docs: update benchmark documentation to reflect current HAMi usage

docs/benchmark.md

@@ -1,23 +1,29 @@
 ## Benchmarks
 
-Three instances from ai-benchmark have been used to evaluate vGPU-device-plugin performance as follows:
+> **⚠️ Note**: The benchmark data below is from an older version of the project (when it was called vGPU-device-plugin) and uses outdated test environments. While the results are kept for historical reference, they may not reflect the current performance of HAMi.
+>
+> For up-to-date performance testing, please refer to the [Running Benchmarks](#running-benchmarks) section below.
 
-| Test Environment | description                                              |
+### Historical Benchmark Results (Legacy)
+
+Three instances from ai-benchmark were used to evaluate vGPU-device-plugin performance:
+
+| Test Environment | Description                                              |
 | ---------------- | :------------------------------------------------------: |
 | Kubernetes version | v1.12.9                                                |
-| Docker  version    | 18.09.1                                                |
+| Docker version     | 18.09.1                                                |
 | GPU Type           | Tesla V100                                             |
 | GPU Num            | 2                                                      |
 
-| Test instance |                         description                         |
+| Test Instance |                         Description                         |
 | ------------- | :---------------------------------------------------------: |
 | nvidia-device-plugin      |               k8s + nvidia k8s-device-plugin                |
-| vGPU-device-plugin        | k8s + VGPU k8s-device-plugin，without virtual device memory |
-| vGPU-device-plugin(virtual device memory) |  k8s + VGPU k8s-device-plugin，with virtual device memory   |
+| vGPU-device-plugin        | k8s + vGPU k8s-device-plugin, without virtual device memory |
+| vGPU-device-plugin (virtual device memory) |  k8s + vGPU k8s-device-plugin, with virtual device memory   |
 
 Test Cases:
 
-| test id |     case      |   type    |         params          |
+| Test ID |     Case      |   Type    |         Params          |
 | ------- | :-----------: | :-------: | :---------------------: |
 | 1.1     | Resnet-V2-50  | inference |  batch=50,size=346*346  |
 | 1.2     | Resnet-V2-50  | training  |  batch=20,size=346*346  |
@@ -30,20 +36,76 @@ Test Cases:
 | 5.1     |     LSTM      | inference | batch=100,size=1024*300 |
 | 5.2     |     LSTM      | training  | batch=10,size=1024*300  |
 
-Test Result: ![img](../imgs/benchmark_inf.png)
+Historical Test Results:
+
+![img](../imgs/benchmark_inf.png)
 
 ![img](../imgs/benchmark_train.png)
 
-To reproduce:
+---
+
+## Running Benchmarks
+
+To benchmark HAMi performance in your environment, follow these steps:
+
+### Prerequisites
+
+- HAMi installed and configured in your Kubernetes cluster (see [Quick Start](../README.md#quick-start))
+- GPU nodes labeled with `gpu=on`
+- Kubernetes version >= 1.18
+- Docker or containerd runtime with NVIDIA support
+
+### Build Benchmark Image (Optional)
+
+If you want to build the benchmark image yourself:
+
+```bash
+cd benchmarks/ai-benchmark
+sh build.sh
+```
+
+### Run Benchmark Jobs
 
-1. install k8s-vGPU-scheduler, and configure properly
-2. run benchmark job
+HAMi provides two benchmark job configurations to compare performance:
 
+**1. Run benchmark on HAMi:**
+
+```bash
+cd benchmarks/deployments
+kubectl apply -f job-on-hami.yml
 ```
-$ kubectl apply -f benchmarks/ai-benchmark/ai-benchmark.yml
+
+This will deploy a job that uses HAMi's GPU sharing and memory isolation features (requesting 50% of GPU memory).
+
+**2. Run benchmark on NVIDIA device plugin (for comparison):**
+
+```bash
+kubectl apply -f job-on-nvidia-device-plugin.yml
 ```
 
-3. View the result by using kubctl logs
+For installing the official NVIDIA device plugin, refer to the [NVIDIA k8s-device-plugin Quick Start](https://github.com/NVIDIA/k8s-device-plugin?tab=readme-ov-file#quick-start).
+
+### View Results
+
+After the jobs complete, view the benchmark results:
 
+```bash
+# Check job status
+kubectl get jobs
+
+# View HAMi benchmark results
+kubectl logs job/ai-benchmark-on-hami
+
+# View NVIDIA device plugin benchmark results
+kubectl logs job/ai-benchmark-on-official
 ```
-$ kubectl logs [pod id]
+
+### Customizing Benchmarks
+
+You can modify the benchmark jobs in `benchmarks/deployments/` to test different configurations:
+
+- Adjust GPU memory allocation (e.g., `nvidia.com/gpumem-percentage: 50`)
+- Test with different GPU counts
+- Compare with and without HAMi's memory isolation features
+
+For more details, see the [benchmarks README](../benchmarks/README.md).

docs/benchmark_cn.md

@@ -1,23 +1,29 @@
 ## 性能测试
 
-在测试报告中，我们一共在下面五种场景都执行了ai-benchmark 测试脚本，并汇总最终结果：
+> **⚠️ 注意**：以下基准测试数据来自项目的早期版本（当时称为 vGPU-device-plugin），使用的测试环境已经过时。虽然这些结果被保留作为历史参考，但可能无法反映 HAMi 当前的性能表现。
+>
+> 如需进行最新的性能测试,请参考下方的[运行基准测试](#运行基准测试)部分。
+
+### 历史基准测试结果（旧版）
+
+在测试报告中，我们在以下场景中执行了 ai-benchmark 测试脚本，并汇总了最终结果：
 
 | 测试环境 | 环境描述                                              |
 | ---------------- | :------------------------------------------------------: |
 | Kubernetes version | v1.12.9                                                |
-| Docker  version    | 18.09.1                                                |
+| Docker version     | 18.09.1                                                |
 | GPU Type           | Tesla V100                                             |
 | GPU Num            | 2                                                      |
 
 | 测试名称 |                      测试用例                      |
 | -------- | :------------------------------------------------: |
-| Nvidia-device-plugin        |         k8s + nvidia官方k8s-device-plugin          |
-| vGPU-device-plugin        |      k8s + VGPU k8s-device-plugin，无虚拟显存      |
-| vGPU-device-plugin(virtual device memory)  | k8s + VGPU k8s-device-plugin，高负载，开启虚拟显存 |
+| Nvidia-device-plugin        |         k8s + nvidia 官方 k8s-device-plugin          |
+| vGPU-device-plugin        |      k8s + vGPU k8s-device-plugin，无虚拟显存      |
+| vGPU-device-plugin (virtual device memory)  | k8s + vGPU k8s-device-plugin，高负载，开启虚拟显存 |
 
-测试内容
+测试内容：
 
-| test id |     名称      |   类型    |          参数           |
+| Test ID |     名称      |   类型    |          参数           |
 | ------- | :-----------: | :-------: | :---------------------: |
 | 1.1     | Resnet-V2-50  | inference |  batch=50,size=346*346  |
 | 1.2     | Resnet-V2-50  | training  |  batch=20,size=346*346  |
@@ -30,21 +36,76 @@
 | 5.1     |     LSTM      | inference | batch=100,size=1024*300 |
 | 5.2     |     LSTM      | training  | batch=10,size=1024*300  |
 
-测试结果： ![img](../imgs/benchmark_inf.png)
+历史测试结果：
+
+![img](../imgs/benchmark_inf.png)
 
 ![img](../imgs/benchmark_train.png)
 
-测试步骤：
+---
+
+## 运行基准测试
+
+要在您的环境中测试 HAMi 的性能，请按照以下步骤操作：
+
+### 前置条件
+
+- HAMi 已安装并在 Kubernetes 集群中配置完成（参见[快速开始](../README_cn.md#快速开始)）
+- GPU 节点已标记 `gpu=on` 标签
+- Kubernetes 版本 >= 1.18
+- Docker 或 containerd 运行时，支持 NVIDIA
+
+### 构建基准测试镜像（可选）
+
+如果您想自己构建基准测试镜像：
+
+```bash
+cd benchmarks/ai-benchmark
+sh build.sh
+```
+
+### 运行基准测试任务
 
-1. 安装nvidia-device-plugin，并配置相应的参数
-2. 运行benchmark任务
+HAMi 提供了两个基准测试任务配置来比较性能：
 
+**1. 在 HAMi 上运行基准测试：**
+
+```bash
+cd benchmarks/deployments
+kubectl apply -f job-on-hami.yml
 ```
-$ kubectl apply -f benchmarks/ai-benchmark/ai-benchmark.yml
+
+这将部署一个使用 HAMi GPU 共享和显存隔离功能的任务（请求 50% 的 GPU 显存）。
+
+**2. 在 NVIDIA device plugin 上运行基准测试（用于对比）：**
+
+```bash
+kubectl apply -f job-on-nvidia-device-plugin.yml
 ```
 
-3. 通过kubctl logs 查看结果
+要安装官方 NVIDIA device plugin，请参考 [NVIDIA k8s-device-plugin 快速开始](https://github.com/NVIDIA/k8s-device-plugin?tab=readme-ov-file#quick-start)。
+
+### 查看结果
+
+任务完成后，查看基准测试结果：
 
+```bash
+# 检查任务状态
+kubectl get jobs
+
+# 查看 HAMi 基准测试结果
+kubectl logs job/ai-benchmark-on-hami
+
+# 查看 NVIDIA device plugin 基准测试结果
+kubectl logs job/ai-benchmark-on-official
 ```
-$ kubectl logs [pod id]
-```
+
+### 自定义基准测试
+
+您可以修改 `benchmarks/deployments/` 中的基准测试任务来测试不同的配置：
+
+- 调整 GPU 显存分配（例如：`nvidia.com/gpumem-percentage: 50`）
+- 测试不同的 GPU 数量
+- 比较启用和不启用 HAMi 显存隔离功能的性能差异
+
+更多详细信息，请参阅[基准测试 README](../benchmarks/README.md)。