I'm using busy waiting to synchronize access to critical regions, like this:
while (p1_flag != T_ID);
/* begin: critical section */
for (int i=0; i<N; i++) {
...
}
/* end: critical section */
p1_flag++;
p1_flag is a global volatile variable that is updated by another concurrent thread. As a matter of fact, I've two critical sections inside a loop and I've two threads (both executing the same loop) that commute execution of these critical regions. For instance, critical regions are named A and B.
Thread 1 Thread 2
A
B A
A B
B A
A B
B A
B
The parallel code executes faster than the serial one, however not as much as I expected. Profiling the parallel program using VTune Amplifier I noticed that a large amount of time is being spent in the synchronization directives, that is, the while(...) and flag update. I'm not sure why I'm seeing so large overhead on these "instructions" since region A is exactly the same as region B. My best guess is that this is due the cache coherence latency: I'm using an Intel i7 Ivy Bridge Machine and this micro-architecture resolves cache coherence at the L3. VTune also tells that the while (...) instruction is consuming all front-end bandwidth, but why?
To make the question(s) clear: Why are while(...) and update flag instructions taking so much execution time? Why would the while(...) instruction saturate the front-end bandwidth?
The overhead you're paying may very well be due to passing the sync variable back and forth between the core caches.
Cache coherency dictates that when you modify the cache line (p1_flag++) you need to have ownership on it. This means it would invalidate any copy existing in other cores, waiting for it to write back any changes made by that other core to a shared cache level. It would then provide the line to the requesting core in M state and perform the modification.
However, the other core would by then be constantly reading this line, read that would snoop the first core and ask if it has copy of that line. Since the first core is holding an M copy of that line, it would get written back to the shared cache and the core would lose ownership.
Now this depends on actual implementation in HW, but if the line was snooped before the change was actually made, the first core would have to attempt to get ownership on it again. In some cases i'd imagine this might take several iterations of attempts.
If you're set on using busy wait, you should at least use some pause inside it
: _mm_pause intrisic, or just __asm("pause"). This would both serve to give the other thread a chance get the lock and release you from waiting, as well as reducing the CPU effort in busy waiting (an out-of-order CPU would fill all pipelines with parallel instances of this busy wait, consuming lots of power - a pause would serialize it so only a single iteration can run at any given time - much less consuming and with the same effect).
A busy-wait is almost never a good idea in multithreaded applications.
When you busy-wait, thread scheduling algorithms will have no way of knowing that your loop is waiting on another thread, so they must allocate time as if your thread is doing useful work. And it does take processor time to check that variable over, and over, and over, and over, and over, and over...until it is finally "unlocked" by the other thread. In the meantime, your other thread will be preempted by your busy-waiting thread again and again, for no purpose at all.
This is an even worse problem if the scheduler is a priority-based one, and the busy-waiting thread is at a higher priority. In this situation, the lower-priority thread will NEVER preempt the higher-priority thread, thus you have a deadlock situation.
You should ALWAYS use semaphores or mutex objects or messaging to synchronize threads. I've never seen a situation where a busy-wait was the right solution.
When you use a semaphore or mutex, then the scheduler knows never to schedule that thread until the semaphore or mutex is released. Thus your thread will never be taking time away from threads that do real work.
Related
When I have a block of code like this:
mutex mtx;
void hello(){
mtx.lock();
for(int i = 0; i < 10; i++){
cout << "hello";
}
mtx.unlock();
}
void hi(){
mtx.lock();
for(int i = 0; i < 10; i++){
cout << "hi";
}
mtx.unlock();
}
int main(){
thread x(hello);
thread y(hi);
x.join();
y.join();
}
What is the difference between just calling `hello()` and `hi()`? (Like so)
...
int main(){
hello();
hi();
}
Are threads more efficient? The purpose of thread is to run at the same time, right?
Can someone explain why we use mutexes within thread functions? Thank you!
The purpose of thread is to run at the same time, right?
Yes, threads are used to perform multiple tasks in parallel, especially on different CPUs.
Can someone explain why we use mutexes within thread functions?
To serialize multiple threads with each other, such as when they are accessing a shared resource that is not safe to access concurrently and needs to be protected.
Are threads more efficient?
No. But see final note (below).
On a single core, threads are much, much less efficient (than function/method calls).
As one example, on my Ubuntu 15.10(64), using g++ v5.2.1,
a) a context switch (from one thread to the other) enforced by use of std::mutex takes about 12,000 nanoseconds
b) but invoking 2 simple methods, for instance std::mutex lock() & unlock(), this takes < 50 nanoseconds. 3 orders of magnitude! So context switch vx function call is no contest.
The purpose of thread is to run at the same time, right?
Yes ... but this can not happen on a single core processor.
And on a multi-core system, context switch time can still dominate.
For example, my Ubuntu system is dual core. The measurement of context switch time I reported above uses a chain of 10 threads, where each thread simply waits for its input semaphore to be unlock()'d. When a thread's input semaphore is unlocked, the thread gets to run ... but the brief thread activity is simply 1) increment a count and check a flag, and 2) unlock() the next thread, and 3) lock() its own input mutex, i.e. wait again for the previous task signal. In that test, the thread we known as main starts the thread-sequencing with unlock() of one of the threads, and stops it with a flag that all threads can see.
During this measurement activity (about 3 seconds), Linux system monitor shows both cores are involved, and reports both cores at abut 60% utilization. I expected both cores at 100% .. don't know why they are not.
Can someone explain why we use mutexes within thread functions? Thank
you!
I suppose the most conventional use of std::mutex's is to serialize access to a memory structure (perhaps a shared-access storage or structure). If your application has data accessible by multiple threads, each write access must be serialized to prevent race conditions from corrupting the data. Sometimes, both read and write access needs to be serialized. (See dining philosophers problem.)
In your code, as an example (although I do not know what system you are using), it is possible that std::cout (a shared structure) will 'interleave' text. That is, a thread context switch might happen in the middle of printing a "hello", or even a 'hi'. This behaviour is usually undesired, but might be acceptable.
A number of years ago, I worked with vxWorks and my team learned to use mutex's on access to std::cout to eliminate that interleaving. Such behavior can be distracting, and generally, customers do not like it. (ultimately, for that app, we did away with the use of the std trio-io (cout, cerr, cin))
Devices, of various kinds, also might not function properly if you allow more than 1 thread to attempt operations on them 'simultaneously'. For example, I have written software for a device that required 50 us or more to complete its reaction to my software's 'poke', before any additional action to the device should be applied. The device simply ignored my codes actions without the wait.
You should also know that there are techniques that do not involve semaphores, but instead use a thread and an IPC to provide serialized (i.e. protected) resource access.
From wikipedia, "In concurrent programming, a monitor is a synchronization construct that allows threads to have both mutual exclusion and the ability to wait (block) for a certain condition to become true."
When the os provides a suitable IPC, I prefer to use a Hoare monitor. In my interpretation, the monitor is simply a thread that accepts commands over the IPC, and is the only thread to access the shared structure or device. When only 1 thread accesses a structure, NO mutex is needed. All other threads must send a message (via IPC) to request (or perhaps command) another structure change. The monitor thread handles one request at a time, sequentially out of the IPC.
Definition: collision
In the context of "thread context switch' and 'mutex semaphores', a 'collision' occurs when a thread must block-and-wait for access to a resource, because that resource is already 'in use' (i.e. 'occupied'). This is a forced context switch. See also the term "critical section".
When the shared resource is NOT currently in use, no collision. The lock() and unlock() cost almost nothing (by comparison to context switch).
When there is a collision, the context switch slows things down by a 'bunch'. But this 'bunch' might still be acceptable ... consider when 'bunch' is small compared to the duration of the activity inside the critical section.
Final note ... With this new idea of 'collision':
a) Multiple threads can be far less efficient in the face of many collisions.
For unexpected example, the function 'new' accesses a thread-shared resource we can call "dynamic memory". In one experience, each thread generated 1000's of new's at start up. One thread could complete that effort in 0.5 seconds. Four threads, started quickly back-to-back, took 40 seconds to complete the 4 start ups. Context switches!
b) Multiple threads can be more efficient, when you have multiple cores and no / or few collisions. Essentially, if the threads seldom interact, they can run (mostly) simultaneously.
Thread efficiency can be any where between a or b, when multiple cores and collisions.
For instance, my ram based "log" mechanisms seems to work well - one mutex access per log entry. Generally, I intentionally used minimal logging. And when debugging a 'discovered' challenge, I added additional logging (maybe later removed) to determine what was going wrong. Generally, the debugger is better than a general logging technique. But sometimes, adding several log entries worked well.
Threads have at least two advantages over purely serial code.
Convenience in separating logically independent sequences of instructions. This is true even on a single core machine. This gives you logical concurrency without necessarily parallelism.
Having multiple threads allows either the operating system or a user-level threading library to multiplex multiple logical threads over a smaller number of CPU cores, without the application developer having to worry about other threads and processes.
Taking advantage of multiple cores / processors. Threads allow you to scale your execution to the number of CPU cores you have, enabling parallelism.
Your example is a little contrived because the entire thread's execution is locked. Normally, threads perform many actions independently and only take a mutex when accessing a shared resource.
More specifically, under your scenario you would not gain any performance. However, if your entire thread was not under a mutex, then you could potentially gain efficiency. I say potentially because there are overheads to running multiple threads which may offset any efficiency gain you obtain.
Threads theoretically run simultaneously, it means that threads could write to the same memory block at the same time. For example, if you have a global var int i;, and two threads try to write different values at same time, which one value remains in i?
Mutex forces synchronous access to memory, inside a mutex block (mutex.lock & mutex.unlock) you warrant synchronous memory access and avoid memory corruption.
When you call mtx.lock(), JUST ONE THREAD KEEPS RUNNING, and any other thread calling the same mtx.lock() stops, waiting for mtx.unlock call.
https://www.linkedin.com/pulse/how-do-i-design-high-frequency-trading-systems-its-part-silahian-2/
avoiding cache misses and CPU’s context switching
how does busy/wait and spinning pattern avoids context switches if it runs two threads in one core ?
It will still have context switches between these two threads(producer thread and 1 consumer thread) right ?
what are the consequences if I don't set thread affinity to one specific core ?
I completely get why it avoids cache misses. But i am still having trouble how does it solve avoiding context switches.
how does busy/wait and spinning pattern avoids context switches if it runs two threads in one core ?
When a thread perform a lock and the lock is taken by another thread, it make s system call so the OS can know that the thread is waiting for a lock and this should be worthless to let it continue its execution. This causes system call and a context switch (because the OS will try to execute another threads on the processing unit) which are expensive.
Using a spin lock somehow lies to the OS by not saying the thread is waiting while this is actually the case. As a result the OS will wait the end of the quantum (time slice allocated to the thread) before doing a context switch. A quantum is generally pretty big (eg. 8 ms) so the overhead of context switches in this case does not seems a problem. However, it is if you care about latency. Indeed, if another thread also use a spin lock, this cause a very inefficient execution because each thread will be executed during the full quantum and the average latency will be half the quantum which is generally far more than the overhead of a context switch. To avoid this happening, you should be sure that there are more core than thread to be actively running and control the environment. Otherwise, spin locks are actually more harmful than context switches. If you do not control the environment, then spin locks are generally not a good idea.
If 2 threads running on the same core and 2-way SMT is enabled, then each thread will likely be executed on each of the hardware thread. In this case spin locks can significantly slow down the other thread while doing nothing. The x86-64 pause instruction can be used to tell to the processor that the thread is doing a spin lock and let the other thread of the same core be executed. This instruction also benefit from reducing contention. Note that even with the pause instruction, modern processor will run at full speed (possibly in turbo frequency) causing them to consume a lot of energy (and so heat).
what are the consequences if I don't set thread affinity to one specific core ?
Threads can then move between cores. If a thread move from one core to another it generally needs to reload data to its cache (typically fill the L2 from the L3 or another L2 cache) during cache misses that may occur on the critical path of a latency-critical operation. In the most critical cases, a thread can move from one NUMA node to another. Data transfer between NUMA nodes are generally slower. Not to mention the core will then access to data own by the memory of another NUMA node which is more expensive. Besides, it can increase the overall cost of context switches.
I'm currently reading C++ Concurrency in Action book by Anthony Williams and there are several lock free data structures implementations. In the forward of the chapter about lock free data structures in the book Anthony is writing:
This brings us to another downside of lock-free and wait-free code: although it can increase the potential for concurrency of operations on a data structure and reduce the time an individual thread spends waiting, it may well decrease overall performance.
And indeed I tested all lock free stack implementations described in the book against lock based implementation from one of the previous chapters. And it seems the performance of lock free code is always lower than the lock based stack.
In what circumstances lock free data structure are more optimal and must be preferred?
One benefit of lock-free structures is that they do not require context switch. However, in modern systems, uncontended locks are also context-switch free. To benefit (performance-wise) from lock-free algo, several conditions have to be met:
Contention has to be high
There should be enough CPU cores so that spinning thread can run uninterrupted (ideally, should be pinned to its own core)
I've done performance study years ago. When the number of threads is small, lock-free data structures and lock-based data structures are comparable. But as the number of threads rises, at some point lock-based data structures exhibit a sharp performance drop, while lock-free data structures scale up to thousands of threads.
it depends on the probability of a collision.
if a collision is very likely, than a mutex is the optimal solution.
For example: 2 threads are constantly pushing data to the end of a container.
With lock-freedom only 1 thread will succeed. The other will need to retry. In this scenario the blocking and waiting would be better.
But if you have a large container and the 2 threads will access the container at different areas, its very likely, that there will be no collision.
For example: one thread modifies the first element of a container and the other thread the last element.
In this case, the probability of a retry is very small, hence lock-freedom would be better here.
Other problem with lock-freedom are spin-locks (heavy memory-usage), the overall performance of the atomic-variables and some constraints on variables.
For example if you have the constraint x == y which needs to be true, you cannot use atomic-variables for x and y, because you cannot change both variables at once, while a lock() would satisfy the constraint
The only way to know which is better is to profile each. The result will change drastically from use case to use case, from one cpu to another, from one arch to another, from one year to another. What might be best today might not be best tomorrow. So always measure and keep measuring.
That said let me give you some of my private thoughts on this:
First: If there is no contention it shouldn't matter what you do. The no-collision case should always be fast. If it's not then you need a different implementation tuned to the no contention case. One implementation might use fewer or faster machine instruction than the other and win but the difference should be minimal. Test, but I expect near identical results.
Next lets look at cases with (high) contention:
Again you might need an implementation tuned to the contention case. One lock mechanism isn't like the other same as lock-free methods.
threads <= cores
It's reasonable to assume all threads will be running and do work. There might be small pauses where a thread gets interrupted but that's really the exception. This obviously will only hold true if you only have one application doing this. The threads of all cpu heavy applications add up for this scenario.
Now with a lock one thread will get the lock and work. Other threads can wait for the lock to become available and react the instant the lock becomes free. They can busy loop or for longer durations sleep, doesn't matter much. The lock limits you to 1 thread doing work and you get that with barely any cpu cycles wasted when switching locks.
On the other hand lock free data structures all fall into some try&repeat loop. They will do work and at some crucial point they will try to commit that work and if there was contention they will fail and try again. Often repeating a lot of expensive operations. The more contention there is the more wasted work there is. Worse, all that access on the caches and memory will slow down the thread that actually manages to commit work in the end. So you are not only not getting ahead faster, you are slowing down progress.
I would always go with locks with any workload that takes more cpu cycles than the lock instruction vs. the CAS (or similar) instruction a lock free implementation needs. It really doesn't take much work there leaving only trivial cases for the lock-free approach. The builtin atomic types are such a case and often CPUs have opcodes to do those atomic operations lock-free in hardware in a single instruction that is (relatively) fast. In the end the lock will use such an instruction itself and can never be faster than such a trivial case.
threads >> cores
If you have much more threads than cores then only a fraction of them can run at any one time. It is likely a thread that sleeps will hold a lock. All other threads needing the lock will then also have to go to sleep until the lock holding thread wakes up again. This is probably the worst case for locking data structures. Nobody gets to do any work.
Now there are implementations for locks (with help from the operating system) where one thread trying to acquire a lock will cause the lock holding thread to take over till it releases the lock. In such systems the waste is reduced to context switching between the thread.
There is also a problem with locks called the thundering herd problem. If you have 100 threads waiting on a lock and the lock gets freed, then depending on your lock implementation and OS support, 100 threads will wake up. One will get the lock and 99 will waste time trying to acquire the lock, fail and go back to sleep. You really don't want a lock implementation suffering from thundering herds.
Lock free data structures begin to shine here. If one thread is descheduled then the other thread will continue their work and succeed in committing the result. The thread will wake up again at some point and fail to commit it's work and retry. The waste is in the work the one descheduled thread did.
cores < threads < 2 * cores
There is a grey zone there when the number of threads is near the number of cores. The chance the blocking thread is running remains high. But this is a very chaotic region. Results what method is better are rather random there. My conclusion: If you don't have tons of threads then try really hard to stay <= core count.
Some more thoughs:
Sometimes the work, once started, needs to be done in a specific order. If one thread is descheduled you can't just skip it. You see this in some data structures where the code will detect a conflict and one thread actually finishes the work a different thread started before it can commit it's own results. Now this is really great if the other thread was descheduled. But if it's actually running it's just wasteful to do the work twice. So data structure with this scheme really aim towards scenario 2 above.
With the amount of mobile computing done today it becomes more and more important to consider the power usage of your code. There are many ways you can optimize your code to change power usage. But really the only way for your code to use less power is to sleep. Something you hear more and more is "race to sleep". If you can make your code run faster so it can sleep earlier then you save power. But the emphasis here is on sleep earlier, or maybe I should say sleep more. If you have 2 threads running 75% of the time they might solve your problem in 75 seconds. But if you can solve the same problem with 2 threads running 50% of the time, alternating with a lock, then they take 100 seconds. But the first also uses 150% cpu power. For a shorter time, true, but 75 * 150% = 112.5 > 100 * 100%. Power wise the slower solution wins. Locks let you sleep while lock free trades power for speed.
Keep that in mind and balance your need for speed with the need to recharge your phone of laptop.
The mutex design will very rarely, if ever out perform the lockless one.
So the follow up question is why would anyone ever use a mutex rather than a lockless design?
The problem is that lockless designs can be hard to do, and require a significant amount of designing to be reliable; while a mutex is quite trivial (in comparison), and when debugging can be even harder. For this reason, people generally prefer to use mutexes first, and then migrate to lock free later once the contention has been proven to be a bottleneck.
I think one thing missing in these answers is locking period. If your locking period is very short, i.e. after acquiring lock if you perform a task for a very short period(like incrementing a variable) then using lock-based data structure would bring in unnecessary context switching, cpu scheduling etc. In this case, lock-free is a good option as the thread would be spinning for a very short time.
When I have a block of code like this:
mutex mtx;
void hello(){
mtx.lock();
for(int i = 0; i < 10; i++){
cout << "hello";
}
mtx.unlock();
}
void hi(){
mtx.lock();
for(int i = 0; i < 10; i++){
cout << "hi";
}
mtx.unlock();
}
int main(){
thread x(hello);
thread y(hi);
x.join();
y.join();
}
What is the difference between just calling `hello()` and `hi()`? (Like so)
...
int main(){
hello();
hi();
}
Are threads more efficient? The purpose of thread is to run at the same time, right?
Can someone explain why we use mutexes within thread functions? Thank you!
The purpose of thread is to run at the same time, right?
Yes, threads are used to perform multiple tasks in parallel, especially on different CPUs.
Can someone explain why we use mutexes within thread functions?
To serialize multiple threads with each other, such as when they are accessing a shared resource that is not safe to access concurrently and needs to be protected.
Are threads more efficient?
No. But see final note (below).
On a single core, threads are much, much less efficient (than function/method calls).
As one example, on my Ubuntu 15.10(64), using g++ v5.2.1,
a) a context switch (from one thread to the other) enforced by use of std::mutex takes about 12,000 nanoseconds
b) but invoking 2 simple methods, for instance std::mutex lock() & unlock(), this takes < 50 nanoseconds. 3 orders of magnitude! So context switch vx function call is no contest.
The purpose of thread is to run at the same time, right?
Yes ... but this can not happen on a single core processor.
And on a multi-core system, context switch time can still dominate.
For example, my Ubuntu system is dual core. The measurement of context switch time I reported above uses a chain of 10 threads, where each thread simply waits for its input semaphore to be unlock()'d. When a thread's input semaphore is unlocked, the thread gets to run ... but the brief thread activity is simply 1) increment a count and check a flag, and 2) unlock() the next thread, and 3) lock() its own input mutex, i.e. wait again for the previous task signal. In that test, the thread we known as main starts the thread-sequencing with unlock() of one of the threads, and stops it with a flag that all threads can see.
During this measurement activity (about 3 seconds), Linux system monitor shows both cores are involved, and reports both cores at abut 60% utilization. I expected both cores at 100% .. don't know why they are not.
Can someone explain why we use mutexes within thread functions? Thank
you!
I suppose the most conventional use of std::mutex's is to serialize access to a memory structure (perhaps a shared-access storage or structure). If your application has data accessible by multiple threads, each write access must be serialized to prevent race conditions from corrupting the data. Sometimes, both read and write access needs to be serialized. (See dining philosophers problem.)
In your code, as an example (although I do not know what system you are using), it is possible that std::cout (a shared structure) will 'interleave' text. That is, a thread context switch might happen in the middle of printing a "hello", or even a 'hi'. This behaviour is usually undesired, but might be acceptable.
A number of years ago, I worked with vxWorks and my team learned to use mutex's on access to std::cout to eliminate that interleaving. Such behavior can be distracting, and generally, customers do not like it. (ultimately, for that app, we did away with the use of the std trio-io (cout, cerr, cin))
Devices, of various kinds, also might not function properly if you allow more than 1 thread to attempt operations on them 'simultaneously'. For example, I have written software for a device that required 50 us or more to complete its reaction to my software's 'poke', before any additional action to the device should be applied. The device simply ignored my codes actions without the wait.
You should also know that there are techniques that do not involve semaphores, but instead use a thread and an IPC to provide serialized (i.e. protected) resource access.
From wikipedia, "In concurrent programming, a monitor is a synchronization construct that allows threads to have both mutual exclusion and the ability to wait (block) for a certain condition to become true."
When the os provides a suitable IPC, I prefer to use a Hoare monitor. In my interpretation, the monitor is simply a thread that accepts commands over the IPC, and is the only thread to access the shared structure or device. When only 1 thread accesses a structure, NO mutex is needed. All other threads must send a message (via IPC) to request (or perhaps command) another structure change. The monitor thread handles one request at a time, sequentially out of the IPC.
Definition: collision
In the context of "thread context switch' and 'mutex semaphores', a 'collision' occurs when a thread must block-and-wait for access to a resource, because that resource is already 'in use' (i.e. 'occupied'). This is a forced context switch. See also the term "critical section".
When the shared resource is NOT currently in use, no collision. The lock() and unlock() cost almost nothing (by comparison to context switch).
When there is a collision, the context switch slows things down by a 'bunch'. But this 'bunch' might still be acceptable ... consider when 'bunch' is small compared to the duration of the activity inside the critical section.
Final note ... With this new idea of 'collision':
a) Multiple threads can be far less efficient in the face of many collisions.
For unexpected example, the function 'new' accesses a thread-shared resource we can call "dynamic memory". In one experience, each thread generated 1000's of new's at start up. One thread could complete that effort in 0.5 seconds. Four threads, started quickly back-to-back, took 40 seconds to complete the 4 start ups. Context switches!
b) Multiple threads can be more efficient, when you have multiple cores and no / or few collisions. Essentially, if the threads seldom interact, they can run (mostly) simultaneously.
Thread efficiency can be any where between a or b, when multiple cores and collisions.
For instance, my ram based "log" mechanisms seems to work well - one mutex access per log entry. Generally, I intentionally used minimal logging. And when debugging a 'discovered' challenge, I added additional logging (maybe later removed) to determine what was going wrong. Generally, the debugger is better than a general logging technique. But sometimes, adding several log entries worked well.
Threads have at least two advantages over purely serial code.
Convenience in separating logically independent sequences of instructions. This is true even on a single core machine. This gives you logical concurrency without necessarily parallelism.
Having multiple threads allows either the operating system or a user-level threading library to multiplex multiple logical threads over a smaller number of CPU cores, without the application developer having to worry about other threads and processes.
Taking advantage of multiple cores / processors. Threads allow you to scale your execution to the number of CPU cores you have, enabling parallelism.
Your example is a little contrived because the entire thread's execution is locked. Normally, threads perform many actions independently and only take a mutex when accessing a shared resource.
More specifically, under your scenario you would not gain any performance. However, if your entire thread was not under a mutex, then you could potentially gain efficiency. I say potentially because there are overheads to running multiple threads which may offset any efficiency gain you obtain.
Threads theoretically run simultaneously, it means that threads could write to the same memory block at the same time. For example, if you have a global var int i;, and two threads try to write different values at same time, which one value remains in i?
Mutex forces synchronous access to memory, inside a mutex block (mutex.lock & mutex.unlock) you warrant synchronous memory access and avoid memory corruption.
When you call mtx.lock(), JUST ONE THREAD KEEPS RUNNING, and any other thread calling the same mtx.lock() stops, waiting for mtx.unlock call.
I'm using pthread on Linux. I have a circular buffer to pass data from one thread to another. Maybe the circular buffer is not the best structure to use here, but changing that would not make my problem go away, so we'll just refer it as a queue.
Whenever my queue is either full or empty, pop/push operations return NULL. This is problematic since my threads fire periodically. Waiting for another thread loop would take too long.
I've tried using semaphores (sem_post, sem_wait) but unlocking under contention takes up to 25 ms, which is about the speed of my loop. I've tried waiting with pthread_cond_t, but the unlocking takes up to between 10 and 15 ms.
Is there a faster mechanism I could use to wait for data?
EDIT*
Ok I used condition variables. I'm on an embedded device so adding "more cores or cpu power" is not an option. This made me realise I had all sorts of thread priorities set all over the place so I'll sort this out before going further
You should use condition variables. The only faster ways are platform-specific, and they're only negligibly faster.
You're seeing what you think is poor performance simply because your threads are being de-scheduled. You're seeing long "delays" when your thread is near the end of its timeslice and the scheduler allows the unblocked thread to pre-empt the running thread. If you have more cores than threads or set your thread to a higher priority, you won't see these delays.
But these delays are actually a good thing, and you shouldn't be concerned about them. Other threads just get a chance to run too.