I am just started coding of device driver and new to threading, went through many documents for getting an idea about threads. I still have some doubts.
what is a kernel thread?
how it differs from user thread?
what is the relationship between the two threads?
how can i implement kernel threads?
where can i see the output of the implementation?
Can anyone help me?
Thanks.
A kernel thread is a task_struct with no userspace components.
Besides the lack of userspace, it has different ancestors (kthreadd kernel thread instead of the init process) and is created by a kernel-only API instead of sequences of clone from fork/exec system calls.
Two kernel threads have kthreadd as a parent. Apart from that, kernel threads enjoy the same "independence" one from another as userspace processes.
Use the kthread_run function/macro from the kthread.h header You will most probably have to write a kernel module in order to call this function, so you should take a look a the Linux Device Drivers
If you are referring to the text output of your implementation (via printk calls), you can see this output in the kernel log using the dmesg command.
A kernel thread is a kernel task running only in kernel mode; it usually has not been created by fork() or clone() system calls. An example is kworker or kswapd.
You probably should not implement kernel threads if you don't know what they are.
Google gives many pages about kernel threads, e.g. Frey's page.
user threads & stack:
Each thread has its own stack so that it can use its own local variables, thread’s share global variables which are part of .data or .bss sections of linux executable.
Since threads share global variables i.e we use synchronization mechanisms like mutex when we want to access/modify global variables in multi threaded application. Local variables are part of thread individual stack, so no need of any synchronization.
Kernel threads
Kernel threads have emerged from the need to run kernel code in process context. Kernel threads are the basis of the workqueue mechanism. Essentially, a thread kernel is a thread that only runs in kernel mode and has no user address space or other user attributes.
To create a thread kernel, use kthread_create():
#include <linux/kthread.h>
structure task_struct *kthread_create(int (*threadfn)(void *data),
void *data, const char namefmt[], ...);
kernel threads & stack:
Kernel threads are used to do post processing tasks for kernel like pdf flush threads, workq threads etc.
Kernel threads are basically new process only without address space(can be created using clone() call with required flags), means they can’t switch to user-space. kernel threads are schedulable and preempt-able as normal processes.
kernel threads have their own stacks, which they use to manage local info.
More about kernel stacks:-
https://www.kernel.org/doc/Documentation/x86/kernel-stacks
Since you're comparing kernel threads with user[land] threads, I assume you mean something like the following.
The normal way of implementing threads nowadays is to do it in the kernel, so those can be considered "normal" threads. It's however also possible to do it in userland, using signals such as SIGALRM, whose handler will save the current process state (registers, mostly) and change them to another one previously saved. Several OSes used this as a way to implement threads before they got proper kernel thread support. They can be faster, since you don't have to go into kernel mode, but in practice they've faded away.
There's also cooperative userland threads, where one thread runs until it calls a special function (usually called yield), which then switches to another thread in a similar way as with SIGALRM above. The advantage here is that the program is in total control, which can be useful when you have timing concerns (a game for example). You also don't have to care much about thread safety. The big disadvantage is that only one thread can run at a time, and therefore this method is also uncommon now that processors have multiple cores.
Kernel threads are implemented in the kernel. Perhaps you meant how to use them? The most common way is to call pthread_create.
Related
I am a newbie to linux..
Do the "user space processes" and "kernel space processes(kernel threads)" share the same set of signal. handlers.Just wanted to how kernel sends signals differently depending on the region(user space or kernel space)where the process is running?
I think there may be some confusion here. When people say "kernel thread" in the context of UNIX, they generally just mean "thread," not "kernel space process." In the past there were two approaches to threading: libraries that implemented the concept without any assistance from the kernel, which is called user threads; and those that mainly just wrap system calls provided by the kernel specifically for multithreading, called kernel threads. These days mostly people use kernel threads, especially because the POSIX threads standard has been part of the Linux kernel since 2.6.
To answer your question, signals are always addressed to a PID (well, unless you use pthread_kill for inter-thread signaling). With POSIX threads, all the threads of a process share a single PID. But only one thread can actually be interrupted. So each thread has as part of its thread-local storage a signal mask. In practice what you are supposed to do is use pthread_sigmask to say explicitly which threads handle which signals. In Linux the root thread is the default.
I have a multi-threaded CPU and I would like each thread of the CPU to be able to launch a seperate CUDA stream. The seperate CPU threads will be doing different things at different times so there is a chance that they won't overlap but if they do launch a CUDA kernel at the same time I would like it to continue to run concurrently.
I'm pretty sure this is possible because in the CUDA Toolkit documentation section 3.2.5.5. It says "A stream is a sequence of commands (possibly issued by different host threads)..."
So if I want to implement this I would do something like
void main(int CPU_ThreadID) {
cudaStream_t *stream;
cudaStreamCreate(&stream);
int *d_a;
int *a;
cudaMalloc((void**)&d_a, 100*sizeof(int));
cudaMallocHost((void**)&a, 100*8*sizeof(int));
cudaMemcpyAsync(d_a, a[100*CPU_ThreadID], 100*size(int), cudaMemcpyHostToDevice, stream);
sum<<<100,32,0,stream>>>(d_a);
cudaStreamDestroy(stream);
}
That is just a simple example. If I know there are only 8 CPU Threads then I know at most 8 streams will be created. Is this the proper way to do this? Will this run concurrently if two or more different host threads reach this code around the same time? Thanks for any help!
Edit:
I corrected some of the syntax issues in the code block and put in the cudaMemcpyAsync as sgar91 suggested.
It really looks to me like you are proposing a multi-process application, not multithreaded. You don't mention which threading architecture you have in mind, nor even an OS, but the threading architectures I know of don't posit a thread routine called "main", and you haven't shown any preamble to the thread code.
A multi-process environment will generally create one device context per process, which will inhibit fine-grained concurrency.
Even if that's just an oversight, I would point out that a multi-threaded application should establish a GPU context on the desired device before threads are spawned.
Each thread can then issue a cudaSetDevice(0); or similar call, which should cause each thread to pick up the established context on the indicated device.
Once that is in place, you should be able to issue commands to the desired streams from whichever threads you like.
You may wish to refer to the cudaOpenMP sample code. Although it omits the streams concepts, it demonstrates a multi-threaded app with the potential for multiple threads to issue commands to the same device (and could be extended to the same stream)
Whether or not kernels happen to run concurrently or not after the above issues have been addressed is a separate issue. Concurrent kernel execution has a number of requirements, and the kernels themselves must have compatible resource requirements (blocks, shared memory, registers, etc.), which generally implies "small" kernels.
Similar points to the one in this question have been raised before here and here, and I'm aware of the Google coredump library (which I've appraised and found lacking, though I might try and work on that if I understand the problem better).
I want to obtain a core dump of a running Linux process without interrupting the process. The natural approach is to say:
if (!fork()) { abort(); }
Since the forked process gets a fixed snapshot copy of the original process's memory, I should get a complete core dump, and since the copy uses copy-on-write, it should generally be cheap. However, a critical shortcoming of this approach is that fork() only forks the current thread, and all other threads of the original process won't exist in the forked copy.
My question is whether it is possible to somehow obtain the relevant data of the other, original threads. I'm not entirely sure how to approach this problem, but here are a couple of sub-questions I've come up with:
Is the memory that contains all of the threads' stacks still available and accessible in the forked process?
Is it possible to (quicky) enumerate all the running threads in the original process and store the addresses of the bases of their stacks? As I understand it, the base of a thread stack on Linux contains a pointer to the kernel's thread bookkeeping data, so...
with the stored thread base addresses, could you read out the relevant data for each of the original threads in the forked process?
If that is possible, perhaps it would only be a matter of appending the data of the other threads to the core dump. However, if that data is lost at the point of the fork already, then there doesn't seem to be any hope for this approach.
Are you familiar with process checkpoint-restart? In particular, CRIU? It seems to me it might provide an easy option for you.
I want to obtain a core dump of a running Linux process without interrupting the process [and] to somehow obtain the relevant data of the other, original threads.
Forget about not interrupting the process. If you think about it, a core dump has to interrupt the process for the duration of the dump; your true goal must therefore be to minimize the duration of this interruption. Your original idea of using fork() does interrupt the process, it just does so for a very short time.
Is the memory that contains all of the threads' stacks still available and accessible in the forked process?
No. The fork() only retains the thread that does the actual call, and the stacks for the rest of the threads are lost.
Here is the procedure I'd use, assuming CRIU is unsuitable:
Have a parent process that generates a core dump of the child process whenever the child is stopped. (Note that more than one consecutive stop event may be generated; only the first one until the next continue event should be acted on.)
You can detect the stop/continue events using waitpid(child,,WUNTRACED|WCONTINUED).
Optional: Use sched_setaffinity() to restrict the process to a single CPU, and sched_setscheduler() (and perhaps sched_setparam()) to drop the process priority to IDLE.
You can do this from the parent process, which only needs the CAP_SYS_NICE capability (which you can give it using setcap 'cap_sys_nice=pe' parent-binary to the parent binary, if you have filesystem capabilities enabled like most current Linux distributions do), in both the effective and permitted sets.
The intent is to minimize the progress of the other threads between the moment a thread decides it wants a snapshot/dump, and the moment when all threads have been stopped. I have not tested how long it takes for the changes to take effect -- certainly they only happen at the end of their current timeslices at the very earliest. So, this step should probably be done a bit beforehand.
Personally, I don't bother. On my four-core machine, the following SIGSTOP alone yields similar latencies between threads as a mutex or a semaphore does, so I don't see any need to strive for even better synchronization.
When a thread in the child process decides it wants to take a snapshot of itself, it sends a SIGSTOP to itself (via kill(getpid(), SIGSTOP)). This stops all threads in the process.
The parent process will receive the notification that the child was stopped. It will first examines /proc/PID/task/ to obtain the TIDs for each thread of the child process (and perhaps /proc/PID/task/TID/ pseudofiles for other information), then attaches to each TID using ptrace(PTRACE_ATTACH, TID). Obviously, ptrace(PTRACE_GETREGS, TID, ...) will obtain the per-thread register states, which can be used in conjunction with /proc/PID/task/TID/smaps and /proc/PID/task/TID/mem to obtain the per-thread stack trace, and whatever other information you're interested in. (For example, you could create a debugger-compatible core file for each thread.)
When the parent process is done grabbing the dump, it lets the child process continue. I believe you need to send a separate SIGCONT signal to let the entire child process continue, instead of just relying on ptrace(PTRACE_CONT, TID), but I haven't checked this; do verify this, please.
I do believe that the above will yield a minimal delay in wall clock time between the threads in the process stopping. Quick tests on AMD Athlon II X4 640 on Xubuntu and a 3.8.0-29-generic kernel indicates tight loops incrementing a volatile variable in the other threads only advance the counters by a few thousand, depending on the number of threads (there's too much noise in the few tests I made to say anything more specific).
Limiting the process to a single CPU, and even to IDLE priority, will drastically reduce that delay even further. CAP_SYS_NICE capability allows the parent to not only reduce the priority of the child process, but also lift the priority back to original levels; filesystem capabilities mean the parent process does not even have to be setuid, as CAP_SYS_NICE alone suffices. (I think it'd be safe enough -- with some good checks in the parent program -- to be installed in e.g. university computers, where students are quite active in finding interesting ways to exploit the installed programs.)
It is possible to create a kernel patch (or module) that provides a boosted kill(getpid(), SIGSTOP) that also tries to kick off the other threads from running CPUs, and thus try to make the delay between the threads stopping even smaller. Personally, I wouldn't bother. Even without the CPU/priority manipulation I get sufficient synchronization (small enough delays between the times the threads are stopped).
Do you need some example code to illustrate my ideas above?
When you fork you get a full copy of the running processes memory. This includes all thread's stacks (after all you could have valid pointers into them). But only the calling thread continues to execute in the child.
You can easily test this. Make a multithreaded program and run:
pid_t parent_pid = getpid();
if (!fork()) {
kill(parent_pid, SIGSTOP);
char buffer[0x1000];
pid_t child_pid = getpid();
sprintf(buffer, "diff /proc/%d/maps /proc/%d/maps", parent_pid, child_pid);
system(buffer);
kill(parent_pid, SIGTERM);
return 0;
} else for (;;);
So all your memory is there and when you create a core dump it will contain all the other threads stacks (provided your maximum core file size permits it). The only pieces that will be missing are their register sets. If you need those then you will have to ptrace your parent to obtain them.
You should keep in mind though that core dumps are not designed to contain runtime information of more then one thread - the one that caused the core dump.
To answer some of your other questions:
You can enumerate threads by going through /proc/[pid]/tasks, but you can not identify their stack bases until you ptrace them.
Yes, you have full access to the other threads stacks snapshots (see above) from the forked process. It is not trivial to determine them, but they do get put into a core dump provided the core file size permits it. Your best bet is to save them in some globally accessible structure if you can upon creation.
If you intend to get the core file at non-specific location, and just get core image of the process running without killing, then you can use gcore.
If you intend to get the core file at specific location (condition) and still continue running the process - a crude approach is to execute gcore programmatically from that location.
A more classical, clean approach would be to check the API which gcore uses and embedded it in your application - but would be too much of an effort compared to the need most of the time.
HTH!
If your goal is to snapshot the entire process in order to understand the exact state of all threads at a specific point then I can't see any way to do this that doesn't require some kind of interrupt service routine. You must halt all processors and record off the current state of each thread.
I don't know of any system that provides this kind of full process core dump. The rough outlines of the process would be:
issue an interrupt across all CPUs (both logical and physical cores).
busy wait for all cores to synchronize (this shouldn't take long).
clone the desired process's memory space: duplicate the page tables and mark all pages as copy on write.
have each processor check whether its current thread is in the target process. If so record the current stack pointer for that thread.
for every other thread examine the thread data block for the current stack pointer and record it.
create a kernel thread to save off the copied memory spaces and the thread stack pointers
resume all cores.
This should capture the entire process state, including a snapshot of any processes that were running at the moment the inter-processor interrupt was issued. Because all threads are interrupted (either through standard scheduler suspension process, or via our custom interrupt process) all register states will be on a stack somewhere in the process memory. You then only need to know where the top of each thread stack is. Using the copy on write mechanism to clone the page tables allows transparent save-off while the original process is allowed to resume.
This is a pretty heavyweight option, since it's main functionality requires suspending all processors for a significant amount of time (synchronize, clone, walk all threads). However this should allow you to exactly capture the status of all threads, as well as determine which threads were running (and on which CPUs) when the checkpoint was reached. I would assume some of the framework for doing this process exists (in CRIU for instance). Of course resuming the process will result in a storm of page allocations as the copy on write mechanism protects the check-pointed system state.
I have a piece of code that handles the multi-threading (with shared resources) issue, like that:
CRITICAL_SECTION gCS;
InitializeCriticalSection(&gCS);
EnterCriticalSection(&gCS);
// Do some shared resources stuff
LeaveCriticalSection(&gCS);
In this MSDN page is written: "The threads of a single process [my bold] can use a critical section object for mutual-exclusion synchronization."
So, my question is: what about the case that the operating system decides to divide the threads to different processes, or even different processors.
Does EnterCriticalSection indeed not do the job? And if the answer is "critical sections are no help with multi-processing", what is the alternative?
I prefer not to use the Boost classes.
An operating system will not divide a thread into different processes.
EnterCriticalSection is appropriate for programs with multiple threads, as well as systems with multiple processors.
So, my question is what about the case that the operation system
decide to divide the theards to different process, or even different
processors.
Different processors - critical sections cover this.
Different processes - you need different synchronization API, which can share [kernel] objects between processes, such as mutexes and semaphores.
See sample usage in Using Mutex Objects section.
If all your threads are started in the same program, they are part of a single process and there is nothing anyone, including the OS, can do to "separate them". They exist only as part of that process and will die with the process. You are perfectly safe using a critical section.
A process is been allocated a newly address space(stack&heap), whereas when a thread is created it is implicitly assigned the initiator process's memory space ,but for a newly allocated own stack space (a new stack space is assigned to each and every different thread)
for the OS a thread executes the same as it was a process,naturally when using threads this might result in more cache and memory\page hits .
the OS executer will give time to the process who then may use his own scheduler to divide time between his threads,but this is not a must since all threads are processes they are in the same process table and can run on any core concurrently\at any time, the same as regular process.
since threads (for the same process) have the same memory they can synchronize on variables\lock objects on User level
a process should not have access to a different process's allocated memory(unless he is a thread of joint space) so synchronizing between processes should be done on some joined\global space or at kernel level
In Windows C++, createThread() causes some of the threads to slow down if one thread is doing a very CPU intensive operation. Will createProcess() alleviate this? If so, does createProcess() imply the code must reside in a second executable, or can this all take place inside the same executable?
The major difference between a process and a thread is that each process has its own memory space, while threads share the memory space of the process that they are running within.
If a thread is truly CPU bound, it will only slow another thread if they are both executing on the same processor core. createProcess will not alleviate this since a process would still have the same issue.
Also, what kind of machine are you running this on? Does it have more than one core?
Not likely - a process is much "heavier" than a thread, so it is likely to be slower still. I'm not sure what you're asking about the 2nd executable, but you can use createProcess on the same .exe.
http://msdn.microsoft.com/en-us/library/ms682425(v=vs.85).aspx
It sounds like you're chasing down some performance issues, so perhaps trying out a threading-oriented profiler would be helpful: http://software.intel.com/en-us/articles/using-intel-thread-profiler-for-win32-threads-philosophy-and-theory/
Each process provides the resources needed to execute a program. A process has a virtual address space, executable code, open handles to system objects, a security context, a unique process identifier, environment variables, a priority class, minimum and maximum working set sizes, and at least one thread of execution. Each process is started with a single thread, often called the primary thread, but can create additional threads from any of its threads.
A thread is the entity within a process that can be scheduled for execution. All threads of a process share its virtual address space and system resources. In addition, each thread maintains exception handlers, a scheduling priority, thread local storage, a unique thread identifier, and a set of structures the system will use to save the thread context until it is scheduled. The thread context includes the thread's set of machine registers, the kernel stack, a thread environment block, and a user stack in the address space of the thread's process. Threads can also have their own security context, which can be used for impersonating clients.
Create process and create thread both cause additional execution on what is a resource limited environment. Meaning no matter how you do parallel processing at some point in time your other lines of execution will imped the current. It is for this reason for very large problems that are suited to parallization distributed system are used. There are pluses and minuses tho threads and processes.
Threads
Threads allow separate execution inside of one address space meaning you can share data variables instances of objects very easily, however it also means you run into many more synchronization issues. These are painfull and as you can see from the shear number of api function involved not a light subject. Threads are a lighter weight on windows then process and as such spin up and down faster and use less resources to maintain. Threads also suffer in that one thread can cause the entire process to fail.
Processes
Process each have there own address space and as such protect themselves from being brought down by another process, but lack the ability to easily communicate. Any communication will necessarily involve some type of IPC ( Pipes, TCP , ...).
The code does not have to be in a second executable just two instances need to run.
That would make things worse. When switching threads, the CPU needs to swap out only a few registers. Since all threads of a process share the same memory, there's no need to flush the cache. But when switching betweeen processes, you also switch mapped memory. Therefore, the CPU has to flush the L1 cache. That's painful.
(L2 cache is physically mapped, i.e. uses hardware addresses. Those don't change, of course.)