiles, you should enable the "fast compiles" setup by following the instructions here.
Focus Areas
Bevy has the following Focus Areas. We are currently focusing our development efforts in these areas, and they will receive priority for Bevy developers' time. If you would like to contribute to Bevy, you are heavily encouraged to join in on these efforts:
Editor-Ready UI
PBR / Clustered Forward Rendering
Scenes
Libraries Used
Bevy is only possible because of the hard work put into these foundational technologies:
- wgpu-rs: modern / low-level / cross-platform graphics library inspired by Vulkan
- glam-rs: a simple and fast 3D math library for games and graphics
- winit: cross-platform window creation and management in Rust
- spirv-reflect: Reflection API in rust for SPIR-V shader byte code
Bevy Cargo Features
This list outlines the different cargo features supported by Bevy. These allow you to customize the Bevy feature set for your use-case.
Third Party Plugins
Plugins are very welcome to extend Bevy's features. Guidelines are available to help integration and usage.
Thanks and Alternatives
Additionally, we would like to thank the Amethyst, macroquad, coffee, ggez, rg3d, and Piston projects for providing solid examples of game engine development in Rust. If you are looking for a Rust game engine, it is worth considering all of your options. Each engine has different design goals, and some will likely resonate with you more than others.