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- Extended `SpatialDiffusion` trait with `apply_step`. - Implemented fused loop in `FiniteDifference1D` to reduce memory bandwidth. - Updated `TuringSystem` to use the optimized path. - Added benchmark `examples/bench_morphogenesis.rs`. - Documented in `.jules/profiler.md`. Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com>
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- Compacted multiline function calls in `math_explorer/examples/bench_morphogenesis.rs` and `math_explorer/src/biology/morphogenesis.rs`. - Fixed indentation in loop body. - Applied changes to match `cargo fmt -- --check` output from CI. Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com>
- Added `#[allow(clippy::too_many_arguments)]` to `SpatialDiffusion::apply_step` - This method is a performance-critical hot path (loop fusion) where bundling arguments would be counter-productive. Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com>
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📉 The Bottleneck:
The
TuringSystem::stepfunction was performing two passes over large arrays for every simulation step: one for diffusion (calculating Laplacian) and one for reaction/integration. This caused excessive memory traffic (6 reads + 4 writes per element).🚀 The Boost:
Before: 44.11µs per step
After: 23.96µs per step
Gain: 45.6% improvement.
💻 Technical Detail:
SpatialDiffusiontrait withapply_stepto allow strategies to fuse operations.apply_stepinFiniteDifference1Dto compute diffusion and reaction in a single loop.🧪 Verification:
bench_morphogenesiscreated and verified.PR created automatically by Jules for task 4639716987234036989 started by @fderuiter