diff --git a/mmrazor/core/searcher/evolution_search.py b/mmrazor/core/searcher/evolution_search.py
index 43cfe558a..a4fdee41c 100644
--- a/mmrazor/core/searcher/evolution_search.py
+++ b/mmrazor/core/searcher/evolution_search.py
@@ -135,14 +135,11 @@ def search(self):
 
                     if self.check_constraints():
                         self.candidate_pool.append(candidate)
-
-                broadcast_candidate_pool = self.candidate_pool
             else:
-                broadcast_candidate_pool = [None] * self.candidate_pool_size
-            broadcast_candidate_pool = broadcast_object_list(
-                broadcast_candidate_pool)
+                self.candidate_pool = [None] * self.candidate_pool_size
+            broadcast_object_list(self.candidate_pool)
 
-            for i, candidate in enumerate(broadcast_candidate_pool):
+            for i, candidate in enumerate(self.candidate_pool):
                 self.algorithm.mutator.set_subnet(candidate)
                 outputs = self.test_fn(self.algorithm_for_test,
                                        self.dataloader)
@@ -213,7 +210,7 @@ def search(self):
                 self.logger.info(
                     f'Epoch:[{epoch + 1}/{self.max_epoch}], top1_score: '
                     f'{list(self.top_k_candidates_with_score.keys())[0]}')
-            self.candidate_pool = broadcast_object_list(self.candidate_pool)
+            broadcast_object_list(self.candidate_pool)
 
         if rank == 0:
             final_subnet_dict = list(
diff --git a/mmrazor/core/searcher/greedy_search.py b/mmrazor/core/searcher/greedy_search.py
index 2d1410389..215d0fffe 100644
--- a/mmrazor/core/searcher/greedy_search.py
+++ b/mmrazor/core/searcher/greedy_search.py
@@ -146,7 +146,7 @@ def search(self):
 
                     # Broadcasts scores in broadcast_scores to the whole
                     # group.
-                    broadcast_scores = broadcast_object_list(broadcast_scores)
+                    broadcast_object_list(broadcast_scores)
                     score = broadcast_scores[0]
                     self.logger.info(
                         f'Slimming group {name}, {self.score_key}: {score}')
diff --git a/mmrazor/core/utils/__init__.py b/mmrazor/core/utils/__init__.py
index 415b5418b..9267327cc 100644
--- a/mmrazor/core/utils/__init__.py
+++ b/mmrazor/core/utils/__init__.py
@@ -1,5 +1,9 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .broadcast import broadcast_object_list
 from .lr import set_lr
+from .utils import get_backend, get_default_group, get_rank, get_world_size
 
-__all__ = ['broadcast_object_list', 'set_lr']
+__all__ = [
+    'broadcast_object_list', 'set_lr', 'get_world_size', 'get_rank',
+    'get_backend', 'get_default_group'
+]
diff --git a/mmrazor/core/utils/broadcast.py b/mmrazor/core/utils/broadcast.py
index 6326311c0..41c2998b4 100644
--- a/mmrazor/core/utils/broadcast.py
+++ b/mmrazor/core/utils/broadcast.py
@@ -1,49 +1,155 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import shutil
-import tempfile
+import pickle
+import warnings
+from typing import Any, List, Optional, Tuple
 
-import mmcv.fileio
 import torch
-import torch.distributed as dist
-from mmcv.runner import get_dist_info
+from mmcv.utils import TORCH_VERSION, digit_version
+from torch import Tensor
+from torch import distributed as dist
 
+from .utils import get_backend, get_default_group, get_rank, get_world_size
 
-def broadcast_object_list(object_list, src=0):
-    """Broadcasts picklable objects in ``object_list`` to the whole group.
 
-    Note that all objects in ``object_list`` must be picklable in order to be
-    broadcasted.
+def _object_to_tensor(obj: Any) -> Tuple[Tensor, Tensor]:
+    """Serialize picklable python object to tensor."""
+    byte_storage = torch.ByteStorage.from_buffer(pickle.dumps(obj))
+    # Do not replace `torch.ByteTensor` or `torch.LongTensor` with torch.tensor
+    # and specifying dtype. Otherwise, it will cause 100X slowdown.
+    # See: https://github.com/pytorch/pytorch/issues/65696
+    byte_tensor = torch.ByteTensor(byte_storage)
+    local_size = torch.LongTensor([byte_tensor.numel()])
+    return byte_tensor, local_size
 
-    Args:
-        object_list (List[Any]): List of input objects to broadcast.
-            Each object must be picklable. Only objects on the src rank will be
-            broadcast, but each rank must provide lists of equal sizes.
-        src (int): Source rank from which to broadcast ``object_list``.
+
+def _tensor_to_object(tensor: Tensor, tensor_size: int) -> Any:
+    """Deserialize tensor to picklable python object."""
+    buf = tensor.cpu().numpy().tobytes()[:tensor_size]
+    return pickle.loads(buf)
+
+
+def _broadcast_object_list(object_list: List[Any],
+                           src: int = 0,
+                           group: Optional[dist.ProcessGroup] = None) -> None:
+    """Broadcast picklable objects in ``object_list`` to the whole group.
+
+    Similar to :func:`broadcast`, but Python objects can be passed in. Note
+    that all objects in ``object_list`` must be picklable in order to be
+    broadcasted.
     """
-    my_rank, _ = get_dist_info()
+    if dist.distributed_c10d._rank_not_in_group(group):
+        return
 
-    MAX_LEN = 512
-    # 32 is whitespace
-    dir_tensor = torch.full((MAX_LEN, ), 32, dtype=torch.uint8, device='cuda')
-    object_list_return = list()
+    my_rank = get_rank()
+    # Serialize object_list elements to tensors on src rank.
     if my_rank == src:
-        mmcv.mkdir_or_exist('.dist_broadcast')
-        tmpdir = tempfile.mkdtemp(dir='.dist_broadcast')
-        mmcv.dump(object_list, osp.join(tmpdir, 'object_list.pkl'))
-        tmpdir = torch.tensor(
-            bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
-        dir_tensor[:len(tmpdir)] = tmpdir
+        tensor_list, size_list = zip(
+            *[_object_to_tensor(obj) for obj in object_list])
+        object_sizes_tensor = torch.cat(size_list)
+    else:
+        object_sizes_tensor = torch.empty(len(object_list), dtype=torch.long)
 
-    dist.broadcast(dir_tensor, src)
-    tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
+    # Current device selection.
+    # To preserve backwards compatibility, ``device`` is ``None`` by default.
+    # in which case we run current logic of device selection, i.e.
+    # ``current_device`` is CUDA if backend is NCCL otherwise CPU device. In
+    # the case it is not ``None`` we move the size and object tensors to be
+    # broadcasted to this device.
+    group_backend = get_backend(group)
+    is_nccl_backend = group_backend == dist.Backend.NCCL
+    current_device = torch.device('cpu')
+    if is_nccl_backend:
+        # See note about using torch.cuda.current_device() here in
+        # docstring. We cannot simply use my_rank since rank == device is
+        # not necessarily true.
+        current_device = torch.device('cuda', torch.cuda.current_device())
+        object_sizes_tensor = object_sizes_tensor.to(current_device)
 
-    if my_rank != src:
-        object_list_return = mmcv.load(osp.join(tmpdir, 'object_list.pkl'))
+    # Broadcast object sizes
+    dist.broadcast(object_sizes_tensor, src=src, group=group)
 
-    dist.barrier()
+    # Concatenate and broadcast serialized object tensors
     if my_rank == src:
-        shutil.rmtree(tmpdir)
-        object_list_return = object_list
+        object_tensor = torch.cat(tensor_list)
+    else:
+        object_tensor = torch.empty(
+            torch.sum(object_sizes_tensor).int().item(),
+            dtype=torch.uint8,
+        )
+
+    if is_nccl_backend:
+        object_tensor = object_tensor.to(current_device)
+    dist.broadcast(object_tensor, src=src, group=group)
+    # Deserialize objects using their stored sizes.
+    offset = 0
+    if my_rank != src:
+        for i, obj_size in enumerate(object_sizes_tensor):
+            obj_view = object_tensor[offset:offset + obj_size]
+            obj_view = obj_view.type(torch.uint8)
+            if obj_view.device != torch.device('cpu'):
+                obj_view = obj_view.cpu()
+            offset += obj_size
+            object_list[i] = _tensor_to_object(obj_view, obj_size)
+
+
+def broadcast_object_list(data: List[Any],
+                          src: int = 0,
+                          group: Optional[dist.ProcessGroup] = None) -> None:
+    """Broadcasts picklable objects in ``object_list`` to the whole group.
+    Similar to :func:`broadcast`, but Python objects can be passed in. Note
+    that all objects in ``object_list`` must be picklable in order to be
+    broadcasted.
+    Note:
+        Calling ``broadcast_object_list`` in non-distributed environment does
+        nothing.
+    Args:
+        data (List[Any]): List of input objects to broadcast.
+            Each object must be picklable. Only objects on the ``src`` rank
+            will be broadcast, but each rank must provide lists of equal sizes.
+        src (int): Source rank from which to broadcast ``object_list``.
+        group: (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+        device (``torch.device``, optional): If not None, the objects are
+            serialized and converted to tensors which are moved to the
+            ``device`` before broadcasting. Default is ``None``.
+    Note:
+        For NCCL-based process groups, internal tensor representations of
+        objects must be moved to the GPU device before communication starts.
+        In this case, the used device is given by
+        ``torch.cuda.current_device()`` and it is the user's responsibility to
+        ensure that this is correctly set so that each rank has an individual
+        GPU, via ``torch.cuda.set_device()``.
+    Examples:
+        >>> import torch
+        >>> import mmrazor.core.utils as dist
+        >>> # non-distributed environment
+        >>> data = ['foo', 12, {1: 2}]
+        >>> dist.broadcast_object_list(data)
+        >>> data
+        ['foo', 12, {1: 2}]
+        >>> # distributed environment
+        >>> # We have 2 process groups, 2 ranks.
+        >>> if dist.get_rank() == 0:
+        >>>     # Assumes world_size of 3.
+        >>>     data = ["foo", 12, {1: 2}]  # any picklable object
+        >>> else:
+        >>>     data = [None, None, None]
+        >>> dist.broadcast_object_list(data)
+        >>> data
+        ["foo", 12, {1: 2}]  # Rank 0
+        ["foo", 12, {1: 2}]  # Rank 1
+    """
+    warnings.warn(
+        '`broadcast_object_list` is now without return value, '
+        'and it\'s input parameters are: `data`,`src` and '
+        '`group`, but its function is similar to the old\'s', UserWarning)
+    assert isinstance(data, list)
+
+    if get_world_size(group) > 1:
+        if group is None:
+            group = get_default_group()
 
-    return object_list_return
+        if digit_version(TORCH_VERSION) >= digit_version('1.8.0'):
+            dist.broadcast_object_list(data, src, group)
+        else:
+            _broadcast_object_list(data, src, group)
diff --git a/mmrazor/core/utils/utils.py b/mmrazor/core/utils/utils.py
new file mode 100644
index 000000000..58b87d2e5
--- /dev/null
+++ b/mmrazor/core/utils/utils.py
@@ -0,0 +1,89 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Optional
+
+from torch import distributed as dist
+
+
+def is_distributed() -> bool:
+    """Return True if distributed environment has been initialized."""
+    return dist.is_available() and dist.is_initialized()
+
+
+def get_default_group() -> Optional[dist.ProcessGroup]:
+    """Return default process group."""
+
+    return dist.distributed_c10d._get_default_group()
+
+
+def get_rank(group: Optional[dist.ProcessGroup] = None) -> int:
+    """Return the rank of the given process group.
+
+    Rank is a unique identifier assigned to each process within a distributed
+    process group. They are always consecutive integers ranging from 0 to
+    ``world_size``.
+    Note:
+        Calling ``get_rank`` in non-distributed environment will return 0.
+    Args:
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Defaults to None.
+    Returns:
+        int: Return the rank of the process group if in distributed
+        environment, otherwise 0.
+    """
+
+    if is_distributed():
+        # handle low versions of torch like 1.5.0 which does not support
+        # passing in None for group argument
+        if group is None:
+            group = get_default_group()
+        return dist.get_rank(group)
+    else:
+        return 0
+
+
+def get_backend(group: Optional[dist.ProcessGroup] = None) -> Optional[str]:
+    """Return the backend of the given process group.
+
+    Note:
+        Calling ``get_backend`` in non-distributed environment will return
+        None.
+    Args:
+        group (ProcessGroup, optional): The process group to work on. The
+            default is the general main process group. If another specific
+            group is specified, the calling process must be part of
+            :attr:`group`. Defaults to None.
+    Returns:
+        str or None: Return the backend of the given process group as a lower
+        case string if in distributed environment, otherwise None.
+    """
+    if is_distributed():
+        # handle low versions of torch like 1.5.0 which does not support
+        # passing in None for group argument
+        if group is None:
+            group = get_default_group()
+        return dist.get_backend(group)
+    else:
+        return None
+
+
+def get_world_size(group: Optional[dist.ProcessGroup] = None) -> int:
+    """Return the number of the given process group.
+
+    Note:
+        Calling ``get_world_size`` in non-distributed environment will return
+        1.
+    Args:
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Defaults to None.
+    Returns:
+        int: Return the number of processes of the given process group if in
+        distributed environment, otherwise 1.
+    """
+    if is_distributed():
+        # handle low versions of torch like 1.5.0 which does not support
+        # passing in None for group argument
+        if group is None:
+            group = get_default_group()
+        return dist.get_world_size(group)
+    else:
+        return 1