implemented pair_symmetry that uses a LUT to calculate the distances

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smail alijagic
2026-05-20 10:29:17 +02:00
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3 changed files with 410 additions and 0 deletions
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@@ -35,6 +35,7 @@ System Version: IdeaPad Slim 5 14AKP10
| --- | --------------------------------------- | ---------------- | -------------- | ------------------------- | ------ |
| 0 | [Baseline](optimization_baseline.md) | 1'058'400'000.00 | 850'352'900.00 | 1.24 | |
| 1 | [Inlining](optimization_inlining.md) | 1'058'400'000.00 | 756'088'260.00 | 1.40 | Elliot |
| 2 | [Symmetry](optimization_symmetry.md) | | | | Smail |
### ARM (Apple)
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# Optimization: Inlining
- **Author**: Smail
- **Predecessor**: variant_inlining_7.cpp
- **File(s)**: variants/variant_inlining_7_symmetry_1.cpp
- **Status**: TBA
## What this changes
Instead of calculating the distance and norm vector between two pedestrians everytime,
we created a LUT that only calculates the distances once and r_a_b_norm (with expensive sqrt flop `dx`, `dy`, `norm`).
The forces are somewhat symmetric, however it would still require a lot of calculations,
maybe this second symmetry optimization can be done at a later stage.
When i > j we need the opposite vector which is $curr * -1$.
## Code (relevant snippet)
TBD - see `variants/variant_inlining_7_symmetry_1.cpp` for the final version, and the
individual file headers for the incremental steps.
## Benchmark
Conditions: single run (TODO: switch to min-of-N), N_ITER=1000,
-O3 -march=native -flto. Predecessor reference for the same config is the
baseline row.
### x86
**Machine**:
```
Processors: 16 x AMD Ryzen AI 7 350 w/ Radeon 860M
Memory: 32 GiB of RAM (30.6 GiB usable)
Graphics Processor: AMD Radeon 860M Graphics
Manufacturer: LENOVO
Product Name: 83HX
System Version: IdeaPad Slim 5 14AKP10
```
**Frequency**: 2 GHz
Existing single-point measurement (N unspecified, kept from the previous
OPTIMIZATIONS.md; W matches roughly N=120 at N_ITER=1000):
| Variant | W(n) [Flops] | T(n) [Cycles] | Performance [Flops/Cycle] |
| -------- | ---------------- | -------------- | ------------------------- |
| Baseline | 1'058'400'000.00 | 850'352'900.00 | 1.24 |
| Inlining | 1'058'400'000.00 | 756'088'260.00 | 1.40 |
| Symmetry | | | |
Inlining is about 12.9% faster on this point. A full N sweep with min-of-3 is TODO.
Full sweep (TODO):
| N | W(n) [Flops] | T(n) [Cycles] | Performance [Flops/Cycle] | Diff vs Predecessor |
| ---- | ------------ | ------------- | ------------------------- | ------------------- |
| 50 | | | | |
| 100 | | | | |
| 200 | | | | |
| 500 | | | | |
| 1000 | | | | |
### ARM (Apple)
**Machine**:
```
TBD
```
**Frequency**: TBD
| N | W(n) [Flops] | T(n) [Cycles] | Performance [Flops/Cycle] | Diff vs Predecessor |
| ---- | ------------ | ------------- | ------------------------- | ------------------- |
| 50 | | | | |
| 100 | | | | |
| 200 | | | | |
| 500 | | | | |
| 1000 | | | | |
## Analysis
Speedup likely comes mainly from halving the number of `sqrt` flops.
## Takeaway
TBD