diff --git a/tutorials/training/source_en/advanced_use/performance_profiling.md b/tutorials/training/source_en/advanced_use/performance_profiling.md
index 9b6a0473c21ddd0abd10f1aedf575e374eb00c50..db1df910bd605255cf23604e217f1e4933031c2b 100644
--- a/tutorials/training/source_en/advanced_use/performance_profiling.md
+++ b/tutorials/training/source_en/advanced_use/performance_profiling.md
@@ -95,8 +95,8 @@ Figure 2 displays the Step Trace page. The Step Trace detail will show the start
 In order to divide the stages, the Step Trace Component need to figure out the forward propagation start operator and the backward propagation end operator. MindSpore will automatically figure out the two operators to reduce the profiler configuration work. The first operator after `get_next` will be selected as the forward start operator and the operator before the last all reduce will be selected as the backward end operator.
 **However, Profiler do not guarantee that the automatically selected operators will meet the user's expectation in all cases.** Users can set the two operators manually as follows:  
 
-- Set environment variable `FP_POINT` to configure the forward start operator, for example, `export FP_POINT=fp32_vars/conv2d/BatchNorm`.
-- Set environment variable `BP_POINT` to configure the backward end operator, for example, `export BP_POINT=loss_scale/gradients/AddN_70`.
+- Set environment variable `PROFILING_FP_START` to configure the forward start operator, for example, `export PROFILING_FP_START=fp32_vars/conv2d/BatchNorm`.
+- Set environment variable `PROFILING_BP_END` to configure the backward end operator, for example, `export PROFILING_BP_END=loss_scale/gradients/AddN_70`.
 
 #### Operator Performance Analysis
 
diff --git a/tutorials/training/source_en/advanced_use/performance_profiling_gpu.md b/tutorials/training/source_en/advanced_use/performance_profiling_gpu.md
index 3ed1296399b8e00857eb1314175390229762fbc5..05ec588d77516a39c77a1753dcad9255f3b11255 100644
--- a/tutorials/training/source_en/advanced_use/performance_profiling_gpu.md
+++ b/tutorials/training/source_en/advanced_use/performance_profiling_gpu.md
@@ -130,6 +130,14 @@ The usage is almost the same as that in Ascend. The difference is GPU Timeline d
 >
 > <https://www.mindspore.cn/tutorial/training/en/master/advanced_use/performance_profiling.html#timeline-analysis>
 
+#### Timeline Analysis
+
+The usage is almost the same as that in Ascend.
+
+> The usage is described as follows:
+>
+> <https://www.mindspore.cn/tutorial/training/en/master/advanced_use/performance_profiling.html#step-trace-analysis>
+
 #### Data Preparation Analysis
 
 The usage is almost the same as that in Ascend.
diff --git a/tutorials/training/source_zh_cn/advanced_use/performance_profiling.md b/tutorials/training/source_zh_cn/advanced_use/performance_profiling.md
index b0ef4c194a5c64643ca827c807d57fa935000613..669965e09caa3a670c62bd080953ba570bb7bc59 100644
--- a/tutorials/training/source_zh_cn/advanced_use/performance_profiling.md
+++ b/tutorials/training/source_zh_cn/advanced_use/performance_profiling.md
@@ -99,8 +99,8 @@ profiler.analyse()
 迭代轨迹在做阶段划分时，需要识别前向计算开始的算子和反向计算结束的算子。为了降低用户使用Profiler的门槛，MindSpore会对这两个算子做自动识别，方法为：  
 前向计算开始的算子指定为`get_next`算子之后连接的第一个算子，反向计算结束的算子指定为最后一次all reduce之前连接的算子。**Profiler不保证在所有情况下自动识别的结果和用户的预期一致，用户可以根据网络的特点自行调整**，调整方法如下：  
 
-- 设置`FP_POINT`环境变量指定前向计算开始的算子，如`export FP_POINT=fp32_vars/conv2d/BatchNorm`。
-- 设置`BP_POINT`环境变量指定反向计算结束的算子，如`export BP_POINT=loss_scale/gradients/AddN_70`。
+- 设置`PROFILING_FP_START`环境变量指定前向计算开始的算子，如`export PROFILING_FP_START=fp32_vars/conv2d/BatchNorm`。
+- 设置`PROFILING_BP_END`环境变量指定反向计算结束的算子，如`export PROFILING_BP_END=loss_scale/gradients/AddN_70`。
 
 #### 算子性能分析
 
diff --git a/tutorials/training/source_zh_cn/advanced_use/performance_profiling_gpu.md b/tutorials/training/source_zh_cn/advanced_use/performance_profiling_gpu.md
index 1be993d8a82b84f9c2dc8e979d3076960052f416..4c6a35e0051e611dcf52c5253649d1299b254106 100644
--- a/tutorials/training/source_zh_cn/advanced_use/performance_profiling_gpu.md
+++ b/tutorials/training/source_zh_cn/advanced_use/performance_profiling_gpu.md
@@ -132,6 +132,14 @@ GPU场景下，Timeline分析的使用方法和Ascend场景相同，不同之处
 >
 > <https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/performance_profiling.html#timeline>
 
+#### 迭代轨迹分析
+
+GPU场景下，迭代轨迹分析的使用方法和Ascend场景相同，使用方法参考：
+
+> 与Ascend使用方式一致，可以参考：
+>
+> <https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/performance_profiling.html#id5>
+
 #### 数据准备性能分析
 
 GPU场景下，数据准备性能分析的使用方法和Ascend场景相同，使用方法参考：