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Commit 41013f0c authored by Kan Liang's avatar Kan Liang Committed by Arnaldo Carvalho de Melo
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perf script python: Add script to profile and resolve physical mem type 



There could be different types of memory in the system. E.g normal
System Memory, Persistent Memory. To understand how the workload maps to
those memories, it's important to know the I/O statistics of them.  Perf
can collect physical addresses, but those are raw data.  It still needs
extra work to resolve the physical addresses.  Provide a script to
facilitate the physical addresses resolving and I/O statistics.

Profile with MEM_INST_RETIRED.ALL_LOADS or MEM_UOPS_RETIRED.ALL_LOADS
event if any of them is available.

Look up the /proc/iomem and resolve the physical address.  Provide
memory type summary.

Here is an example output:

  # perf script report mem-phys-addr
  Event: mem_inst_retired.all_loads:P
  Memory type                                    count   percentage
  ----------------------------------------  -----------  -----------
  System RAM                                        74        53.2%
  Persistent Memory                                 55        39.6%
  N/A

  ---

Changes since V2:
 - Apply the new license rules.
 - Add comments for globals

Changes since V1:
 - Do not mix DLA and Load Latency. Do not compare the loads and stores.
   Only profile the loads.
 - Use event name to replace the RAW event

Signed-off-by: default avatarKan Liang <Kan.liang@intel.com>
Reviewed-by: default avatarAndi Kleen <ak@linux.intel.com>
Cc: Dan Williams <dan.j.williams@intel.com>
Cc: Jiri Olsa <jolsa@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Philippe Ombredanne <pombredanne@nexb.com>
Cc: Stephane Eranian <eranian@google.com>
Link: https://lkml.kernel.org/r/1515099595-34770-1-git-send-email-kan.liang@intel.com


Signed-off-by: default avatarArnaldo Carvalho de Melo <acme@redhat.com>
parent dd8bd53a

tools/perf/scripts/python/bin/mem-phys-addr-record

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+19 −0
Original line number Diff line number Diff line 
#!/bin/bash



#

# Profiling physical memory by all retired load instructions/uops event

# MEM_INST_RETIRED.ALL_LOADS or MEM_UOPS_RETIRED.ALL_LOADS

#



load=`perf list | grep mem_inst_retired.all_loads`

if [ -z "$load" ]; then

	load=`perf list | grep mem_uops_retired.all_loads`

fi

if [ -z "$load" ]; then

	echo "There is no event to count all retired load instructions/uops."

	exit 1

fi



arg=$(echo $load | tr -d ' ')

arg="$arg:P"

perf record --phys-data -e $arg $@

tools/perf/scripts/python/bin/mem-phys-addr-report

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Original line number Diff line number Diff line 
#!/bin/bash

# description: resolve physical address samples

perf script $@ -s "$PERF_EXEC_PATH"/scripts/python/mem-phys-addr.py

tools/perf/scripts/python/mem-phys-addr.py

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# mem-phys-addr.py: Resolve physical address samples

# SPDX-License-Identifier: GPL-2.0

#

# Copyright (c) 2018, Intel Corporation.



from __future__ import division

import os

import sys

import struct

import re

import bisect

import collections



sys.path.append(os.environ['PERF_EXEC_PATH'] + \

	'/scripts/python/Perf-Trace-Util/lib/Perf/Trace')



#physical address ranges for System RAM

system_ram = []

#physical address ranges for Persistent Memory

pmem = []

#file object for proc iomem

f = None

#Count for each type of memory

load_mem_type_cnt = collections.Counter()

#perf event name

event_name = None



def parse_iomem():

	global f

	f = open('/proc/iomem', 'r')

	for i, j in enumerate(f):

		m = re.split('-|:',j,2)

		if m[2].strip() == 'System RAM':

			system_ram.append(long(m[0], 16))

			system_ram.append(long(m[1], 16))

		if m[2].strip() == 'Persistent Memory':

			pmem.append(long(m[0], 16))

			pmem.append(long(m[1], 16))



def print_memory_type():

	print "Event: %s" % (event_name)

	print "%-40s  %10s  %10s\n" % ("Memory type", "count", "percentage"),

	print "%-40s  %10s  %10s\n" % ("----------------------------------------", \

					"-----------", "-----------"),

	total = sum(load_mem_type_cnt.values())

	for mem_type, count in sorted(load_mem_type_cnt.most_common(), \

					key = lambda(k, v): (v, k), reverse = True):

		print "%-40s  %10d  %10.1f%%\n" % (mem_type, count, 100 * count / total),



def trace_begin():

	parse_iomem()



def trace_end():

	print_memory_type()

	f.close()



def is_system_ram(phys_addr):

	#/proc/iomem is sorted

	position = bisect.bisect(system_ram, phys_addr)

	if position % 2 == 0:

		return False

	return True



def is_persistent_mem(phys_addr):

	position = bisect.bisect(pmem, phys_addr)

	if position % 2 == 0:

		return False

	return True



def find_memory_type(phys_addr):

	if phys_addr == 0:

		return "N/A"

	if is_system_ram(phys_addr):

		return "System RAM"



	if is_persistent_mem(phys_addr):

		return "Persistent Memory"



	#slow path, search all

	f.seek(0, 0)

	for j in f:

		m = re.split('-|:',j,2)

		if long(m[0], 16) <= phys_addr <= long(m[1], 16):

			return m[2]

	return "N/A"



def process_event(param_dict):

	name       = param_dict["ev_name"]

	sample     = param_dict["sample"]

	phys_addr  = sample["phys_addr"]



	global event_name

	if event_name == None:

		event_name = name

	load_mem_type_cnt[find_memory_type(phys_addr)] += 1

tools/perf/util/scripting-engines/trace-event-python.c

+2 −0
Original line number Diff line number Diff line
@@ -499,6 +499,8 @@ static PyObject *get_perf_sample_dict(struct perf_sample *sample, 
			PyLong_FromUnsignedLongLong(sample->time));

	pydict_set_item_string_decref(dict_sample, "period",

			PyLong_FromUnsignedLongLong(sample->period));

	pydict_set_item_string_decref(dict_sample, "phys_addr",

			PyLong_FromUnsignedLongLong(sample->phys_addr));

	set_sample_read_in_dict(dict_sample, sample, evsel);

	pydict_set_item_string_decref(dict, "sample", dict_sample);