diff --git a/python/paddle/v2/fluid/distribute_transpiler.py b/python/paddle/v2/fluid/distribute_transpiler.py
index 76e8734f1371d0874d8b10f597a9bb6989de9297..009f079e839ae64572cb80c82cd8a44bfa89a54a 100644
--- a/python/paddle/v2/fluid/distribute_transpiler.py
+++ b/python/paddle/v2/fluid/distribute_transpiler.py
@@ -56,8 +56,6 @@ def split_dense_variable(var_list,
                 (block_id) * block_size))
             block = VarBlock(var.name, block_id, curr_block_size)
             blocks.append(str(block))
-        print("$$ splited var: ", var.name, var.shape, split_count, len(blocks),
-              block_size)
     return blocks
 
 
@@ -126,7 +124,7 @@ class DistributeTranspiler:
         # let send_op know which endpoint to send which var, eplist is of the same
         # order of send_inputs.
         eplist = split_method(send_inputs, pserver_endpoints)
-        # create mapping of endpoint -> var to create pserver side program
+        # create mapping of endpoint -> splited var to create pserver side program
         self.param_grad_ep_mapping = dict()
         for i, ep in enumerate(eplist):
             param = send_outputs[i]
@@ -142,7 +140,6 @@ class DistributeTranspiler:
             outputs={"Out": send_outputs},
             attrs={"endpoints": pserver_endpoints,
                    "epmap": eplist})
-
         # step4
         for varname, splited_var in param_var_mapping.iteritems():
             if len(splited_var) <= 1:
@@ -187,21 +184,6 @@ class DistributeTranspiler:
                 var_mapping[varname].append(var)
         return var_mapping
 
-    def _clone_param(self, block, v):
-        assert isinstance(v, Parameter)
-        new_p = Parameter(
-            block=block,
-            shape=v.shape,
-            dtype=v.dtype,
-            type=v.type,
-            lod_level=v.lod_level,
-            stop_gradient=v.stop_gradient,
-            trainable=v.trainable,
-            optimize_attr=v.optimize_attr,
-            regularizer=v.regularizer,
-            name=v.name)
-        block.vars[new_p.name] = new_p
-
     def _clone_var(self, block, var):
         assert isinstance(var, Variable)
         return block.create_var(
@@ -210,7 +192,9 @@ class DistributeTranspiler:
             dtype=var.dtype,
             type=var.type,
             lod_level=var.lod_level,
-            persistable=var.persistable)
+            # HACK: let all param in pserver persistable so child
+            # program in recv can get them
+            persistable=True)
 
     def _append_split_op(self, program, gradblocks):
         var_mapping = self._create_vars_from_blocklist(program, gradblocks)
@@ -318,9 +302,10 @@ class DistributeTranspiler:
                     return
                 tmpvar = program.global_block().create_var(
                     name=param_block.name,
-                    persistable=param_block.persistable,
+                    persistable=True,
                     dtype=param_block.dtype,
                     shape=param_block.shape)
+
                 new_inputs[key] = tmpvar
 
         for key, var in opt_op.inputs.iteritems():
@@ -330,7 +315,6 @@ class DistributeTranspiler:
             param_shape = new_inputs["Param"].shape
             new_shape = self._get_optimizer_input_shape(opt_op.type, key,
                                                         var.shape, param_shape)
-            print("var, new shape", key, var.name, new_shape)
             tmpvar = program.global_block().create_var(
                 name=var.name,
                 persistable=var.persistable,
@@ -338,7 +322,8 @@ class DistributeTranspiler:
                 shape=new_shape)
             new_inputs[key] = tmpvar
 
-        # FIXME: change outputs ParamOut
+        # change outputs ParamOut variable
+        opt_op.outputs["ParamOut"] = new_inputs["Param"]
         program.global_block().append_op(
             type=opt_op.type,
             inputs=new_inputs,
@@ -380,6 +365,7 @@ class DistributeTranspiler:
             else:
                 self._append_pserver_non_opt_ops(optimize_sub_program, opt_op)
 
+        print("####", optimize_sub_program)
         pserver_program.global_block().append_op(
             type="recv",
             inputs={"RX": self.param_grad_ep_mapping[endpoint]["grads"]
@@ -400,3 +386,53 @@ class DistributeTranspiler:
             })
         pserver_program.sync_with_cpp()
         return pserver_program
+
+    def get_startup_program(self, endpoint):
+        """
+        Get startup program for current parameter server.
+        Modify operator input variables if there are variables that
+        was splited to several blocks.
+        """
+        s_prog = Program()
+        orig_s_prog = framework.default_startup_program()
+        params = self.param_grad_ep_mapping[endpoint]["params"]
+
+        def _get_splited_name_and_shape(varname):
+            for idx, splited_param in enumerate(params):
+                pname = splited_param.name
+                if pname.startswith(varname) and varname != pname:
+                    return pname, splited_param.shape
+            return "", []
+
+        # 1. create vars
+        created_var_map = dict()
+        for var in params:
+            print("%%%% append var", var.name, var.shape)
+            tmpvar = s_prog.global_block().create_var(
+                name=var.name,
+                persistable=True,
+                dtype=var.dtype,
+                shape=var.shape)
+            created_var_map[var.name] = tmpvar
+
+        # 2. rename op outputs
+        for op in orig_s_prog.global_block().ops:
+            new_outputs = dict()
+            for key, var in op.outputs.iteritems():
+                newname, _ = _get_splited_name_and_shape(var.name)
+                if newname:
+                    new_outputs[key] = created_var_map[newname]
+                else:
+                    new_outputs[key] = var
+            # do not append startup op if var is not on this pserver
+            var_on_pserver = False
+            for _, var in new_outputs.iteritems():
+                if var.name in created_var_map:
+                    var_on_pserver = True
+            if var_on_pserver:
+                s_prog.global_block().append_op(
+                    type=op.type,
+                    inputs=op.inputs,
+                    outputs=new_outputs,
+                    attrs=op.attrs)
+        return s_prog