# Technical Document Extraction: Attention Backward Speed Benchmark
## 1. Header Information
* **Title:** Attention backward speed, head dim 64 (H100 80GB SXM5)
* **Hardware Context:** NVIDIA H100 80GB SXM5 GPU.
* **Operation:** Backward pass of the Attention mechanism.
* **Parameter:** Head dimension fixed at 64.
## 2. Chart Metadata
* **Chart Type:** Grouped Bar Chart.
* **Y-Axis Label:** Speed (TFLOPs/s)
* **Y-Axis Scale:** 0 to 600 (increments of 200 labeled).
* **X-Axis Label:** Sequence length
* **X-Axis Categories:** 512, 1k, 2k, 4k, 8k, 16k.
* **Legend Location:** Top-left [x: ~0.15, y: ~0.85].
## 3. Legend and Series Identification
The chart compares four different implementations of the attention mechanism:
1. **Standard attention** (Blue): Represents the baseline implementation.
2. **FlashAttention-2** (Orange): An optimized attention algorithm.
3. **cuDNN** (Red): NVIDIA's Deep Neural Network library implementation.
4. **FlashAttention-3** (Purple): The latest iteration of the FlashAttention algorithm.
## 4. Data Extraction and Trend Analysis
### Trend Verification
* **Standard attention (Blue):** Shows very slow growth, plateauing early. It fails to scale with sequence length and results in an "OOM" (Out of Memory) error at the largest sequence length.
* **FlashAttention-2 (Orange):** Shows steady growth from 512 to 8k, then plateaus between 8k and 16k.
* **cuDNN (Red):** Shows significant performance gains as sequence length increases, consistently outperforming FlashAttention-2.
* **FlashAttention-3 (Purple):** The highest performing series across all sequence lengths. It shows a strong upward trend that begins to taper slightly at 16k but remains the leader.
### Data Table (TFLOPs/s)
| Sequence Length | Standard attention (Blue) | FlashAttention-2 (Orange) | cuDNN (Red) | FlashAttention-3 (Purple) |
| :--- | :--- | :--- | :--- | :--- |
| **512** | 68 | 198 | 266 | 272 |
| **1k** | 76 | 238 | 348 | 363 |
| **2k** | 88 | 264 | 395 | 422 |
| **4k** | 92 | 279 | 417 | 453 |
| **8k** | 95 | 287 | 432 | 472 |
| **16k** | OOM* | 291 | 433 | 474 |
*\*OOM = Out of Memory*
## 5. Key Observations
* **Performance Leadership:** FlashAttention-3 is the fastest implementation across all tested sequence lengths, reaching a peak of 474 TFLOPs/s at a 16k sequence length.
* **Memory Efficiency:** Standard attention is the only method that fails (OOM) at the 16k sequence length, highlighting the memory efficiency of the other three optimized kernels.
* **Scaling:** The performance gap between optimized kernels (FlashAttention-3, cuDNN) and the baseline (Standard attention) widens significantly as sequence length increases. At 8k, FlashAttention-3 is approximately 4.9x faster than Standard attention.
* **Comparison:** FlashAttention-3 consistently maintains a performance lead over the cuDNN implementation, with the gap being most pronounced at sequence lengths between 2k and 8k.
