# Speed Test Guide: HRM SRAM/DRAM Benchmarking

Use this guide to measure the **hardware performance** (latency, throughput, memory overhead) of the memory-tiered architecture.

> [!NOTE]
> This guide uses **synthetic data**. No external datasets or prior training are required. You can run this immediately after setup.

---

### 1. Environment Setup
Create a clean virtual environment and install dependencies:

```bash
python -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
pip install triton matplotlib
```

---

### 2. Run Hardware Comparison
This command compares the **Baseline (Original)** model vs. the **Tiered (Optimized)** model across multiple configurations.

```bash
python run_benchmark.py --mode compare --plot
```

**What this does:**
1. Generates random tensors to simulate a reasoning workload.
2. Compiles Triton kernels for the L-level (SRAM) and H-level (DRAM) paths.
3. Records precise GPU timing using `torch.cuda.Event`.
4. Saves charts to `benchmark_results/`.

---

### 3. Deep Dive into Tiered Metrics
To get a detailed breakdown of memory tier behavior:

```bash
python run_benchmark.py --mode tiered --iterations 50 --batch-sizes 8 --seq-lens 128
```

**Key Metrics to watch:**
- **L_lat(μs):** Speed of the fast-updating L-level module in SRAM.
- **H_lat(μs):** Speed of the planning H-level module in DRAM.
- **H/L Ratio:** Shows the latency multiplier between memory tiers.
- **Memory Efficiency:** Percentage of time spent on math vs. memory transfer.

---

### 4. Custom Hardware Stress Test
Test the limits of your GPU by increasing batch sizes or sequence lengths:

```bash
python run_benchmark.py \
    --mode compare \
    --batch-sizes 64,128 \
    --seq-lens 256,512 \
    --hidden-size 1024
```