Tag: performance

Be aware of blocking IO APIs

Yesterday, I did performance analysis for one project. This is the output of mpstat command for old version:

And this is the CPU utilisation for new version:

For new version, the iostat ratio is remarkably high. After Checking the code, I found the original serialisation was just a fflush, but now for some reasons, it was replaced by fdatasync which is a blocking API and only returns when the data is transferred to the storage device. Therefore the thread which invokes fdatasync will be stuck there and can’t process any other message. So we must pay attention to use blocking IO APIs, sometimes they may bring you results which you don’t want.

Does CPU’s run queue include current running task on illumos?

vmstat is a useful tool to check system performance on Unix Operating Systems. The following is its output from illumos:

# vmstat
 kthr      memory            page            disk          faults      cpu
 r b w   swap  free  re  mf pi po fr de sr ro s0 -- --   in   sy   cs us sy id
 1 0 0 1831316 973680 14 38  0  0  1  0 5314 3 3  0  0 1513  472  391 83  9  7

From the manual, the first column kthr:r’ meaning is:

kthr
                 Report the number of kernel threads in each of the three following states:

                 r
                      the number of kernel threads in run queue

So the question is how to define “run queue” here? After some googling, I find this good post:

The reported operating system run queue includes both processes waiting to be serviced and those currently being serviced.

But from another excerpt from Solaris 9 System Monitoring and Tuning:

The column labeled r under the kthr section is the run queue of processes waiting to get on the CPU(s).

Does illumos‘s run queue include both current running and wait-to-run tasks? I can’t get a clear answer (I know illumos is derived from Solaris, but not sure whether the description in Solaris 9 is still applicable for illumos).

I tried to get help from illumos discussion mailing list, and Joshua gave a detailed explanation. Though he didn’t tell me the final answer, I knew where I should delve into:

(1) The statistic of runque is actually from disp_nrunnable (the code is here):

        ......
        /*
        * First count the threads waiting on kpreempt queues in each
        * CPU partition.
        */
        uint_t cpupart_nrunnable = cpupart->cp_kp_queue.disp_nrunnable;
        cpupart->cp_updates++;
        nrunnable += cpupart_nrunnable;
        ......
        /* Now count the per-CPU statistics. */
        uint_t cpu_nrunnable = cp->cpu_disp->disp_nrunnable;
        nrunnable += cpu_nrunnable;
        ......
        if (nrunnable) {
            sysinfo.runque += nrunnable;
            sysinfo.runocc++;
        }

The definition of disp_nrunnable:

/*
 * Dispatch queue structure.
 */
typedef struct _disp {
......
volatile int    disp_nrunnable; /* runnable threads in cpu dispq */
......
} disp_t;

(2) Checked the code related to disp_nrunnable:

/*
 * disp() - find the highest priority thread for this processor to run, and
 * set it in TS_ONPROC state so that resume() can be called to run it.
 */
static kthread_t *
disp()
{
    ......
    dq = &dp->disp_q[pri];
    tp = dq->dq_first;
    ......
    /*
     * Found it so remove it from queue.
     */
    dp->disp_nrunnable--;
    ......
    thread_onproc(tp, cpup);        /* set t_state to TS_ONPROC */
    ......
}

Checked definition of TS_ONPROC:

/*
 * Values that t_state may assume. Note that t_state cannot have more
 * than one of these flags set at a time.
 */
......
#define TS_RUN      0x02    /* Runnable, but not yet on a processor */
#define TS_ONPROC   0x04    /* Thread is being run on a processor */
......

Umm, it became clear: when kernel picks one task to run, removes it from the dispatch queue, decreases disp_nrunnable by 1, and set task’s state as TS_ONPROC. Based on above analysis, illumos‘s run queue should include only wait-to-run tasks, not current running ones.

To verify my thought, I implemented following simple CPU-intensive program:

# cat foo.c
int main(){
    while(1);
}

My virtual machine has only 1 CPU. Use vmstat when it is in idle state:

# vmstat 1
 kthr      memory            page            disk          faults      cpu
 r b w   swap  free  re  mf pi po fr de sr ro s0 -- --   in   sy   cs us sy id
 1 0 0 1829088 970732 8  22  0  0  0  0 2885 1 2  0  0 1523  261  343 85 10  6
 0 0 0 1827484 968844 16 53  0  0  0  0  0  0  0  0  0 2173  330  285  0 10 90
 0 0 0 1827412 968808 0   1  0  0  0  0  0  0  0  0  0 2177  284  258  0 10 90
 0 0 0 1827412 968808 0   0  0  0  0  0  0  0  0  0  0 2167  296  301  0  9 91
 0 0 0 1827412 968808 0   0  0  0  0  0  0  0  0  0  0 2173  278  298  0  9 91
 0 0 0 1827412 968808 0   0  0  0  0  0  0  0  0  0  0 2173  280  283  0  9 91
 0 0 0 1827412 968808 0   0  0  0  0  0  0  0  0  0  0 2175  279  329  0 10 90
 ......;

kthr:r was 0, and cpu:id (the last column) was more than 90. Launched one instance of foo program:

# ./foo &
[1] 668

Checked vmstat again:

# vmstat 1
 kthr      memory            page            disk          faults      cpu
 r b w   swap  free  re  mf pi po fr de sr ro s0 -- --   in   sy   cs us sy id
 1 0 0 1829076 970720 8  21  0  0  0  0 2860 1 2  0  0 1528  260  343 84 10  6
 0 0 0 1826220 968100 16 53  0  0  0  0  0  0  0  0  0 1550  334  262 90 10  0
 0 0 0 1826148 968064 0   1  0  0  0  0  0  0  0  0  0 1399  288  264 91  9  0
 0 0 0 1826148 968064 0   0  0  0  0  0  0  0  0  0  0 1283  277  253 92  8  0
 0 0 0 1826148 968064 0   0  0  0  0  0  0  0  0  0  0 1367  281  247 91  9  0
 0 0 0 1826148 968064 0   0  0  0  0  0  0  0  0  0  0 1420  277  239 91  9  0
 0 0 0 1826148 968064 0   0  0  0  0  0  0  0  0  0  0 1371  281  239 91  9  0
 0 0 0 1826148 968064 0   0  0  0  0  0  0  0  0  0  0 1289  278  250 92  8  0
 ......

This time kthr:r was still 0, but cpu:id became 0. Launched another foo:

# ./foo &
[2] 675

Checked vmstat again:

# vmstat 1
 kthr      memory            page            disk          faults      cpu
 r b w   swap  free  re  mf pi po fr de sr ro s0 -- --   in   sy   cs us sy id
 1 0 0 1828912 970572 7  20  0  0  0  0 2672 1 1  0  0 1518  244  337 84  9  6
 1 0 0 1825748 967656 16 53  0  0  0  0  0  0  0  0  0 1554  335  284 90 10  0
 1 0 0 1825676 967620 0   1  0  0  0  0  0  0  0  0  0 1497  286  271 90 10  0
 1 0 0 1825676 967620 0   0  0  0  0  0  0  0  0  0  0 1387  309  288 92  8  0
 2 0 0 1825676 967620 0   0  0  0  0  0  0  0  0  0  0 1643  365  291 90 10  0
 1 0 0 1825676 967620 0   0  0  0  0  0  0  0  0  0  0 1446  276  273 91  9  0
 1 0 0 1825676 967620 0   0  0  0  0  0  0  0  0  0  0 1325  645  456 92  8  0
 1 0 0 1825676 967620 0   0  0  0  0  0  0  0  0  0  0 1375  296  300 91  9  0

kthr:r became 1 (yes, it was 2 once). There were 2 CPU-intensive foo program running, whereas kthr:r‘s value was only 1, it means kthr:r excludes the on-CPU task. I also run another foo process, and as expected, kthr:r became 2.

Finally, the test results proved the statement from Solaris 9 System Monitoring and Tuning is right:

The column labeled r under the kthr section is the run queue of processes waiting to get on the CPU(s).

Cacheline-Orientated programming

From CPU’s perspective, the memory hierarchy is registers, L1 cache, L2 cache, L3 cache, main memory, among others. The smallest unit of cache is one cacheline, and it is 64 bytes in most cases:

$ getconf LEVEL1_DCACHE_LINESIZE
64

To make your applications run efficiently, you need to take cacheline into account. Take notorious cacheline fales sharing as an example:

    ......
    struct Foo
    {
        int a;
        int b;
        int c[14];
    };
    .....

The size of struct Foo is 64 bytes, and it can be stored in one cacheline. If CPU 0 accesses Foo.a while CPU 1 accesses Foo.b at the same time, there will be “cacheline ping-ponging” between CPUs, and the performance will be downgraded drastically.

The other trick is to allocate memory cacheline size aligned. Still use above struct Foo as the example. To guarantee the whole struct Foo in one cacheline, posix_memalign can be used:

    struct Foo *foo;
    posix_memalign(&foo, 64, sizeof(struct Foo));

The 64 is the alignment requirement.

Last but not least, sometimes padding is needed. E.g.:

    ......
    struct Foo
    {
        int a;
        int b;
        int c[12];
        int padding[2];
    };
    ......
    struct Foo *foo;
    posix_memalign(&foo, 64, sizeof(struct Foo) * 10);

Or using compiler’s aligned attribute:

    ......
    struct Foo
    {
        int a;
        int b;
        int c[12];
    } __attribute__((aligned(64)));;
    ......

The original struct Foo‘s size is 56 bytes, after padding (or through compiler’s aligned attribure), it becomes 64 bytes, and can be loaded in one cacheline. Now we can allocate an array of struct Foo, and every CPU will process one element of the array, no “cacheline ping-ponging” will occur.

Be aware of AddressSanitizer’s shadow memory usage

The following is excerpted from AddressSanitizerAlgorithm:

AddressSanitizer maps 8 bytes of the application memory into 1 byte of the shadow memory.

It means if you use ASAN_POISON_MEMORY_REGION/ASAN_UNPOISON_MEMORY_REGION (please refer AddressSanitizerManualPoisoning), you should take shadow memory usage into account if application memory is huge. E.g., my application occupies ~216 GiB, the shadow memory will occupy about 216 / 8 = 27 GiB.

Beware of OpenMP’s thread pool

For the sake of efficiency, OpenMP‘s implementation always uses thread pool to cache threads (please refer this topic). Check following simple code:

#include <unistd.h>
#include <stdio.h>
#include <omp.h>

int main(void){
        #pragma omp parallel for
        for(int i = 0; i < 256; i++)
        {
            sleep(1);
        }

        printf("Exit loop\n");

        while (1)
        {
            sleep(1);
        }

        return 0;
}

Mys server has 104 logical CPUs. Build and run it:

$ gcc -fopenmp test.c -o test
$ ./test
Exit loop

After “Exit loop” is printed, there is actually only master thread is active. Check number of threads:

$ ps --no-headers -T `pidof test` | wc -l
104

We can see all non-active threads are not destroyed and ready for future use (clang also uses thread pool inside).

The 103 non-active threads are not free; they consume resource and Operating System needs to take care of them. Sometimes they can encumber your process’s performance, especially on a system which already has heavy load. So when you write following code next time:

 #pragma omp parallel for
 for(...)
 {
    ......
 }

Try to answer following questions:
1) How many threads will be spawned?
2) Will these threads be actively used in future or only this time? If they are only valid for this time, is it possible that they become burden of the process? Please try to measure the performance of program. If the answer is yes, how about use other thread implementation instead?

P.S., the full code is here.