How many context switches per second linux

This is because the vast majority of time in this benchmark is spent in the kernel switching from one thread to the other, and the core migrations that occur when it's not pinned greatly ouweigh the loss of the very minimal parallelism. Just for fun, I rewrote the same benchmark in Go ; two goroutines ping-ponging short message between themselves over a channel.

The throughput this achieves is dramatically higher - around 2. Since switching between goroutines doesn't require an actual kernel context switch or even a system call , this isn't too surprising. For comparison, Google's fibers use a new Linux system call that can switch between two tasks in about the same time, including the kernel time.

A word of caution: benchmarks tend to be taken too seriously. Please take this one only for what it demonstrates - a largely synthetical workload used to poke into the cost of some fundamental concurrency primitives. Remember - it's quite unlikely that the actual workload of your task will be negligible compared to the us context switch; as we've seen, even a modest memcpy takes longer.

Any sort of server logic such as parsing headers, updating state, etc. If there's one takeaway to remember from these sections is that context switching on modern Linux systems is super fast. Now it's time to discuss the other overhead of a large number of threads - memory.

Even though all threads in a process share their, there are still areas of memory that aren't shared. In the post about clone we've mentioned page tables in the kernel, but these are comparatively small. A much larger memory area that it private to each thread is the stack. The default per-thread stack size on Linux is usually 8 MiB, and we can check what it is by invoking ulimit :. To see this in action, let's start a large number of threads and observe the process's memory usage.

This sample launches 10, threads and sleeps for a bit to let us observe its memory usage with external tools. What is the difference, and how can it use 80 GiB of memory on a machine that only has 16 available? A short interlude on what virtual memory means. When a Linux program allocates memory with malloc or otherwise, this memory initially doesn't really exist - it's just an entry in a table the OS keeps.

Only when the program actually accesses the memory is the backing RAM for it found; this is what virtual memory is all about. Therefore, the "memory usage" of a process can mean two things - how much virtual memory it uses overall, and how much actual memory it uses.

While the former can grow almost without bounds - the latter is obviously limited to the system's RAM capacity with swapping to disk being the other mechanism of virtual memory to assist here if usage grows above the side of physical memory.

The actual physical memory on Linux is called "resident" memory, because it's actually resident in RAM. There's a good StackOverflow discussion of this topic; here I'll just limit myself to a simple example:.

This program starts by allocating MiB of memory assuming an int size of 4 with malloc , and later "touches" this memory by writing a number into every element of the allocated array. It reports its own memory usage at every step - see the full code sample for the reporting code [4].

Here's the output from a sample run:. The most interesting thing to note is how vm size remains the same between the second and third steps, while max RSS grows from the initial value to MiB. This is precisely because until we touch the memory, it's fully "virtual" and isn't actually counted for the process's RAM usage.

Therefore, distinguishing between virtual memory and RSS in realistic usage is very important - this is why the thread launching sample from the previous section could "allocate" 80 GiB of virtual memory while having only 80 MiB of resident memory.

As we've seen, a new thread on Linux is created with 8 MiB of stack space, but this is virtual memory until the thread actually uses it. If the thread actually uses its stack, resident memory usage goes up dramatically for a large number of threads. I've added a configuration option to the sample program that launches a large number of threads; with it enabled, the thread function actually uses stack memory and from the RSS report it is easy to observe the effects.

How to we control the stack size of threads? The more interesting question is - what should the stack size be set to? As we have seen above, just creating a large stack for a thread doesn't automatically eat up all the machine's memory - not before the stack is being used. Environment Red Hat Enterprise Linux Issue Observed high number of context switching and interrupt rate on Linux box, is this a cause of concern?

Here are the common uses of Markdown. Learn more Close. Are you sure you want to request a translation? We appreciate your interest in having Red Hat content localized to your language. Please note that excessive use of this feature could cause delays in getting specific content you are interested in translated. And when you interrupt other processes , You need to save the current state of the process , So after the interruption ends , The process can still resume running from its original state.

We can go through vmstat Tool to view the context switch of the system. Want to see the details of each process , Need to use pidstat, Give it a plus -w Options , You can view the context switching of each process.

There are two columns of these results that we focus on , One is cswch, Represents the number of voluntary context switches per second ; The other is nvcswch, Represents the number of involuntary context switches per second.

Comprehensive analysis of , Because the system's ready queue is too long , It's running and waiting CPU Too many processes for , There's a lot of context switching , And context switching leads to the system CPU The occupancy rate of. You can find ,CPU The rise in usage is sysbench As a result of , But context switching comes from other processes , Including the one with the highest frequency of involuntary context switching pidstat, And the kernel thread with the highest frequency of voluntary context switching kworker and sshd.

Default pidstat Display the indicator data of the process , add -t After the parameter , Will output thread indicators. It's not over yet. The root cause is the scheduling problem of too many tasks , It's consistent with the previous analysis. This value actually depends on the system itself CPU performance. If the number of context switching is stable , From hundreds to , It should be normal. If the number of context switches exceeds , Or when the number of handoffs increases by an order of magnitude , There's probably a performance problem already.

Deep understanding of CPU context switching in Linux. In fact, these tasks don't really run at the same time , Because the system in a very short time , take CPU Assign them in turn , The illusion of multitasking running at the same time. Context switch , It's about putting the previous task first CPU The context is saved , The context of the new task is then loaded into these registers and program counters , Finally, jump to the new position indicated by the program counter , Run a new task.

And these saved contexts , Will be stored in the system kernel , And load in again when the task is rescheduled and executed. This ensures that the original state of the task is not affected , Make the task seem to run continuously.