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#!/usr/bin/env python
# SPDX-FileCopyrightText: Copyright (c) 2021-2023 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 gpustats
import time
class GPULowUtilization(gpustats.GPUOperation):
DEFAULT_THRESHOLD = 50
DEFAULT_NUM_CHUNKS = 30
display_name = 'DEPRECATED - Use gpu_time_util instead'
usage = '{SCRIPT} -- {{DISPLAY_NAME}}'
should_display = False
message_advice = ("The following are time regions with an average GPU"
" utilization below {THRESHOLD}%%. Addressing the gaps might improve"
" application performance.\n\n"
"Suggestions:\n"
" 1. Use CPU sampling data, OS Runtime blocked state backtraces,"
" and/or OS Runtime APIs related to thread synchronization to"
" understand if a sluggish or blocked CPU is causing the gaps.\n"
" 2. Add NVTX annotations to CPU code to understand the reason"
" behind the gaps.")
message_noresult = ("There were no problems detected with GPU utilization."
" No time regions have an average GPU utilization below {THRESHOLD}%%.")
def MessageAdvice(self, extended=True):
return self.message_advice.format(
THRESHOLD=self._threshold, NUM_CHUNKS=self._chunks)
def MessageNoResult(self):
return self.message_noresult.format(
THRESHOLD=self._threshold, NUM_CHUNKS=self._chunks)
create_chunk_table = """
CREATE TEMP TABLE CHUNK (
rangeId INTEGER PRIMARY KEY NOT NULL
)
"""
insert_chunk_table = """
INSERT INTO temp.CHUNK
WITH RECURSIVE
range AS (
SELECT
0 AS rangeId
UNION ALL
SELECT
rangeId + 1 AS rangeId
FROM
range
LIMIT {NUM_CHUNKS}
)
SELECT rangeId FROM range
"""
query_format_columns = """
SELECT
ROW_NUMBER() OVER(ORDER BY average, duration) AS "Row#",
average AS "In-Use:ratio_%",
duration AS "Duration:dur_ns",
start AS "Start:ts_ns",
pid AS "PID",
deviceId AS "Device ID",
contextId AS "Context ID",
globalId AS "_Global ID",
api AS "_API"
FROM
({GPU_UNION_TABLE})
LIMIT {ROW_LIMIT}
"""
# Return chunks that have an average GPU utilization below the given threshold.
# 1. CTE "range": Define the range being analyzed for each deviceId/PID with
# the corresponding chunk size.
# 2. CTE "chunk": Duplicate chunks for each deviceId/PID with the appropriate
# start and end.
# 3. CTE "utilization": Find all ranges being run in each chunk and keep only
# the ones that have a percentage of GPU utilization lower than the threshold.
# If there are multiple streams, the utilizations are added up.
# 4. CTE "chunkgroup": Give a groupId that will be used to define consecutive
# chunks.
# 5. Coalesce chunks with same groupId and calculate the weighted average.
query_chunk = """
WITH
ops AS (
{{GPU_TABLE}}
),
range AS (
SELECT
min(start) AS start,
max(end) AS end,
ceil(CAST(max(end) - min(start) AS FLOAT) / {NUM_CHUNKS}) AS chunkSize,
pid,
globalId,
deviceId,
contextId,
api
FROM
ops
GROUP BY deviceId, pid
),
chunk AS (
SELECT
chunk.rangeId,
chunk.rangeId * range.chunkSize + range.start AS cstart,
min(chunk.rangeId * range.chunkSize + range.start + range.chunkSize, range.end) AS cend,
chunkSize,
range.pid,
range.globalId,
range.deviceId,
range.contextId,
range.api
FROM
temp.CHUNK AS chunk
JOIN
range
ON cstart < cend
),
utilization AS (
SELECT
chunk.rangeId,
chunk.cstart AS start,
chunk.cend AS end,
chunk.cend - chunk.cstart AS size,
sum(CAST(coalesce(min(ops.end, chunk.cend) - max(ops.start, chunk.cstart), 0) AS FLOAT)) / (chunk.cend - chunk.cstart) * 100 AS timePercentage,
chunk.pid,
chunk.globalId,
chunk.deviceId,
chunk.contextId,
chunk.api
FROM
chunk
LEFT JOIN
ops
ON ops.deviceId == chunk.deviceId
AND ops.pid == chunk.pid
AND ops.start < chunk.cend
AND ops.end > chunk.cstart
GROUP BY
chunk.rangeId, chunk.deviceId, chunk.pid
HAVING
timePercentage < {THRESHOLD}
),
chunkgroup AS
(
SELECT
*,
rangeId - ROW_NUMBER() OVER (PARTITION BY pid, deviceId ORDER BY rangeId) AS groupId
FROM
utilization
)
SELECT
min(start) AS start,
max(end) - min(start) AS duration,
round(sum(size * timePercentage) / sum(size), 1) AS average,
pid,
globalId,
deviceId,
contextId,
api
FROM
chunkgroup
GROUP BY groupId, deviceId, pid
LIMIT {ROW_LIMIT}
"""
_arg_opts = [
[['threshold'], {'type': int, 'default': DEFAULT_THRESHOLD,
'help': 'maximum percentage of time the GPU is being used'}],
[['chunks'], {'type': int, 'default': DEFAULT_NUM_CHUNKS,
'help': 'number of equal-duration chunks'}],
]
def setup(self):
err = super().setup()
if err != None:
return err
self._threshold = self.parsed_args.threshold
self._chunks = self.parsed_args.chunks
if self._chunks and not 1 <= self._chunks <= 1000:
self._parser.error("argument --chunks: value must be between 1 and 1000")
self.statements = [
self.create_chunk_table,
self.insert_chunk_table.format(NUM_CHUNKS = self._chunks)]
err = self.create_gpu_ops_view(self.query_chunk.format(
NUM_CHUNKS = self._chunks,
THRESHOLD = self._threshold,
ROW_LIMIT = self._row_limit))
if err != None:
return err
self.query = self.query_format_columns.format(
GPU_UNION_TABLE = self.query_gpu_ops_union(),
ROW_LIMIT = self._row_limit)
if __name__ == "__main__":
GPULowUtilization.Main()
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