Remove out of date design

doc/design/parallel_executor.md

@@ -1,74 +0,0 @@
-# ParallelExecutor Design Doc
-
-## Introduction
-
-We introduce `ParallelExecutor` to run multi-GPU training in PaddlePaddle Fluid. It supports
-1. keeping a copy of the parameters on each GPU
-1. allreduce on a separate stream allowing computation and communication overlap
-
-An example of switching single GPU training to multiple GPUs:
-```python
-cost = your_neural_network()
-opt = fluid.optimizer.SGDOptimizer()
-opt.minimize(avg_cost)
-
-# change Executor -> ParallelExecutor
-exe = fluid.ParallelExecutor(gpu_list=[0, 1])
-
-for iter in xranges(iter_num):
-    exe.run()
-```
-
-## Design
-
-In the constructor, a list of parameter, whose gradients need to be allreduced, is given.
-
-During the runtime, `ParallelExecutor` starts `#gpu` threads to run each `Executor`. For every
-operator run on each GPU, it will automatically sync with different streams when necessary.
-
-```c++
-// if op's input is params' grad:
-    // sync with allreduce stream
-    // e.g. sgd should wait for allreduce to be finished
-CallBack->BeforeOp(op);
-
-op->Run(*local_scope, place_);
-
-// if op's output is params' grad:
-//     sync with computation stream
-//     e.g. allreduce shoudl wait for fc_grad to be finished.
-CallBack->AfterOp(op);
-```
-
-And the `Callback` object can be implemented as the following
-
-```c++
-struct AllReduceCallBack {
-  void BeforeOp(framework::OperatorBase* op);
-  void AfterOp(framework::OperatorBase* op);
-
-  std::unordered_set<std::string> reduced_param_grad_names;
-  std::unordered_set<std::string> param_grad_names_;
-
-  platform::DeviceContext* computation_dev_ctx;    // computation device context
-  platform::DeviceContext* communication_dev_ctx;  // communication device context
-
-  framework::Scope* scope;
-  platform::NCCL::Communicator* nccl_com;
-};
-
-AllReduceCallBack::BeforeOp(framework::OperatorBase* op) {
-  if (op->Input() in reduced_param_grad_names) {
-    communication_dev_ctx->Wait();
-    reduced_param_grad_names.erase(op->Input())
-  }
-}
-
-AllReduceCallBack::AfterOp(framework::OperatorBase* op) {
-  if (op->Output() in param_grad_names) {
-    computation_dev_ctx->Wait();
-    reduced_param_grad_names.insert(op->Output());
-    ncclAllreduce(scope, op->Output(), communication_dev_ctx);
-  }
-}
-```