update by comment

doc/fluid/design/dist_train/async_update.md

@@ -4,41 +4,46 @@
 
 For the typical synchronous distributed training, some significant steps are as follows:
 
-1. A Trainer will compute the gradients and SEND them to the Parameter
-Server(PServer) nodes.
-1. After the PServer node received gradients came from all the Trainers, it would apply the gradient to the respective variables, and using an optimize algorithms(SGD,
- Momentment...) to update the parameters.
-1. The Trainer would wait for the PServers finished the optimize stage, and GET the parameters from PServer, so all the Trainers would get the same parameters.
+1. A Trainer will compute the gradients and SEND them to the Parameter Server(PServer) nodes.
+1. After the PServer node received gradients came from all the Trainers,
+it would apply the gradient to the respective variables, and using an optimize algorithms(SGD,
+Momentment...) to update the parameters.
+1. The Trainer would wait for the PServers finished the optimize stage, and GET the parameters from PServer,
+so all the Trainers would get the same parameters.
 
 In the synchronously distributed training, there should be a `Barrier` to synchronise the
-parameters after the optimizing stage. The performance of a distributed training job 
-depends on the lowest node, if there were hundreds or thousand training nodes in a Job,
-the performance of synchronously distributed training might be very slow because of 
+parameters after the optimizing stage. The performance of a distributed training job would
+depend on the slowest node if there were hundreds or thousands of training nodes in a
+Job, the performance of synchronously distributed training might be very poor because of
 the slow node. So this design doc would introduce an approach to implement
 *asynchronously* distributed training in PaddlePaddle Fluid.
 
 ## Design
 
-<img src="./src/async_update.png" width="450"/>
+<img src="./src/async_update.png" width="600"/>
 
 As the figure above, we describe a global view of asynchronously update process and use
 the parameter `w1` as an example to introduce the steps:
 1. For each gradient variables, they may distribute on different GPU card and aggregate
 them while they are all calculated.
 1. Split the gradient variable into multiple blocks according to the number of PServer
-instances and sent them.
-1. PServer would run an `Optimize Block` to use a specified optimize algorithm to update
-the specified parameter, such as `w1`.
-1. The trainer will fetch the latest parameter after PServer finished the optimize stage.
+instances and then sent them.
+1. PServer would run an `Optimize Block` using a specified optimize algorithm to update
+the specified parameter.
+1. The trainer will fetch the parameter before running forward Op depends on the specified
+parameter.
 1. Broadcast the received variable into multiple GPU cards and continue to run the next
 mini-batch.
 
 ### Trainer
 
-- We need a new Operator named `RemoteOptimize` to send gradients to multiple PServer 
-instances and fetch the latest parameter.
+- For the multiple devices distributed training, we need to aggregate the gradient
+variables which placed on different devices firstly, and then schedule a `SendVars` Operator to
+send the gradient variables to the multiple PServer instances.
+- Schedule `FetchVars` operator to fetch the latest parameter from PServer before running
+the forward ops.
 - There could be a large number of gradient variables to be sent, so we need to use another
-thread pool(IO Threadpool) which number of the schedulable threads is larger than the
+thread pool(IO Threadpool) which a number of the schedulable threads is larger than the
 computing thread pool to avoid competitive the thread resources with computing.
 
 ### Parameter Server