(Micro)benchmarking Linux kernel functions

Vincent Bernat

Usually, the performance of a Linux subsystem is measured through an external (local or remote) process stressing it. Depending on the input point used, a large portion of code may be involved. To benchmark a single function, one solution is to write a kernel module.

Minimal kernel module

Let’s suppose we want to benchmark the IPv4 route lookup function, fib_lookup(). The following kernel function executes 1,000 lookups for and returns the average value.1 It uses the get_cycles() function to compute the execution “time.”

/* Execute a benchmark on fib_lookup() and put
   result into the provided buffer `buf`. */
static int do_bench(char *buf)
    unsigned long long t1, t2;
    unsigned long long total = 0;
    unsigned long i;
    unsigned count = 1000;
    int err = 0;
    struct fib_result res;
    struct flowi4 fl4;

    memset(&fl4, 0, sizeof(fl4));
    fl4.daddr = in_aton("");

    for (i = 0; i < count; i++) {
        t1 = get_cycles();
        err |= fib_lookup(&init_net, &fl4, &res, 0);
        t2 = get_cycles();
        total += t2 - t1;
    if (err != 0)
        return scnprintf(buf, PAGE_SIZE, "err=%d msg=\"lookup error\"\n", err);
    return scnprintf(buf, PAGE_SIZE, "avg=%llu\n", total / count);

Now, we need to embed this function in a kernel module. The following code registers a sysfs directory containing a pseudo-file run. When a user queries this file, the module runs the benchmark function and returns the result as content.

#define pr_fmt(fmt) "kbench: " fmt

#include <linux/kernel.h>
#include <linux/version.h>
#include <linux/module.h>
#include <linux/inet.h>
#include <linux/timex.h>
#include <net/ip_fib.h>

/* When a user fetches the content of the "run" file, execute the
   benchmark function. */
static ssize_t run_show(struct kobject *kobj,
                        struct kobj_attribute *attr,
                        char *buf)
    return do_bench(buf);

static struct kobj_attribute run_attr = __ATTR_RO(run);
static struct attribute *bench_attributes[] = {
static struct attribute_group bench_attr_group = {
    .attrs = bench_attributes,
static struct kobject *bench_kobj;

int init_module(void)
    int rc;
    /* ❶ Create a simple kobject named "kbench" in /sys/kernel. */
    bench_kobj = kobject_create_and_add("kbench", kernel_kobj);
    if (!bench_kobj)
        return -ENOMEM;

    /* ❷ Create the files associated with this kobject. */
    rc = sysfs_create_group(bench_kobj, &bench_attr_group);
    if (rc) {
        return rc;

    return 0;

void cleanup_module(void)

/* Metadata about this module */
MODULE_DESCRIPTION("Microbenchmark for fib_lookup()");

In ❶, kobject_create_and_add() creates a new kobject named kbench. A kobject is the abstraction behind the sysfs filesystem. This new kobject is visible as the /sys/kernel/kbench/ directory.

In ❷, sysfs_create_group() attaches a set of attributes to our kobject. These attributes are materialized as files inside /sys/kernel/kbench/. Currently, we declare only one of them, run, with the __ATTR_RO macro. The attribute is therefore read-only (0444) and when a user tries to fetch the content of the file, the run_show() function is invoked with a buffer of PAGE_SIZE bytes as last argument and is expected to return the number of bytes written.

For more details, you can look at the documentation in the kernel and the associated example. Beware, random posts found on the web (including this one) may be outdated.2

The following Makefile will compile this example:

# Kernel module compilation
KDIR = /lib/modules/$(shell uname -r)/build
obj-m += kbench_mod.o
kbench_mod.ko: kbench_mod.c
    make -C $(KDIR) M=$(PWD) modules

After executing make, you should get a kbench_mod.ko file:

$ modinfo kbench_mod.ko
filename:       /home/bernat/code/…/kbench_mod.ko
description:    Microbenchmark for fib_lookup()
license:        GPL
name:           kbench_mod
vermagic:       4.14.0-1-amd64 SMP mod_unload modversions

You can load it and execute the benchmark:

$ insmod ./kbench_mod.ko
$ ls -l /sys/kernel/kbench/run
-r--r--r-- 1 root root 4096 déc.  10 16:05 /sys/kernel/kbench/run
$ cat /sys/kernel/kbench/run

The result is a number of cycles. You can get an approximate time in nanoseconds if you divide it by the frequency of your processor in gigahertz (25 ns if you have a 3 GHz processor).3

Configurable parameters

The module hard-code two constants: the number of loops and the destination address to test. We can make these parameters user-configurable by exposing them as attributes of our kobject and define a pair of functions to read/write them:

static unsigned long loop_count      = 5000;
static u32           flow_dst_ipaddr = 0x08080808;

/* A mutex is used to ensure we are thread-safe when altering attributes. */
static DEFINE_MUTEX(kb_lock);

/* Show the current value for loop count. */
static ssize_t loop_count_show(struct kobject *kobj,
                               struct kobj_attribute *attr,
                               char *buf)
    ssize_t res;
    res = scnprintf(buf, PAGE_SIZE, "%lu\n", loop_count);
    return res;

/* Store a new value for loop count. */
static ssize_t loop_count_store(struct kobject *kobj,
                                struct kobj_attribute *attr,
                                const char *buf,
                                size_t count)
    unsigned long val;
    int err = kstrtoul(buf, 0, &val);
    if (err < 0)
        return err;
    if (val < 1)
        return -EINVAL;
    loop_count = val;
    return count;

/* Show the current value for destination address. */
static ssize_t flow_dst_ipaddr_show(struct kobject *kobj,
                                    struct kobj_attribute *attr,
                                    char *buf)
    ssize_t res;
    res = scnprintf(buf, PAGE_SIZE, "%pI4\n", &flow_dst_ipaddr);
    return res;

/* Store a new value for destination address. */
static ssize_t flow_dst_ipaddr_store(struct kobject *kobj,
                                     struct kobj_attribute *attr,
                                     const char *buf,
                                     size_t count)
    flow_dst_ipaddr = in_aton(buf);
    return count;

/* Define the new set of attributes. They are read/write attributes. */
static struct kobj_attribute loop_count_attr      = __ATTR_RW(loop_count);
static struct kobj_attribute flow_dst_ipaddr_attr = __ATTR_RW(flow_dst_ipaddr);
static struct kobj_attribute run_attr             = __ATTR_RO(run);
static struct attribute *bench_attributes[] = {

The IPv4 address is stored as a 32-bit integer but displayed and parsed using the dotted quad notation. The kernel provides the appropriate helpers for this task.

After this change, we have two new files in /sys/kernel/kbench. We can read the current values and modify them:

# cd /sys/kernel/kbench
# ls -l
-rw-r--r-- 1 root root 4096 déc.  10 19:10 flow_dst_ipaddr
-rw-r--r-- 1 root root 4096 déc.  10 19:10 loop_count
-r--r--r-- 1 root root 4096 déc.  10 19:10 run
# cat loop_count
# cat flow_dst_ipaddr
# echo > flow_dst_ipaddr
# cat flow_dst_ipaddr

We still need to alter the do_bench() function to make use of these parameters:

static int do_bench(char *buf)
    /* … */
    count = loop_count;
    fl4.daddr = flow_dst_ipaddr;

    for (i = 0; i < count; i++) {
        /* … */

Meaningful statistics

Currently, we only compute the average lookup time. This value is usually inadequate:

  • A small number of outliers can raise this value quite significantly. An outlier can happen because we were preempted out of CPU while executing the benchmarked function. This doesn’t happen often if the function execution time is short (less than a millisecond), but when this happens, the outliers can be off by several milliseconds, which is enough to make the average inadequate when most values are several order of magnitude smaller. For this reason, the median usually gives a better view.4

  • The distribution may be asymmetrical or have several local maxima. It’s better to keep several percentiles or even a distribution graph.

To be able to extract meaningful statistics, we store the results in an array.

static int do_bench(char *buf)
    unsigned long long *results;
    /* … */

    results = kmalloc(sizeof(*results) * count, GFP_KERNEL);
    if (!results)
        return scnprintf(buf, PAGE_SIZE, "msg=\"no memory\"\n");

    for (i = 0; i < count; i++) {
        t1 = get_cycles();
        err |= fib_lookup(&init_net, &fl4, &res, 0);
        t2 = get_cycles();
        results[i] = t2 - t1;

    if (err != 0) {
        return scnprintf(buf, PAGE_SIZE, "err=%d msg=\"lookup error\"\n", err);
    /* Compute and display statistics */
    display_statistics(buf, results, count);

    return strnlen(buf, PAGE_SIZE);

Then, we need an helper function to be able to compute percentiles:

static unsigned long long percentile(int p,
                                     unsigned long long *sorted,
                                     unsigned count)
    int index = p * count / 100;
    int index2 = index + 1;
    if (p * count % 100 == 0)
        return sorted[index];
    if (index2 >= count)
        index2 = index - 1;
    if (index2 < 0)
        index2 = index;
    return (sorted[index] + sorted[index+1]) / 2;

This function needs a sorted array as input. The kernel provides a heapsort function, sort(), for this purpose. Another useful value to have is the deviation from the median. Here is a function to compute the median absolute deviation:5

static unsigned long long mad(unsigned long long *sorted,
                              unsigned long long median,
                              unsigned count)
    unsigned long long *dmedian = kmalloc(sizeof(unsigned long long) * count,
    unsigned long long res;
    unsigned i;

    if (!dmedian) return 0;
    for (i = 0; i < count; i++) {
        if (sorted[i] > median)
            dmedian[i] = sorted[i] - median;
            dmedian[i] = median - sorted[i];
    sort(dmedian, count, sizeof(unsigned long long), compare_ull, NULL);
    res = percentile(50, dmedian, count);
    return res;

With these two functions, we can provide additional statistics:

static void display_statistics(char *buf,
                               unsigned long long *results,
                               unsigned long count)
    unsigned long long p95, p90, p50;

    sort(results, count, sizeof(*results), compare_ull, NULL);
    if (count == 0) {
        scnprintf(buf, PAGE_SIZE, "msg=\"no match\"\n");

    p95 = percentile(95, results, count);
    p90 = percentile(90, results, count);
    p50 = percentile(50, results, count);
    scnprintf(buf, PAGE_SIZE,
          "min=%llu max=%llu count=%lu 95th=%llu 90th=%llu 50th=%llu mad=%llu\n",
          results[count - 1],
          mad(results, p50, count));

UPDATED (2018.01): Intel recommends looking at the minimum if its variance is small.

We can also append a graph of the distribution function (and of the cumulative distribution function):

min=72 max=33364 count=100000 95th=154 90th=142 50th=112 mad=6
    value │                      ┊                         count
       72 │                                                   51
       77 │▒                                                3548
       82 │▒▒░░                                             4773
       87 │▒▒░░░░░                                          5918
       92 │░░░░░░░                                          1207
       97 │░░░░░░░                                           437
      102 │▒▒▒▒▒▒░░░░░░░░                                  12164
      107 │▒▒▒▒▒▒▒░░░░░░░░░░░░░░                           15508
      112 │▒▒▒▒▒▒▒▒▒▒▒░░░░░░░░░░░░░░░░░░░░░░               23014
      117 │▒▒▒░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░             6297
      122 │░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░              905
      127 │▒░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░           3845
      132 │▒▒▒░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░       6687
      137 │▒▒░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░     4884
      142 │▒▒░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░   4133
      147 │░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  1015
      152 │░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░  1123

Benchmark validity

While the benchmark produces some figures, we may question their validity. There are several traps when writing a microbenchmark:

dead code
Compiler may optimize away our benchmark because the result is not used. In our example, we ensure to combine the result in a variable to avoid this.
warmup phase
One-time initializations may affect negatively the benchmark. This is less likely to happen with C code since there is no JIT. Nonetheless, you may want to add a small warmup phase.
too small dataset
If the benchmark is running using the same input parameters over and over, the input data may fit entirely in the L1 cache. This affects positively the benchmark. Therefore, it is important to iterate over a large dataset.
too regular dataset
A regular dataset may still affect positively the benchmark despite its size. While the whole dataset will not fit into L1/L2 cache, the previous run may have loaded most of the data needed for the current run. In the route lookup example, as route entries are organized in a tree, it’s important to not linearly scan the address space. Address space could be explored randomly (a simple linear congruential generator brings reproducible randomness).
large overhead
If the benchmarked function runs in a few nanoseconds, the overhead of the benchmark infrastructure may be too high. Typically, the overhead of the method presented here is around 5 nanoseconds. get_cycles() is a thin wrapper around the RDTSC instruction: it returns the number of cycles for the current processor since last reset. It’s also virtualized with low-overhead in case you run the benchmark in a virtual machine. If you want to measure a function with a greater precision, you need to wrap it in a loop. However, the loop itself adds to the overhead, notably if you need to compute a large input set (in this case, the input can be prepared). Compilers also like to mess with loops. At last, a loop hides the result distribution.
While the benchmark is running, the thread executing it can be preempted (or when running in a virtual machine, the whole virtual machine can be preempted by the host). When the function takes less than a millisecond to execute, one can assume preemption is rare enough to be filtered out by using a percentile function.
When running the benchmark, noise from unrelated processes (or sibling hosts when benchmarking in a virtual machine) needs to be avoided as it may change from one run to another. Therefore, it is not a good idea to benchmark in a public cloud. On the other hand, adding controlled noise to the benchmark may lead to less artificial results: in our example, route lookup is only a small part of routing a packet and measuring it alone in a tight loop affects positively the benchmark.
syncing parallel benchmarks
While it is possible (and safe) to run several benchmarks in parallel, it may be difficult to ensure they really run in parallel: some invocations may work in better conditions because other threads are not running yet, skewing the result. Ideally, each run should execute bogus iterations and start measures only when all runs are present. This doesn’t seem a trivial addition.

As a conclusion, the benchmark module presented here is quite primitive (notably compared to a framework like JMH for Java) but, with care, can deliver some conclusive results like in these posts: “IPv4 route lookup on Linux” and “IPv6 route lookup on Linux.”

UPDATED (2018.01): Intel also recommends disabling all power optimizations, notably frequency scaling (cpupower frequency-set -g performance) and turbo mode functionalities (echo 1 > /sys/devices/system/cpu/intel_pstate/no_turbo) as the TSC is independent of the frequency.


Use of a tracing tool is an alternative approach. For example, if we want to benchmark IPv4 route lookup times, we can use the following process:

while true; do
  ip route get $((RANDOM%100)).$((RANDOM%100)).$((RANDOM%100)).5
  sleep 0.1

Then, we instrument the __fib_lookup() function with eBPF (through BCC):

$ sudo funclatency-bpfcc __fib_lookup
Tracing 1 functions for "__fib_lookup"... Hit Ctrl-C to end.
     nsecs               : count     distribution
         0 -> 1          : 0        |                    |
         2 -> 3          : 0        |                    |
         4 -> 7          : 0        |                    |
         8 -> 15         : 0        |                    |
        16 -> 31         : 0        |                    |
        32 -> 63         : 0        |                    |
        64 -> 127        : 0        |                    |
       128 -> 255        : 0        |                    |
       256 -> 511        : 3        |*                   |
       512 -> 1023       : 1        |                    |
      1024 -> 2047       : 2        |*                   |
      2048 -> 4095       : 13       |******              |
      4096 -> 8191       : 42       |********************|

Currently, the overhead is quite high, as a route lookup on an empty routing table is less than 100 ns. Once Linux supports inter-event tracing, the overhead of this solution may be reduced to be usable for such microbenchmarks.

  1. In this simple case, it may be more accurate to use:

    t1 = get_cycles();
    for (i = 0; i < count; i++) {
        err |= fib_lookup();
    t2 = get_cycles();
    total = t2 - t1;

    However, this prevents us to compute more statistics. Moreover, when you need to provide a non-constant input to the fib_lookup() function, the first way is likely to be more accurate. 

  2. In-kernel API backward compatibility is a non-goal of the Linux kernel. 

  3. You can get the current frequency with cpupower frequency-info. As the frequency may vary (even when using the performance governor), this may not be accurate but this still provides an easier representation (comparable results should use the same frequency). 

  4. It is possible to disable kernel preemption before running the benchmark:

    /* […] */

    However, on physical machines, management code may still steal the CPU and on virtual machines, there is no way to ensure the guest is not preempted. 

  5. Only integer arithmetic is available in the kernel. While it is possible to approximate a standard deviation using only integers, the median absolute deviation just reuses the percentile() function defined above. 

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