diff --git a/paddle/fluid/operators/distributed_ops/lookup_remote_table.h b/paddle/fluid/operators/distributed_ops/lookup_remote_table.h
new file mode 100644
index 0000000000000000000000000000000000000000..5b066c8196108bb482da7d372bbe2e39541a38b7
--- /dev/null
+++ b/paddle/fluid/operators/distributed_ops/lookup_remote_table.h
@@ -0,0 +1,192 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <future>  // NOLINT
+#include <ostream>
+#include <vector>
+#include <set>
+#include <unordered_map>
+
+#include "paddle/fluid/framework/data_type.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/operators/detail/macros.h"
+#include "paddle/fluid/memory/memcpy.h"
+#include "paddle/fluid/operators/distributed_ops/send_recv_util.h"
+
+namespace paddle {
+namespace operators {
+namespace distributed {
+
+inline size_t GetSectionIndex(int64_t id, const std::vector<int64_t>& abs_sections) {
+  for (size_t i = 1; i < abs_sections.size(); ++i) {
+    if (row < abs_sections[i]) {
+      return i - 1;
+    }
+  }
+  return abs_sections.size() - 1;
+}
+
+inline std::vector<int64_t> ToAbsoluteSection(
+        const std::vector<int64_t>& height_sections) {
+  std::vector<int64_t> abs_sections;
+  abs_sections.resize(height_sections.size());
+  abs_sections[0] = 0;
+  for (size_t i = 1; i < height_sections.size(); ++i) {
+    abs_sections[i] = height_sections[i - 1] + abs_sections[i - 1];
+  }
+  return abs_sections;
+}
+
+inline std::vector<std::vector<int64_t>> SplitIds(
+        const std::string& id_name,
+        const std::vector<int64_t>& height_section,
+        framework::Scope* scope) {
+  auto& id_tensor = scope->Var(id_name)->Get<framework::LoDTensor>();
+  auto* id_data = id_tensor.data<int64_t>();
+  std::set<int64_t> all_ids;
+  for (size_t i = 0; i < id_tensor.numel(); ++i) {
+    all_ids.insert(id_data[i]);
+  }
+  auto abs_sections = ToAbsoluteSection(height_section);
+  std::vector<std::vector<int64_t>> splited_ids;
+  splited_ids.resize(height_section.size() + 1);
+  for (auto& id : all_ids) {
+    auto section_index = GetSectionIndex(id);
+    splited_ids[section_index].push_back(id - abs_sections[section_index]);
+  }
+}
+
+inline void SplitIdsIntoMultipleVarsBySection(
+        const std::string& id_name,
+        const std::vector<std::string>& in_var_names,
+        const std::vector<int64_t>& height_section,
+        const std::vector<std::vector<int64_t>>& splited_ids,
+        framework::Scope* scope) {
+  PADDLE_ENFORCE_EQ(in_var_names.size(), height_section.size() + 1, "");
+
+  auto place = platform::CPUPlace();
+
+  for (size_t i = 0; i < in_var_names.size(); ++i) {
+    auto* id_tensor = scope->Var(in_var_names[i])->GetMutable<framework::LoDTensor>();
+    auto& ids = splited_ids[i];
+    if (!ids.empty()) {
+      auto* id_tensor_data = id_tensor->mutable_data<int64_t>(framework::make_ddim({ids.size(), 1}), place);
+      memcpy(id_tensor_data, ids.data(), sizeof(int64_t) * ids.size());
+    }
+  }
+}
+
+inline void MergeMultipleVarsIntoOnBySection(
+        const std::string& id_name,
+        const std::string& out_name,
+        const std::vector<std::string>& out_var_names,
+        const std::vector<int64_t>& height_section,
+        const std::vector<std::vector<int64_t>>& splited_ids,
+        framework::Scope* scope) {
+  PADDLE_ENFORCE_EQ(in_var_names.size(), height_section.size() + 1, "");
+
+  auto cpu_place = platform::CPUPlace();
+
+  auto abs_sections = ToAbsoluteSection(height_section);
+  auto& id_tensor = scope->Var(id_name)->Get<framework::LoDTensor>();
+  auto* id_data = id_tensor.data<int64_t>();
+  std::unordered_map<int64_t, std::vector<size_t>> id_to_offset;
+  for (size_t i = 0; i < id_tensor.numel(); ++i) {
+    id_to_offset[id_data[i]].push_back(i);
+  }
+
+  auto& out_tensor = scope->Var(out_name)->Get<framework::LoDTensor>();
+  auto* out_tensor_data = out_tensor.mutable_data<float>();
+
+  for (size_t section_idx = 0; section_idx < out_var_names.size(); ++section_idx) {
+    auto& ids_in_this_section = splited_ids[section_idx];
+    auto& prefetch_out_var = scope->Var(out_var_names[section_idx])->Get<framework::LoDTensor>();
+    const auto* out_var_data = prefetch_out_var.mutable_data<float>();
+    auto& dims = prefetch_out_var.dims();
+
+    PADDLE_ENFORCE_EQ(dims.size(), 2, "");
+    PADDLE_ENFORCE_EQ(ids_in_this_section.size(), dims[0]);
+
+    auto row_numel = dims[1];
+
+    for (size_t i = 0; i < dims[0]; ++i) {
+      auto id = ids_in_this_section[i];
+      auto origin_id = id + abs_sections[section_idx];
+      auto& offsets = id_to_offset[origin_id];
+      for (auto& offset : offsets) {
+        // should support GPU tensor
+        memory::Copy(cpu_place, out_tensor_data + offset * row_numel,
+                     cpu_place, out_var_data + i * grad_row_numel,
+                     sizeof(T) * grad_row_numel);
+      }
+    }
+  }
+}
+
+inline void prefetch(
+        const std::string& table_name,
+        const std::string& id_name,
+        const std::string& out_name,
+        const std::vector<std::string>& epmap,
+        const std::vector<int64_t>& height_section,
+        const framework::Scope& scope,
+        const platform::Place& place) const {
+
+  auto local_scope = scope.NewScope();
+
+  platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
+  auto& ctx = *pool.Get(place);
+
+  distributed::RPCClient* rpc_client =
+          distributed::RPCClient::GetInstance<RPCCLIENT_T>(
+                  Attr<int>("trainer_id"));
+
+  std::vector<std::string> in_var_names;
+  std::vector<std::string> out_var_names;
+  for (size_t i = 0; i < epmap.size(); ++i) {
+    in_var_names.push_back(id_name + "@" + epmap[i]);
+    out_var_names.push_back(out_name + "@" + epmap[i]);
+  }
+
+  auto splited_ids = SplitIds(id_name, height_section, local_scope);
+  SplitIdsIntoMultipleVarsBySection(id_name, in_var_names, height_section, splited_ids, local_scope);
+
+  // create output var in local scope
+  for (auto& name : out_var_names) {
+    local_scope.Var(name)->GetMutable<framework::LoDTensor>();
+  }
+
+  std::vector<distributed::VarHandlePtr> rets;
+  for (size_t i = 0; i < ins.size(); i++) {
+    if (NeedSend(local_scope, ins[i])) {
+      VLOG(30) << "sending " << ins[i] << " to " << epmap[i] << " to get "
+      << outs[i] << " back";
+      rets.push_back(rpc_client->AsyncPrefetchVar(epmap[i], ctx, local_scope,
+                                                  in_var_names[i], out_var_names[i]));
+    } else {
+      VLOG(30) << "don't send no-initialied variable: " << out_var_names[i];
+    }
+  }
+  for (size_t i = 0; i < rets.size(); i++) {
+    PADDLE_ENFORCE(rets[i]->Wait(), "internal error in RPCClient");
+  }
+
+  MergeMultipleVarsIntoOnBySection(id_name, out_name, out_var_names, height_section, plited_ids, scope)
+
+  scope.DeleteScope(local_scope);
+}
+
+}  // namespace distributed
+}  // namespace operators
+}  // namespace paddle