# 性能優化指南

## 基準測試

運行完整基準測試：

```bash
swift run G12BServer ./model markbase --benchmark
```

輸出示例：

```
╔══════════════════════════════════════╗
║   Performance Benchmark              ║
║   Model: markbase                    ║
╚══════════════════════════════════════╝

📊 Model Loading Benchmark
────────────────────────────────────────
  Engine initialization: 0.123s
  Model loading: 2.456s
  Total: 2.579s
  Layers: 48
  Vocab: 262144
  Hidden: 3840

📊 Token Generation Benchmark
────────────────────────────────────────
  Run 1: 20 tokens in 1.052s (19.0 tok/s)
  Run 2: 20 tokens in 1.048s (19.1 tok/s)
  Run 3: 20 tokens in 1.050s (19.0 tok/s)

  Average: 20 tokens in 1.050s
  Speed: 19.0 tok/s

📊 Tokenizer Benchmark
────────────────────────────────────────
  Encode: "Hello, world!..." -> 5 tokens in 0.012ms
  Decode: 5 tokens -> "Hello, world!..." in 0.005ms

📊 Buffer Pool Benchmark
────────────────────────────────────────
  Without pool: 0.123s (8130 allocs/s)
  With pool: 0.045s (22222 allocs/s)

  Speedup: 2.7x
```

## 優化技術

### 1. SIMD 優化

- **Attention**: 17.22x 提升 (threadgroup cache + float4)
- **Matmul**: 2.93x 提升 (SIMD batch processing)
- **RMS Norm**: 4.53x 提升 (parallel reduction)

### 2. Kernel 融合

融合 kernels 減少 dispatch 次數：

- `rms_norm_matmul_fused` - 融合 RMS Norm + Matmul
- `gelu_eltwise_mul_fused` - 融合 GELU + Elementwise Mul
- `residual_rms_norm_fused` - 融合 Residual + RMS Norm
- `attention_o_proj_fused` - 融合 Attention + O Projection

### 3. Buffer Pool

緩衝區池減少內存分配：

- 重用 MTLBuffer
- 256 字節對齊
- 2.7x 加速比

### 4. 異步推理

異步生成減少阻塞：

- AsyncTokenGenerator
- Prefetcher
- 非阻塞 forward pass

## 性能對比

| 優化項 | 基準 | 優化後 | 提升 |
|--------|------|--------|------|
| Attention | 3.75ms | 0.22ms | 17.22x |
| Matmul | 1.38ms | 0.42ms | 2.93x |
| RMS Norm | 0.748ms | 0.165ms | 4.53x |
| **總體** | **0.100s/token** | **0.051s/token** | **2x** |

## 未來優化

### Phase 4+

- [ ] Float16 支持
- [ ] Batch inference
- [ ] Paged attention
- [ ] Speculative decoding