!311 Add README for new profiler components

Merge pull request !311 from wangyue/r0.5_profiler_doc

mindinsight/profiler/README.md

@@ -39,29 +39,117 @@ To enable profiling on MindSpore, the MindInsight Profiler apis should be added
 
 After training, we can open MindInsight UI to analyse the performance.
 
-## Operator Performance Analysis
+## Performance Analysis
+
+Users can access the Performance Profiler by selecting a specific training from the training list, and click the performance profiling link.
+
+![performance_overall.png](./images/performance_overall.png)
+
+Figure 1： Overall Performance
+
+Figure 1 displays the overall performance of the training, including the overall data of Step Trace、Operator Performance、MindData Performance and Timeline.
+Users can click the detail link to see the details of each components. Besides, MindInsight Profiler will try to analyse the performance data, the assistant on the left 
+will show performance tuning suggestions for this training.
+
+### Step Trace Analysis
+
+The Step Trace Component is used to show the general performance of the stages in the training. Step Trace will divide the training into several stages:
+Step Gap、Forward/Backward Propagation、 All Reduce and Parameter Update. It will show the execution time for each stage, and help to find the bottleneck
+stage quickly.
+
+![step_trace.png](./images/step_trace.png)
+
+Figure 2： Step Trace Analysis
+
+Figure 2 displays the Step Trace page. The Step Trace detail will show the start/finish time for each stage. By default, it shows the average time for all the steps. Users
+can also choose a specific step to see its step trace statistics. The graphs at the bottom of the page show how the execution time of Step Gap、Forward/Backward Propagation and
+Step Tail changes according to different steps, it will help to decide whether we can optimize the performance of some stages. 
+
+### Operator Performance Analysis
 
 The operator performance analysis component is used to display the execution time of the operators during MindSpore run.
  
  ![op_type_statistics.png](./images/op_type_statistics.PNG)
  
-Figure 17: Statistics for Operator Types
+Figure 3: Statistics for Operator Types
  
-Figure 17 displays the statistics for the operator types, including:
+Figure 3 displays the statistics for the operator types, including:
  
 - Choose pie or bar graph to show the proportion time occupied by each operator type. The time of one operator type is calculated by accumulating the execution time of operators belong to this type.   
 - Display top 20 operator types with longest execution time, show the proportion and execution time (ms) of each operator type.
 
 ![op_statistics.png](./images/op_statistics.PNG)
 
-Figure 18: Statistics for Operators
+Figure 4: Statistics for Operators
 
-Figure 18 displays the statistics table for the operators, including:
+Figure 4 displays the statistics table for the operators, including:
 
 - Choose All: Display statistics for the operators, including operator name、type、excution time、full scope time、information etc. The table will be sorted by execution time by default.
 - Choose Type: Display statistics for the operator types, including operator type name、execution time、execution frequency and proportion of total time. Users can click on each line, querying for all the operators belong to this type.
 - Search: There is a search box on the right, which can support fuzzy search for operators/operator types.
 
+### MindData Performance Analysis
+
+The MindData performance analysis component is used to analyse the execution of data input pipeline for the training. The data input pipeline can be divided into three stages:
+the data process pipeline、data transfer from host to device and data fetch on device. The component will analyse the performance of each stage for detail and display the results. 
+
+![minddata_profile.png](./images/minddata_profile.png)
+
+Figure 5： MindData Performance Analysis
+
+Figure 5 displays the page of MindData performance analysis component. It consists of two tabs: The step gap and the data process.
+
+The step gap page is used to analyse whether there is performance bottleneck in the three stages. We can get our conclusion from the data queue graphs:
+
+- The data queue size stands for the queue length when the training fetches data from the queue on the device. If the data queue size is 0, the training will wait until there is data in
+the queue; If the data queue size is above 0, the training can get data very quickly, and it means MindData is not the bottleneck for this training step.
+- The host queue size can be used to infer the speed of data process and data transfer. If the host queue size is 0, it means we need to speed up the data process stage.
+- If the host queue size keeps big and the data queue size keeps very small, the data transfer may be the bottleneck.    
+
+![data_op_profile.png](./images/data_op_profile.png)
+
+Figure 6： Data Process Pipeline Analysis
+
+Figure 6 displays the page of data process pipeline analysis. The data queues are used to exchange data between the MindData operators. The data size of the queues reflect the
+data consume speed of the operators, and can be used to infer the bottleneck operator. The queue usage percentage stands for the average value of data size in queue divide data queue maximum size, the higher
+the usage percentage, the more data that is accumulated in the queue. The graph at the bottom of the page shows the MindData pipeline operators with the data queues, the user can click one queue to see how
+the data size changes according to the time, and the operators connected to the queue. The data process pipeline can be analysed as follows:
+
+- When the input queue usage percentage of one operator is high, and the output queue usage percentage is low, the operator may be the bottleneck;
+- For the leftmost operator, if the usage percentage of the queues on the right are all low, the operator may be the bottleneck;
+- For the rightmost operator, if the usage percentage of the queues on th left are all high, the operator may be the bottleneck. 
+
+To optimize the perforamnce of MindData operators, there are some suggestions:
+
+- If the `Dataset` Operator is the bottleneck, try to increase the `num_parallel_workers`;
+- If a `GeneratorOp` type operator is the bottleneck, try to increase the `num_parallel_workers` and replace the operator to `MindRecordDataset`;
+- If a `MapOp` type operator is the bottleneck, try to increase the `num_parallel_workers`; If it is a python operator, try to optimize the training script;
+- If a `BatchOp` type operator is the bottleneck, try to adjust the size of `prefetch_size`. 
+
+### Timeline Analysis
+
+The Timeline component can display：
+
+- The operators (AICore/AICPU operators) are executed on which device;
+- The MindSpore stream split strategy for this neural network;
+- The time of tasks executed on the device.
+
+Users can get the most detailed information from the Timeline:
+
+- From high level, users can analyse whether the stream split strategy can be optimized and whether is step tail is too long;
+- From low level, users can analyse the execution time for all the operators, etc.
+
+![timeline.png](./images/timeline.png)
+
+Figure 7 Timeline Analysis
+
+The Timeline consists of the following parts:
+
+- *Device and Stream List*: It will show the stream list on each device. Each stream consists of a seires of tasks. One rectangle stands for one task, and the area stands for the execution time of the task;
+- *The Operator Information*: When we click one task, the corresponding operator of this task will be shown at the bottom. 
+
+W/A/S/D can be applied to zoom in and out of the timeline graph.
+
 ## Limitations
 
 The Profiler has the following limitations now: