blob: c899398a871f59918a21e663d31220ae20b96d25 [file] [log] [blame]
#!/usr/bin/env python
# @lint-avoid-python-3-compatibility-imports
# cpuunclaimed Sample CPU run queues and calculate unclaimed idle CPU.
# For Linux, uses BCC, eBPF.
# This samples the length of the run queues and determine when there are idle
# CPUs, yet queued threads waiting their turn. Report the amount of idle
# (yet unclaimed by waiting threads) CPU as a system-wide percentage.
# This situation can happen for a number of reasons:
# - An application has been bound to some, but not all, CPUs, and has runnable
# threads that cannot migrate to other CPUs due to this configuration.
# - CPU affinity: an optimization that leaves threads on CPUs where the CPU
# caches are warm, even if this means short periods of waiting while other
# CPUs are idle. The wait period is tunale (see sysctl, kernel.sched*).
# - Scheduler bugs.
# An unclaimed idle of < 1% is likely to be CPU affinity, and not usually a
# cause for concern. By leaving the CPU idle, overall throughput of the system
# may be improved. This tool is best for identifying larger issues, > 2%, due
# to the coarseness of its 99 Hertz samples.
# This is an experimental tool that currently works by use of sampling to
# keep overheads low. Tool assumptions:
# - CPU samples consistently fire around the same offset. There will sometimes
# be a lag as a sample is delayed by higher-priority interrupts, but it is
# assumed the subsequent samples will catch up to the expected offsets (as
# is seen in practice). You can use -J to inspect sample offsets. Some
# systems can power down CPUs when idle, and when they wake up again they
# may begin firing at a skewed offset: this tool will detect the skew, print
# an error, and exit.
# - All CPUs are online (see ncpu).
# If this identifies unclaimed CPU, you can double check it by dumping raw
# samples (-j), as well as using other tracing tools to instrument scheduler
# events (although this latter approach has much higher overhead).
# This tool passes all sampled events to user space for post processing.
# I originally wrote this to do the calculations entirerly in kernel context,
# and only pass a summary. That involves a number of challenges, and the
# overhead savings may not outweigh the caveats. You can see my WIP here:
# USAGE: cpuunclaimed [-h] [-j] [-J] [-T] [interval] [count]
# If you see "Lost 1881 samples" warnings, try increasing wakeup_hz.
# REQUIRES: Linux 4.9+ (BPF_PROG_TYPE_PERF_EVENT support). Under tools/old is
# a version of this tool that may work on Linux 4.6 - 4.8.
# Copyright 2016 Netflix, Inc.
# Licensed under the Apache License, Version 2.0 (the "License")
# 20-Dec-2016 Brendan Gregg Created this.
from __future__ import print_function
from bcc import BPF, PerfType, PerfSWConfig
from time import sleep, strftime
from ctypes import c_int
import argparse
import multiprocessing
from os import getpid, system
import ctypes as ct
# arguments
examples = """examples:
./cpuunclaimed # sample and calculate unclaimed idle CPUs,
# output every 1 second (default)
./cpuunclaimed 5 10 # print 5 second summaries, 10 times
./cpuunclaimed -T 1 # 1s summaries and timestamps
./cpuunclaimed -j # raw dump of all samples (verbose), CSV
parser = argparse.ArgumentParser(
description="Sample CPU run queues and calculate unclaimed idle CPU",
parser.add_argument("-j", "--csv", action="store_true",
help="print sample summaries (verbose) as comma-separated values")
parser.add_argument("-J", "--fullcsv", action="store_true",
help="print sample summaries with extra fields: CPU sample offsets")
parser.add_argument("-T", "--timestamp", action="store_true",
help="include timestamp on output")
parser.add_argument("interval", nargs="?", default=-1,
help="output interval, in seconds")
parser.add_argument("count", nargs="?", default=99999999,
help="number of outputs")
parser.add_argument("--ebpf", action="store_true",
args = parser.parse_args()
countdown = int(args.count)
frequency = 99
dobind = 1
wakeup_hz = 10 # frequency to read buffers
wakeup_s = float(1) / wakeup_hz
ncpu = multiprocessing.cpu_count() # assume all are online
debug = 0
# process arguments
if args.fullcsv:
args.csv = True
if args.csv:
interval = 0.2
if args.interval != -1 and (args.fullcsv or args.csv):
print("ERROR: cannot use interval with either -j or -J. Exiting.")
if args.interval == -1:
args.interval = "1"
interval = float(args.interval)
# define BPF program
bpf_text = """
#include <uapi/linux/ptrace.h>
#include <uapi/linux/bpf_perf_event.h>
#include <linux/sched.h>
struct data_t {
u64 ts;
u64 cpu;
u64 len;
// Declare enough of cfs_rq to find nr_running, since we can't #import the
// header. This will need maintenance. It is from kernel/sched/sched.h:
struct cfs_rq_partial {
struct load_weight load;
unsigned int nr_running, h_nr_running;
int do_perf_event(struct bpf_perf_event_data *ctx)
int cpu = bpf_get_smp_processor_id();
u64 now = bpf_ktime_get_ns();
* Fetch the run queue length from task->se.cfs_rq->nr_running. This is an
* unstable interface and may need maintenance. Perhaps a future version
* of BPF will support task_rq(p) or something similar as a more reliable
* interface.
unsigned int len = 0;
struct task_struct *task = NULL;
struct cfs_rq_partial *my_q = NULL;
task = (struct task_struct *)bpf_get_current_task();
my_q = (struct cfs_rq_partial *)task->se.cfs_rq;
len = my_q->nr_running;
struct data_t data = {.ts = now, .cpu = cpu, .len = len};
events.perf_submit(ctx, &data, sizeof(data));
return 0;
# code substitutions
if debug or args.ebpf:
if args.ebpf:
# initialize BPF & perf_events
b = BPF(text=bpf_text)
# TODO: check for HW counters first and use if more accurate
ev_config=PerfSWConfig.TASK_CLOCK, fn_name="do_perf_event",
sample_period=0, sample_freq=frequency)
if args.csv:
if args.timestamp:
print("TIME", end=",")
print("TIMESTAMP_ns", end=",")
print(",".join("CPU" + str(c) for c in range(ncpu)), end="")
if args.fullcsv:
print(",", end="")
print(",".join("OFFSET_ns_CPU" + str(c) for c in range(ncpu)), end="")
print(("Sampling run queues... Output every %s seconds. " +
"Hit Ctrl-C to end.") % args.interval)
class Data(ct.Structure):
_fields_ = [
("ts", ct.c_ulonglong),
("cpu", ct.c_ulonglong),
("len", ct.c_ulonglong)
samples = {}
group = {}
last = 0
# process event
def print_event(cpu, data, size):
event = ct.cast(data, ct.POINTER(Data)).contents
samples[event.ts] = {}
samples[event.ts]['cpu'] = event.cpu
samples[event.ts]['len'] = event.len
exiting = 0 if args.interval else 1
slept = float(0)
# Choose the elapsed time from one sample group to the next that identifies a
# new sample group (a group being a set of samples from all CPUs). The
# earliest timestamp is compared in each group. This trigger is also used
# for sanity testing, if a group's samples exceed half this value.
trigger = int(0.8 * (1000000000 / frequency))
# read events
b["events"].open_perf_buffer(print_event, page_cnt=64)
while 1:
# allow some buffering by calling sleep(), to reduce the context switch
# rate and lower overhead.
if not exiting:
except KeyboardInterrupt:
exiting = 1
slept += wakeup_s
if slept < 0.999 * interval: # floating point workaround
slept = 0
positive = 0 # number of samples where an idle CPU could have run work
running = 0
idle = 0
if debug >= 2:
print("DEBUG: begin samples loop, count %d" % len(samples))
for e in sorted(samples):
if debug >= 2:
print("DEBUG: ts %d cpu %d len %d delta %d trig %d" % (e,
samples[e]['cpu'], samples[e]['len'], e - last,
e - last > trigger))
# look for time jumps to identify a new sample group
if e - last > trigger:
# first first group timestamp, and sanity test
g_time = 0
g_max = 0
for ge in sorted(group):
if g_time == 0:
g_time = ge
g_max = ge
# process previous sample group
if args.csv:
lens = [0] * ncpu
offs = [0] * ncpu
for ge in sorted(group):
lens[samples[ge]['cpu']] = samples[ge]['len']
if args.fullcsv:
offs[samples[ge]['cpu']] = ge - g_time
if g_time > 0: # else first sample
if args.timestamp:
print("%-8s" % strftime("%H:%M:%S"), end=",")
print("%d" % g_time, end=",")
print(",".join(str(lens[c]) for c in range(ncpu)), end="")
if args.fullcsv:
print(",", end="")
print(",".join(str(offs[c]) for c in range(ncpu)))
# calculate stats
g_running = 0
g_queued = 0
for ge in group:
if samples[ge]['len'] > 0:
g_running += 1
if samples[ge]['len'] > 1:
g_queued += samples[ge]['len'] - 1
g_idle = ncpu - g_running
# calculate the number of threads that could have run as the
# minimum of idle and queued
if g_idle > 0 and g_queued > 0:
if g_queued > g_idle:
i = g_idle
i = g_queued
positive += i
running += g_running
idle += g_idle
# now sanity test, after -J output
g_range = g_max - g_time
if g_range > trigger / 2:
# if a sample group exceeds half the interval, we can no
# longer draw conclusions about some CPUs idle while others
# have queued work. Error and exit. This can happen when
# CPUs power down, then start again on different offsets.
# TODO: Since this is a sampling tool, an error margin should
# be anticipated, so an improvement may be to bump a counter
# instead of exiting, and only exit if this counter shows
# a skewed sample rate of over, say, 1%. Such an approach
# would allow a small rate of outliers (sampling error),
# and, we could tighten the trigger to be, say, trigger / 5.
# In the case of a power down, if it's detectable, perhaps
# the tool could reinitialize the timers (although exiting
# is simple and works).
print(("ERROR: CPU samples arrived at skewed offsets " +
"(CPUs may have powered down when idle), " +
"spanning %d ns (expected < %d ns). Debug with -J, " +
"and see the man page. As output may begin to be " +
"unreliable, exiting.") % (g_range, trigger / 2))
# these are done, remove
for ge in sorted(group):
del samples[ge]
# begin next group
group = {}
last = e
# stash this timestamp in a sample group dict
group[e] = 1
if not args.csv:
total = running + idle
unclaimed = util = 0
if debug:
print("DEBUG: hit %d running %d idle %d total %d buffered %d" % (
positive, running, idle, total, len(samples)))
if args.timestamp:
print("%-8s " % strftime("%H:%M:%S"), end="")
# output
if total:
unclaimed = float(positive) / total
util = float(running) / total
print("%%CPU %6.2f%%, unclaimed idle %0.2f%%" % (100 * util,
100 * unclaimed))
countdown -= 1
if exiting or countdown == 0: