120 lines
5.2 KiB
Markdown
120 lines
5.2 KiB
Markdown
# lofivor optimizations
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organized by performance goal. see journal.txt for detailed benchmarks.
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## current ceiling
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- **~150k entities @ 60fps** (i5-6500T / HD 530 integrated)
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- **~260k entities @ 60fps** (AMD Radeon discrete)
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- bottleneck: GPU-bound (update loop stays <1ms even at 200k+)
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---
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## completed optimizations
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### rendering pipeline (GPU)
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#### baseline: individual drawCircle
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- technique: `rl.drawCircle()` per entity
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- result: ~5k entities @ 60fps
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- problem: each call = separate GPU draw call
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#### optimization 1: texture blitting
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- technique: pre-render circle to 16x16 texture, `drawTexture()` per entity
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- result: ~50k entities @ 60fps
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- improvement: **10x** over baseline
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- why it works: raylib batches same-texture draws internally
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#### optimization 2: rlgl quad batching
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- technique: bypass `drawTexture()`, submit vertices directly via `rl.gl`
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- result: ~100k entities @ 60fps
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- improvement: **2x** over texture blitting, **20x** total
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- why it works: eliminates per-call overhead, vertices go straight to GPU buffer
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#### optimization 3: increased batch buffer
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- technique: increase raylib batch buffer from 8192 to 32768 vertices
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- result: ~140k entities @ 60fps (i5-6500T)
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- improvement: **~40%** over default buffer
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- why it works: fewer GPU flushes per frame
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#### optimization 4: GPU instancing (tested, minimal gain)
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- technique: `drawMeshInstanced()` with per-entity transform matrices
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- result: ~150k entities @ 60fps (i5-6500T) - similar to rlgl batching
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- improvement: **negligible** on integrated graphics
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- why it didn't help:
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- integrated GPU shares system RAM (no PCIe transfer savings)
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- 64-byte Matrix per entity vs ~80 bytes for rlgl vertices (similar bandwidth)
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- bottleneck is memory bandwidth, not draw call overhead
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- rlgl batching already minimizes draw calls effectively
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- note: may help more on discrete GPUs with dedicated VRAM
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---
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## future optimizations
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### milestone: push GPU ceiling higher
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these target the rendering bottleneck since update loop is already fast.
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| technique | description | expected gain |
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| ---------------------- | -------------------------------------------------------------------- | ------------------------------- |
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| SSBO instance data | pack (x, y, color) = 12 bytes instead of 64-byte matrices | moderate (less bandwidth) |
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| compute shader updates | move entity positions to GPU entirely, avoid CPU→GPU sync | significant |
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| OpenGL vs Vulkan | test raylib's Vulkan backend | unknown |
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| discrete GPU testing | test on dedicated GPU where instancing/SSBO shine | significant (different hw) |
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#### rendering culling
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| technique | description | expected gain |
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| ------------------ | ---------------------------------------- | ---------------------- |
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| frustum culling | skip entities outside view | depends on game design |
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| LOD rendering | reduce detail for distant/small entities | moderate |
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| temporal rendering | update/render subset per frame | moderate |
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---
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### milestone: push CPU ceiling (when it becomes the bottleneck)
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currently not the bottleneck - update stays <1ms at 100k. these become relevant when adding game logic, AI, or collision.
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#### collision detection
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| technique | description | expected gain |
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| ------------------ | ----------------------------------------- | ---------------------- |
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| uniform grid | spatial hash, O(1) neighbor lookup | high for dense scenes |
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| quadtree | adaptive spatial partitioning | high for sparse scenes |
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| broad/narrow phase | cheap AABB check before precise collision | moderate |
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#### update loop
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| technique | description | expected gain |
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| ---------------- | ----------------------------------------------- | ------------------- |
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| SIMD (AVX2/SSE) | vectorized position/velocity math | 2-4x on update |
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| struct-of-arrays | cache-friendly memory layout for SIMD | enables better SIMD |
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| multithreading | thread pool for parallel entity updates | scales with cores |
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| fixed-point math | integer math, deterministic, potentially faster | minor-moderate |
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#### memory layout
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| technique | description | expected gain |
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| --------------------- | ------------------------------------- | --------------------------- |
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| cache-friendly layout | hot data together, cold data separate | reduces cache misses |
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| entity pools | pre-allocated, reusable entity slots | reduces allocation overhead |
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| component packing | minimize struct padding | better cache utilization |
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---
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## testing methodology
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1. set target entity count
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2. run for 30+ seconds
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3. record frame times (target: stable 16.7ms)
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4. note when 60fps breaks
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5. compare update_ms vs render_ms to identify bottleneck
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see journal.txt for raw benchmark data.
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