diff --git a/mindinsight/profiler/common/_utils.py b/mindinsight/profiler/common/_utils.py
new file mode 100755
index 0000000000000000000000000000000000000000..25a24b651b82bbdacf1f742a837176721f4a2555
--- /dev/null
+++ b/mindinsight/profiler/common/_utils.py
@@ -0,0 +1,110 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Profiler utils."""
+import os
+import re
+
+def fwrite_format(output_data_path, data_source=None, is_print=False, is_start=False):
+    """
+    Write data to the output file.
+
+    Args:
+         output_data_path(str): the output file path of the data.
+         data_source(list): the data to write.
+         is_print(bool): whether to print the data to stdout.
+         is_start(bool): Whether is the first line of the output file, will remove the old file if True."
+    """
+
+    if is_start is True and os.path.exists(output_data_path):
+        os.remove(output_data_path)
+
+    if data_source.startswith("title:"):
+        title_label = '=' * 20
+        data_source = title_label + data_source[6:] + title_label
+
+    with open(output_data_path, 'a+') as f:
+        f.write(data_source)
+        f.write("\n")
+
+    if is_print:
+        print(data_source)
+
+
+def get_log_slice_id(file_name):
+    pattern = re.compile(r'(?<=slice_)\d+')
+    slice_list = pattern.findall(file_name)
+    index = re.findall(r'\d+', slice_list[0])
+    return int(index[0])
+
+def get_file_join_name(input_path, file_name):
+    """
+    Search files under the special path, and will join all the files to one file.
+
+    Args:
+        input_path(str): the source path, will search files under it.
+        file_name(str): the target of the filename, such as 'hwts.log.data.45.dev'.
+
+    Returns:
+        str: the join file name.
+    """
+    name_list = []
+    file_join_name = ''
+    input_path = os.path.realpath(input_path)
+    if os.path.exists(input_path):
+        files = os.listdir(input_path)
+        for f in files:
+            if file_name in f and not f.endswith('.done') and not f.endswith('.join') \
+                    and not f.endswith('.zip'):
+                name_list.append(f)
+
+        # resort name_list
+        name_list.sort(key=get_log_slice_id)
+
+    if len(name_list) == 1:
+        file_join_name = os.path.join(input_path, name_list[0])
+    elif len(name_list) > 1:
+        file_join_name = os.path.join(input_path, '%s.join' % file_name)
+        if os.path.exists(file_join_name):
+            os.remove(file_join_name)
+        with open(file_join_name, 'ab') as bin_data:
+            for i in name_list:
+                file = input_path + os.sep + i
+                with open(file, 'rb') as txt:
+                    bin_data.write(txt.read())
+    return file_join_name
+
+def get_file_names(input_path, file_name):
+    """
+    Search files under the special path.
+
+    Args:
+        input_path(str): the souce path, will serch files under it.
+        file_name(str): the target of the filename, such as 'host_start_log'.
+
+    Returns:
+        list: file name list.
+    """
+
+    input_path = os.path.realpath(input_path)
+    name_list = []
+    if os.path.exists(input_path):
+        files = os.listdir(input_path)
+        for f in files:
+            if file_name in f and not f.endswith('.done') \
+                    and not f.endswith('.zip'):
+                name_list.append(f)
+                break
+
+    return name_list
diff --git a/mindinsight/profiler/common/validator/checkparam.py b/mindinsight/profiler/common/validator/checkparam.py
new file mode 100755
index 0000000000000000000000000000000000000000..ebe8cc167324b7d885d308a315eb6340270959d0
--- /dev/null
+++ b/mindinsight/profiler/common/validator/checkparam.py
@@ -0,0 +1,26 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""Profiler check parameters."""
+def check_bool(input_param, param_name):
+    """Bool type judgment."""
+    if isinstance(input_param, bool):
+        return input_param
+    raise TypeError("Parameter {}: input type must be bool!".format(param_name))
+
+def check_subgraph(subgraph):
+    """Check subgraph."""
+    if subgraph in ("all", "Default", "Gradients"):
+        return subgraph
+    raise ValueError("subgraph must be all or Default or Gradients, but got {}.".format(subgraph))
diff --git a/mindinsight/profiler/parser/hwts_log_parser.py b/mindinsight/profiler/parser/hwts_log_parser.py
new file mode 100755
index 0000000000000000000000000000000000000000..1b1d14ee52310c6cf4c801006740e8ef7404ed97
--- /dev/null
+++ b/mindinsight/profiler/parser/hwts_log_parser.py
@@ -0,0 +1,100 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""The parser for hwts log file."""
+import struct
+from tabulate import tabulate
+from mindinsight.profiler.common._utils import fwrite_format, get_file_join_name
+from mindinsight.profiler.common.log import logger
+
+class HWTSLogParser:
+    """
+    The Parser for hwts log files.
+
+    Args:
+         _input_path(str): The profiling job path. Such as: '/var/log/npu/profiling/JOBAIFGJEJFEDCBAEADIFJAAAAAAAAAA".
+         output_filename(str): The output data path and name. Such as: './output_format_data_hwts_0.txt'.
+    """
+
+    _source_file_target = 'hwts.log.data.45.dev.profiler_default_tag'
+    _dst_file_title = 'title:45 HWTS data'
+    _dst_file_column_title = ['Type', 'cnt', 'Core ID', 'Block ID', 'Task ID',
+                              'Cycle counter', 'Stream ID']
+
+    def __init__(self, input_path, output_filename):
+        self._input_path = input_path
+        self._output_filename = output_filename
+        self._source_flie_name = self._get_source_file()
+
+    def _get_source_file(self):
+        """Get hwts log file name, which was created by ada service."""
+
+        file_name = get_file_join_name(self._input_path, self._source_file_target)
+        if not file_name:
+            msg = ("Fail to find hwts log file, under directory %s" \
+                   %self._input_path)
+            raise RuntimeError(msg)
+
+        return file_name
+
+    def execute(self):
+        """
+        Execute the parser, get result data, and write it to the output file.
+
+        Returns:
+            bool: whether succeed to analyse hwts log.
+        """
+
+        content_format = ['QIIIIIIIIIIII', 'QIIQIIIIIIII', 'IIIIQIIIIIIII']
+        log_type = ['Start of task', 'End of task', 'Start of block', 'End of block', 'Block PMU']
+
+        result_data = []
+
+        with open(self._source_flie_name, 'rb') as hwts_data:
+            while True:
+                line = hwts_data.read(64)
+                if line:
+                    if not line.strip():
+                        continue
+                else:
+                    break
+                byte_first_four = struct.unpack('BBHHH', line[0:8])
+                byte_first = bin(byte_first_four[0]).replace('0b', '').zfill(8)
+                ms_type = byte_first[-3:]
+                cnt = int(byte_first[0:4], 2)
+                core_id = byte_first_four[1]
+                blk_id, task_id = byte_first_four[3], byte_first_four[4]
+                if ms_type in ['000', '001', '010']:  # log type 0,1,2
+                    result = struct.unpack(content_format[0], line[8:])
+                    syscnt = result[0]
+                    stream_id = result[1]
+                    result_data.append((log_type[int(ms_type, 2)], cnt, core_id, blk_id, task_id, syscnt, stream_id))
+
+                elif ms_type == '011':  # log type 3
+                    result = struct.unpack(content_format[1], line[8:])
+                    syscnt = result[0]
+                    stream_id = result[1]
+                    result_data.append((log_type[int(ms_type, 2)], cnt, core_id, blk_id, task_id, syscnt, stream_id))
+                elif ms_type == '100':  # log type 4
+                    result = struct.unpack(content_format[2], line[8:])
+                    stream_id = result[2]
+                    result_data.append((log_type[int(ms_type, 2)], cnt, core_id, blk_id, task_id, total_cyc, stream_id))
+                else:
+                    logger.info("Profiling: invalid hwts log record type %s", ms_type)
+
+        fwrite_format(self._output_filename, data_source=self._dst_file_title, is_start=True)
+        fwrite_format(self._output_filename, data_source=tabulate(result_data,
+                                                                  self._dst_file_column_title,
+                                                                  tablefmt='simple'))
+        return True
diff --git a/mindinsight/profiler/parser/optime_parser.py b/mindinsight/profiler/parser/optime_parser.py
new file mode 100755
index 0000000000000000000000000000000000000000..fe1830528d5fdc9a8ab18afa8525b73f49fa2f9d
--- /dev/null
+++ b/mindinsight/profiler/parser/optime_parser.py
@@ -0,0 +1,122 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""op compute time files parser"""
+from tabulate import tabulate
+from mindinsight.profiler.common._utils import fwrite_format
+
+class OPComputeTimeParser:
+    """
+    Join hwts info and framework info, get op time info, and output to the result file.
+
+    Args:
+         hwts_output_file(str): The file path of hwts_output_file. Such as: './output_format_data_hwts_0.txt".
+         output_filename(str): The output data file path and name. Such as: './output_op_compute_time_0.txt'.
+         op_task_info(dict): The task and op relation info. format as: {taskid, [opname, streamid, block dim]}.
+    """
+
+    _dst_file_title = 'title:op compute time'
+    _dst_file_column_title = ['op_name', 'compute_time(ms)', 'stream_id']
+
+    def __init__(self, hwts_output_file, output_filename, op_task_info):
+        self._hwts_output_file = hwts_output_file
+        self._output_filename = output_filename
+        self._op_task_info = op_task_info
+
+    def _get_op_task_id_map(self):
+        """
+        Read hwts data file, get the task time info.
+
+        Returns:
+           list: all hwts task time info.
+        """
+
+        op_map_result = []
+        hwts_list = []
+        with(open(self._hwts_output_file, 'r')) as data_file:
+            lines = data_file.readlines()
+            for line in lines:
+                if line.startswith("Start of task"):
+                    line_split = line.split()
+                    hwts_list.append([line_split[0], line_split[6], line_split[7], line_split[8]])
+                if line.startswith('End of task'):
+                    line_split = line.split()
+                    hwts_list.append([line_split[0], line_split[6], line_split[7], line_split[8]])
+
+        # hwts op map by taskId
+        for hwts in hwts_list:
+            if hwts[1] in self._op_task_info.keys():
+                op_map_result.append([self._op_task_info[hwts[1]], hwts[0], hwts[1], hwts[2], hwts[3]])
+
+        return op_map_result
+
+    def execute(self):
+        """Execute the parser, compute all op, get op time, and write it to the output file."""
+
+        result_data = []
+        tmp_result_data = []
+        op_map_list = self._get_op_task_id_map()
+
+        cur_index = 0
+        length = len(op_map_list)
+
+        while cur_index < length:
+            if cur_index + 1 == length:
+                break
+            op_start = op_map_list[cur_index]
+            op_end = op_map_list[cur_index+1]
+
+            if op_start[1] == "Start" and op_end[1] == "End"\
+                and op_start[0] == op_end[0]:
+                # op_name, taskId, cycle counter, streamId
+                tmp_result_data.append([op_start[0], op_start[2], int(op_end[3]) - int(op_start[3]), op_start[4]])
+                cur_index += 2
+            else:
+                cur_index += 1
+
+        op_name_time_dict = {}
+        op_name_steamid_dict = {}
+        op_name_count_dict = {}
+        op_name_task_dict = {}
+
+        # compute all op
+        for item in tmp_result_data:
+            if item[0] in op_name_time_dict.keys():
+                op_name_time_dict[item[0]] += float(item[2])/1e5  # cycle counter/1*10^5 ms
+                if item[1] == op_name_task_dict[item[0]]:
+                    op_name_count_dict[item[0]] += 1
+
+            else:
+                op_name_time_dict[item[0]] = float(item[2])/1e5
+                op_name_steamid_dict[item[0]] = item[-1]
+                op_name_task_dict[item[0]] = item[1]
+                op_name_count_dict[item[0]] = 1
+
+
+        for op_name, time in op_name_time_dict.items():
+            if op_name in op_name_steamid_dict.keys():
+                stream_id = op_name_steamid_dict[op_name]
+                avg_time = time / op_name_count_dict[op_name]
+                result_data.append([op_name, avg_time, stream_id])
+
+        result_data.sort(key=lambda x: x[0])
+        total_time = 0
+        for item in result_data:
+            total_time += item[1]
+        result_data.append(["total op", total_time, 0])
+
+        fwrite_format(self._output_filename, data_source=self._dst_file_title, is_start=True)
+        fwrite_format(self._output_filename, data_source=tabulate(result_data,
+                                                                  self._dst_file_column_title,
+                                                                  tablefmt='simple'))
diff --git a/mindinsight/profiler/profiling.py b/mindinsight/profiler/profiling.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d0fd99db181c504b61b3fd93c46cc30f37a609e
--- /dev/null
+++ b/mindinsight/profiler/profiling.py
@@ -0,0 +1,290 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""profiling api file."""
+import os
+import time
+from tabulate import tabulate
+from mindinsight.profiler.parser.hwts_log_parser import HWTSLogParser
+from mindinsight.profiler.parser.framework_parser import FrameworkParser
+from mindinsight.profiler.parser.optime_parser import OPComputeTimeParser
+from mindinsight.profiler.parser.aicpu_data_parser import DataPreProcessParser
+from mindinsight.profiler.analyser.analyser_factory import AnalyserFactory
+from mindinsight.profiler.analyser.integrator import Integrator
+from mindinsight.profiler.common._utils import get_file_names, fwrite_format
+from mindinsight.profiler.common.validator.validate_path import \
+    validate_and_normalize_path
+from mindinsight.profiler.common.validator.checkparam import \
+    check_bool, check_subgraph
+from mindinsight.profiler.common.log import logger
+from mindinsight.utils.exceptions import MindInsightException
+
+profiling_log_base_path = "/var/log/npu/profiling"
+
+class Profiler:
+    """Performance profiling tool."""
+    _base_profiling_container_path = "/var/log/npu/profiling/container"
+    _hwts_output_filename_target = "output_format_data_hwts_"
+    _opcompute_output_filename_target = "output_op_compute_time_"
+    _aicpu_op_output_filename_target = "output_data_preprocess_aicpu_"
+
+    def __init__(self, subgraph='all', is_detail=True, is_show_op_path=False, output_path='./data',
+                 optypes_to_deal='', optypes_not_deal='Variable', job_id=""):
+        """
+        Init profiling service, called berfore network training.
+
+        Args:
+             subgraph(str): which subgraph to monit and anlayse, can be 'all', 'Default', 'Gradients'.
+             is_detail(Bool): whether to show profiling data for op_instace level, only show optype level if False.
+             is_show_op_path(Bool): whether to save the full path for each op instace.
+             output_path(Bool): output data path.
+             optypes_to_deal(List): Op type names, the data of which optype should be collected and analysed,
+                                    will deal with all op if null.
+             optypes_not_deal(List): Op type names, the data of which optype will not be collected and analysed.
+        """
+
+        dev_id = os.getenv('DEVICE_ID')
+        if not dev_id:
+            dev_id = "0"
+            logger.error("Fail to get DEVICE_ID, use 0 instead.")
+        self._dev_id = dev_id
+        self._container_path = os.path.join(self._base_profiling_container_path, dev_id)
+        data_path = os.path.join(self._container_path, "data")
+        if not os.path.exists(data_path):
+            os.makedirs(data_path)
+        self._output_path = validate_and_normalize_path(output_path,
+                                                        'Profiler output path (' + output_path + ')')
+        self._output_path = os.path.join(self._output_path, "profiler")
+        if not os.path.exists(self._output_path):
+            os.makedirs(self._output_path)
+
+        os.environ['PROFILING_MODE'] = 'true'
+        os.environ['PROFILING_OPTIONS'] = 'training_trace:task_trace'
+        os.environ['AICPU_PROFILING_MODE'] = 'true'
+        os.environ['PROFILING_DIR'] = str(self._container_path)
+        self._subgraph = check_subgraph(subgraph)
+        self._valid_optype_name = optypes_to_deal.split(",") if optypes_to_deal else []
+        self._filt_optype_names = optypes_not_deal.split(",") if optypes_not_deal else []
+        self._detail = check_bool(is_detail, 'is_detail')
+        self._withfullpath = check_bool(is_show_op_path, 'is_show_op_path')
+        self._profiling_job_id = job_id
+        self._start_time = int(time.time() * 10000000)
+        logger.info("Profiling: profiling start time: %d", self._start_time)
+
+
+    def analyse(self):
+        """Collect and analyze performance data, called after training or during training."""
+
+        try:
+            from mindspore.communication.management import release
+            release()
+        except ImportError:
+            logger.error("Profiling: fail to import release from mindspore.")
+
+        logger.info("begin profiler analyse")
+
+        job_id = self._get_profiling_job_id()
+        if not job_id:
+            msg = ("Fail to get profiling job, please check whether job dir was generated under path %s"\
+                   %profiling_log_base_path)
+            raise RuntimeError(msg)
+
+        logger.info("Profiling: job id is %s ", job_id)
+
+        source_path = os.path.join(profiling_log_base_path, job_id)
+        # parse hwts.log.data.45.dev file, and get task profiling data
+        hwts_output_filename = self._hwts_output_filename_target + self._dev_id + ".txt"
+        hwts_output_filename = os.path.join(self._output_path, hwts_output_filename)
+        hwtslog_parser = HWTSLogParser(source_path, hwts_output_filename)
+        result = hwtslog_parser.execute()
+        if not result:
+            logger.error("Profiling: fail to parse hwts log file.")
+            return
+
+        # parse Framework file, and get the relation of op and tasks
+        framework_parser = FrameworkParser(job_id, self._dev_id, self._output_path)
+        framework_parser.parse()
+        op_task_dict = framework_parser.to_task_id_full_op_name_dict()
+        if not op_task_dict:
+            logger.error("Profiling: fail to parse framework files.")
+            return
+
+        # get op compute time from hwts data and framework data, write output_op_compute_time.txt
+        opcompute_output_filename = self._opcompute_output_filename_target + self._dev_id + ".txt"
+        opcompute_output_filename = os.path.join(self._output_path, opcompute_output_filename)
+        optime_parser = OPComputeTimeParser(hwts_output_filename, opcompute_output_filename, op_task_dict)
+        optime_parser.execute()
+
+        # parse DATA_PREPROCESS.dev.AICPU file, write output_data_preprocess_aicpu_x.txt
+        output_data_preprocess_aicpu = self._aicpu_op_output_filename_target + self._dev_id + ".txt"
+        output_data_preprocess_aicpu = os.path.join(self._output_path, output_data_preprocess_aicpu)
+        aicpu_data_parser = DataPreProcessParser(source_path, output_data_preprocess_aicpu)
+        aicpu_data_parser.execute()
+
+        # analyse op compute time info
+        try:
+            self._analyser_op_info()
+        except MindInsightException as err:
+            logger.error(err.message)
+
+    def __del__(self):
+        """Disable the profiling collection service, called after training."""
+
+        os.environ['PROFILING_MODE'] = str("false")
+
+    def _get_profiling_job_id(self):
+        """Get profiling job id, which was generated by ada service.
+
+        Returns:
+            str: profiling jon id.
+        """
+
+        if self._profiling_job_id:
+            return self._profiling_job_id
+
+        job_id = ""
+        cmd = "ls -t " + profiling_log_base_path + "|grep JOB|awk '{print $1}'"
+        r = os.popen(cmd)
+        profiling_job_dirs = r.readlines()
+        r.close()
+        for item in profiling_job_dirs:
+            path = os.path.join(profiling_log_base_path, item.strip())
+            log_file = get_file_names(path, "host_start.log")
+            if not log_file:
+                logger.error("Profiling: job path %s, host_start.log not exist.", path)
+                continue
+
+            log_file = os.path.join(path, log_file[0])
+            item_dict = self._parse_host_start_log(log_file)
+
+            if not item_dict:
+                logger.error("Profiling: job path %s, fail to get job start info.", path)
+                continue
+            if self._start_time > int(item_dict["start_time"]):
+                logger.info("Profiling: job path %s, start_time %s, training start_time %d.",
+                            path, item_dict["start_time"], self._start_time)
+                break
+
+            if self._dev_id != item_dict["device_id"]:
+                logger.info("Profiling: job path %s, dev id %s, training device id %s.",
+                            path, item_dict["device_id"], self._dev_id)
+                continue
+
+            job_id = item.strip()
+            break
+
+        return job_id
+
+    def _parse_host_start_log(self, input_file):
+        """
+        Parse host start log file, get the device id and start time of the job.
+
+        Args:
+             input_file(str): the file path of the host start log file.
+
+        Returns:
+            dict: job start time and device id.
+        """
+
+        item_dict = {}
+        for line in open(input_file):
+            if "Device" in line:
+                item_dict["device_id"] = line[7:len(line)-2]
+            elif "clock_realtime" in line:
+                item_dict["start_time"] = line[16:len(line)-3]
+
+        return item_dict
+
+    def _analyser_op_info(self):
+        """Analyser the operator information."""
+        integrator = Integrator(self._output_path, self._dev_id)
+        integrator.integrate()
+
+        aicore_type_result = self._query_op_type_info()
+        detail_file_path = os.path.join(
+            self._output_path,
+            'output_op_compute_time_detail_{}.txt'.format(self._dev_id)
+        )
+        fwrite_format(detail_file_path, data_source='title:op compute time')
+        display_names = [
+            'optype_name', 'compute_time(ms, per-step)',
+            'called_times(per-step)', 'percent'
+        ]
+        data_source = tabulate(aicore_type_result, display_names)
+        fwrite_format(detail_file_path, data_source=data_source, is_print=True)
+
+        if self._detail:
+            op_type_order = [item[0] for item in aicore_type_result]
+            aicore_detail_result = self._query_op_detail_info(op_type_order)
+            fwrite_format(detail_file_path, data_source='', is_print=True)
+            fwrite_format(detail_file_path, data_source='Detail:', is_print=True)
+            data_source = tabulate(
+                aicore_detail_result.get('object'),
+                aicore_detail_result.get('col_name')
+            )
+            fwrite_format(detail_file_path, data_source=data_source, is_print=True)
+
+    def _query_op_type_info(self):
+        """
+        Query AICORE operator type information.
+
+        Returns:
+            list[list], the AICORE operator type and execution time information.
+        """
+        condition = {
+            'sort_condition': {
+                'name': 'execution_time',
+                'type': 'descending'
+            }
+        }
+        analyser = AnalyserFactory.instance().get_analyser(
+            'aicore_type', self._output_path, self._dev_id
+        )
+        result = analyser.query(condition)
+        return result.get('object')
+
+    def _query_op_detail_info(self, op_type_order):
+        """
+        Query AICORE operator detail information.
+
+        Args:
+            op_type_order(list): The name of the op type in order.
+
+        Returns:
+            dict, the AICORE operator detail information.
+        """
+
+        op_type_condition = {}
+        if self._valid_optype_name:
+            op_type_condition['in'] = self._valid_optype_name
+        if self._filt_optype_names:
+            op_type_condition['not_in'] = self._filt_optype_names
+
+        subgraph_condition = {}
+        if self._subgraph != 'all':
+            subgraph_condition['in'] = [self._subgraph]
+
+        filter_condition = {
+            'op_type': op_type_condition,
+            'subgraph': subgraph_condition,
+            'is_display_detail': False,
+            'is_display_full_op_name': self._withfullpath
+        }
+        analyser = AnalyserFactory.instance().get_analyser(
+            'aicore_detail', self._output_path, self._dev_id
+        )
+        result = analyser.query_and_sort_by_op_type(
+            filter_condition, op_type_order
+        )
+        return result