Update async_executor.md

doc/fluid/design/async_executor/async_executor.md

@@ -28,7 +28,7 @@ def train_loop():
             print("accuracy %f" % val)
 ```
 ## Difference between async_executor and other executors
-async_executor is mainly designed for cpu training scenarios where data throughputs are high and the computation part of training is not intensive compared with GPU trained models such as resnet-50. Since data throughputs ability is very important in async_executor, we have to design very fast data IO modules to handle very large scale data reading. Another different key aspect is that memory is not a problem in cpu training scenarios given 128G or 256G RAW in modern server. 
+async_executor is mainly designed for cpu training scenarios where data throughputs are high and the computation part of training is not intensive compared with GPU trained models such as resnet-50. Since data throughputs ability is very important in async_executor, we have to design very fast data IO modules to handle very large scale data reading. Another different key aspect is that memory is not a problem in cpu training scenarios given 128G or 256G RAM in modern clusters. 
 
 executor and parallel_executor are designed for geneneral training cases in particular for gpu training. Executor is a single thread implementation for model training and it is mostly used for startup_program running currently. Another application scenario of executor is reinforcement learning where input data and main_program may change through training. Parallel_executor is mainly designed for synchronous training on high performance devices such as gpu. Operators are executed concurrently following topological orders on different graphs and model parameter gradients are synchrounized iteratively.