diff --git a/python/paddle/fluid/data_feeder.py b/python/paddle/fluid/data_feeder.py
index ac02401c79b787716b2e5f43e0d1c5686cf2bd13..0051b698471b40bffc12921f86dcde642714e07d 100644
--- a/python/paddle/fluid/data_feeder.py
+++ b/python/paddle/fluid/data_feeder.py
@@ -16,6 +16,7 @@ from __future__ import print_function
 import core
 import numpy
 import six.moves as six
+import multiprocessing
 
 from framework import Variable, default_main_program
 
@@ -116,3 +117,60 @@ class DataFeeder(object):
         for each_name, each_converter in six.zip(self.feed_names, converter):
             ret_dict[each_name] = each_converter.done()
         return ret_dict
+
+    def feed_parallel(self, iterable, num_places=None):
+        if isinstance(self.place, core.CUDAPlace):
+            places = [
+                core.CUDAPlace(i)
+                for i in six.xrange(self._get_number_of_places_(num_places))
+            ]
+        else:
+            places = [
+                core.CPUPlace()
+                for _ in six.xrange(self._get_number_of_places_(num_places))
+            ]
+
+        if len(iterable) != len(places):
+            raise ValueError("feed_parallel takes multiple mini-batches. Each "
+                             "mini-batch will be feed on each device. The "
+                             "number of devices and number of mini-batches "
+                             "must be same.")
+
+        place = self.place
+        for p, batch in six.zip(places, iterable):
+            self.place = p
+            yield self.feed(batch)
+        self.place = place
+
+    def _get_number_of_places_(self, num_places):
+        if num_places is not None:
+            return int(num_places)
+        elif isinstance(self.place, core.CUDAPlace):
+            return core.get_cuda_device_count()
+        else:
+            return multiprocessing.cpu_count()
+
+    def decorate_reader(self,
+                        reader,
+                        multi_devices,
+                        num_places=None,
+                        drop_last=True):
+        def __reader_creator__():
+            if not multi_devices:
+                for item in reader():
+                    yield self.feed(item)
+            else:
+                num = self._get_number_of_places_(num_places)
+                item = []
+                for batch in reader():
+                    item.append(batch)
+                    if len(item) == num:
+                        yield list(self.feed_parallel(item, num))
+                        item = []
+                if not drop_last and len(item) != 0:
+                    raise ValueError(
+                        "The data batch which cannot fit for devices will be "
+                        "dropped is not implementation. Other strategies are "
+                        "not implemented")
+
+        return __reader_creator__
diff --git a/python/paddle/fluid/tests/unittests/test_parallel_executor.py b/python/paddle/fluid/tests/unittests/test_parallel_executor.py
index 4eb25a6e00b7564ac17db568ec78c1c84933af43..6db3a13718d0b258b297261f9df6457ad2035e3e 100644
--- a/python/paddle/fluid/tests/unittests/test_parallel_executor.py
+++ b/python/paddle/fluid/tests/unittests/test_parallel_executor.py
@@ -796,5 +796,42 @@ class TestFetchOp(unittest.TestCase):
         self.parallel_exe(train_inputs, seed=1)
 
 
+class TestFeedParallel(unittest.TestCase):
+    def test_main(self):
+        main = fluid.Program()
+        startup = fluid.Program()
+        startup.random_seed = 1
+        with fluid.scope_guard(fluid.core.Scope()):
+            with fluid.program_guard(main, startup):
+                data = fluid.layers.data(
+                    name='image', shape=[3, 224, 224], dtype='float32')
+                label = fluid.layers.data(
+                    name='label', shape=[1], dtype='int64')
+                out = Lenet(data, class_dim=102)
+                loss = fluid.layers.cross_entropy(input=out, label=label)
+                loss = fluid.layers.mean(loss)
+                opt = fluid.optimizer.Momentum(
+                    learning_rate=0.1,
+                    momentum=0.9,
+                    regularization=fluid.regularizer.L2Decay(1e-4))
+
+                opt.minimize(loss)
+        place = fluid.CUDAPlace(0)
+        feeder = fluid.DataFeeder(place=place, feed_list=[data, label])
+        reader = feeder.decorate_reader(
+            paddle.batch(
+                flowers.train(), batch_size=16), multi_devices=True)
+        exe = fluid.Executor(place)
+        exe.run(startup)
+        pe = fluid.ParallelExecutor(
+            use_cuda=True, loss_name=loss.name, main_program=main)
+
+        for batch_id, data in enumerate(reader()):
+            loss_np = np.array(pe.run(feed=data, fetch_list=[loss.name])[0])
+            print batch_id, loss_np
+            if batch_id == 2:
+                break
+
+
 if __name__ == '__main__':
     unittest.main()