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# SPDX-FileCopyrightText: Copyright (c) 2021-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
import nsysstats
# Used as intermediate class to create GPU operation tables including the profiling overhead.
class GPUOperation(nsysstats.ExpertSystemsReport):
query_union = """
UNION ALL
"""
def __init__(self, dbfile, args=[]):
super().__init__(dbfile, args)
self._gpu_ops_tables = {
"GPU_CUDA": self._query_cuda_gpu_ops(),
"GPU_VULKAN": self._query_vulkan_gpu_ops(),
"GPU_OPENGL": self._query_opengl_gpu_ops(),
"GPU_DX12": self._query_dx12_gpu_ops()
}
self._gpu_ops_tables = {key: value for key, value in self._gpu_ops_tables.items() if value is not None}
self._query_gpu_ops_union = ""
def query_gpu_ops_union(self):
return self._query_gpu_ops_union
def _select_gpu_ops_columns(self, table: str, api: str, global_id='globalPid', device_id='deviceId', context_id='contextId'):
query = """
SELECT
start,
end,
({GLOBAL_ID} >> 24) & 0x00FFFFFF AS pid,
{GLOBAL_ID} AS globalId,
{DEVICE_ID} AS deviceId,
{CONTEXT_ID} AS contextId,
'{API}' AS api
FROM
{TABLE}
WHERE
start > 0
"""
return query.format(
TABLE = table,
GLOBAL_ID = global_id,
DEVICE_ID = device_id,
CONTEXT_ID = context_id,
API = api
)
def _add_profiling_overhead(self, gpu_ops_table: str, overhead_condition='false'):
if self.table_exists('PROFILER_OVERHEAD'):
# Add the profiling overhead to the GPU operation table
# 1. CTE "range": Get [min(start), max(end)] for each deviceId/PID. It will be
# used as the clipping range for overheads.
# 2. CTE "overhead": Select the profiling overhead that we want to take into
# account.
# 3. Duplicate overhead rows for each deviceId/PID. This will create a deviceId
# column that is not initially in the PROFILER_OVERHEAD table.
# Note: a profiling overhead on one thread affects all GPUs of the same
# process.
# 4. The overhead rows are combined with GPU operation rows.
query_overhead = """
WITH
gpuops AS (
{TABLE}
),
range AS (
SELECT
min(start) AS start,
max(end) AS end,
pid,
globalId,
deviceId,
contextId,
api
FROM
gpuops
GROUP BY deviceId, pid
),
overheadID AS (
SELECT
id
FROM
StringIds
WHERE
{CONDITION}
),
overhead AS (
SELECT
po.start,
po.end,
(po.globalTid >> 24) & 0x00FFFFFF AS pid
FROM
PROFILER_OVERHEAD AS po
JOIN
overheadID AS co
ON co.id == po.nameId
)
SELECT
co.start,
co.end,
co.pid,
range.globalId,
range.deviceId,
range.contextId,
range.api
FROM
overhead AS co
JOIN
range
ON co.pid == range.pid
AND co.start > range.start
AND co.end < range.end
UNION ALL
SELECT
*
FROM
gpuops
"""
gpu_ops_table = query_overhead.format(
TABLE = gpu_ops_table,
CONDITION = overhead_condition
)
return gpu_ops_table
def _query_cuda_gpu_ops(self):
sub_queries = []
kernel = 'CUPTI_ACTIVITY_KIND_KERNEL'
memcpy = 'CUPTI_ACTIVITY_KIND_MEMCPY'
memset = 'CUPTI_ACTIVITY_KIND_MEMSET'
if self.table_exists(kernel):
sub_queries.append(self._select_gpu_ops_columns(kernel, 'cuda'))
if self.table_exists(memcpy):
sub_queries.append(self._select_gpu_ops_columns(memcpy, 'cuda'))
if self.table_exists(memset):
sub_queries.append(self._select_gpu_ops_columns(memset, 'cuda'))
if len(sub_queries) == 0:
return
ops = self.query_union.join(sub_queries)
overhead_condition = "value == 'CUDA profiling data flush overhead' \
OR value == 'CUDA profiling stop overhead' \
OR value == 'CUDA profiling overhead'"
return self._add_profiling_overhead(ops, overhead_condition)
def _query_vulkan_gpu_ops(self):
vulkan = 'VULKAN_WORKLOAD'
if not self.table_exists(vulkan):
return None
self.table_col_checks[vulkan] = \
{ 'gpu':
"{DBFILE} could not be analyzed due to missing 'gpu'."
" Please re-export the report file with a recent version of Nsight Systems." }
ops = self._select_gpu_ops_columns(vulkan, 'vulkan', 'globalTid', 'gpu')
return self._add_profiling_overhead(ops, "value == 'Vulkan profiling overhead'")
def _query_opengl_gpu_ops(self):
opengl = 'OPENGL_WORKLOAD'
if not self.table_exists(opengl):
return None
ops = self._select_gpu_ops_columns(opengl, 'opengl', 'globalTid', 'gpu')
return self._add_profiling_overhead(ops, "value == 'OpenGL profiling overhead'")
def _query_dx12_gpu_ops(self):
dx12 = 'DX12_WORKLOAD'
if not self.table_exists(dx12):
return None
self.table_col_checks[dx12] = \
{ 'gpu':
"{DBFILE} could not be analyzed due to missing 'gpu'."
" Please re-export the report file with a recent version of Nsight Systems."}
ops = self._select_gpu_ops_columns(dx12, 'dx12', 'globalTid', 'gpu', 'shortContextId')
return self._add_profiling_overhead(ops, "value == 'DX12 profiling overhead'")
# Creates the GPU operation view for each API and combines them into one.
# query_to_apply is a string query that needs to be applied to each GPU operation view
# before the union. It must contain a placeholder with 'GPU_TABLE' as named index.
def create_gpu_ops_view(self, query_to_apply=None):
tables_to_union = []
create_gpu_view = """
CREATE TEMP VIEW {TABLE} AS
{QUERY}
"""
query_gpu_ops = """
SELECT *
FROM {TABLE}
"""
for table_name, query in self._gpu_ops_tables.items():
if query_to_apply is not None:
query = query_to_apply.format(GPU_TABLE = query)
errmsg = self._execute_statement(
create_gpu_view.format(
TABLE = table_name,
QUERY = query
)
)
if errmsg != None:
return errmsg
tables_to_union.append(query_gpu_ops.format(TABLE = table_name))
self._query_gpu_ops_union = self.query_union.join(tables_to_union)
def setup(self):
if len(self._gpu_ops_tables) == 0:
return "{DBFILE} could not be analyzed because it does not contain the required data." \
" Does the application launch GPU operations?"
err = super().setup()
if err != None:
return err
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