Created by: helinwang

1. Single node multiple CPU threads
   1. Executor support multiple threads: @helinwang propose to drive this.
   2. Transpiling: convert user's input ProgramDesc to a ExecutionPlan that supports CPU-only sync-SGD data parallelism: @Yancey1989 .
2. Single node multiple GPUs
   1. Transpiling: convert user's input ProgramDesc to a ExecutionPlan that supports GPU sync-SGD data parallelism.
3. Multiple nodes
   1. operators for feeding data
   2. Transpiling: convert user's input ProgramDesc to a ExecutionPlan that runs on multiple nodes.
   3. Send / Recv OP
   4. ExecutionPlan partition: partition the single ExecutionPlan to multiple ExecutionPlans, each partitioned ExecutionPlan runs on a node. Send / Recv OP is added between edges that cross nodes.
4. Fault tolerant: single node failure stops the training job and causes a job restart.
   1. every executor should save state automatically and loads state upon restart.
5. Elastic ML: number of nodes can change without interrupting the training (training job will not stop).

Please comment if you have question or suggestions, thanks!