fix doc

doc/PERFORMANCE_OPTIM.md

-# Performance optimization
+# Performance Optimization
+
+([简体中文](./PERFORMANCE_OPTIM_CN.md)|English)
 
 Due to different model structures, different prediction services consume different computing resources when performing predictions. For online prediction services, models that require less computing resources will have a higher proportion of communication time cost, which is called communication-intensive service. Models that require more computing resources have a higher time cost for inference calculations, which is called computationa-intensive services.
 
-For a prediction service, the easiest way to determine what type it is is to look at the time ratio. Paddle Serving provides [Timeline tool] (../python/examples/util/README_CN.md), which can intuitively display the time spent in each stage of the prediction service.
+For a prediction service, the easiest way to determine what type it is is to look at the time ratio. Paddle Serving provides [Timeline tool](../python/examples/util/README_CN.md), which can intuitively display the time spent in each stage of the prediction service.
 
 For communication-intensive prediction services, requests can be aggregated, and within a limit that can tolerate delay, multiple prediction requests can be combined into a batch for prediction.
 

doc/PERFORMANCE_OPTIM_CN.md

 # 性能优化
 
+(简体中文|[English](./PERFORMANCE_OPTIM.md))
+
 由于模型结构的不同，在执行预测时不同的预测服务对计算资源的消耗也不相同。对于在线的预测服务来说，对计算资源要求较少的模型，通信的时间成本占比就会较高，称为通信密集型服务，对计算资源要求较多的模型，推理计算的时间成本较高，称为计算密集型服务。对于这两种服务类型，可以根据实际需求采取不同的方式进行优化
 
 对于一个预测服务来说，想要判断属于哪种类型，最简单的方法就是看时间占比，Paddle Serving提供了[Timeline工具](../python/examples/util/README_CN.md)，可以直观的展现预测服务中各阶段的耗时。