This is an exam question (practice exam, not the real one). It's about concurrent programming using a multi-core processor and the problems with using locks.
"In concurrent programming is it possible that, by using locks, a program might sometimes use more processors than are necessary?"
In other words, is this ever possible? It's a true/false question. I can't find an answer anywhere and I'm revising for my exams.
The concurrent program with N threads of execution using locks at any point in time can have M=0 .. N-1 threads waiting for locks; thus this program can only be utilizing N-M processors since waiting for a lock does not require a processor.
Thus, no, using locks does not increase the number of processors required by a concurrent program.
With an efficient implementation of multi-threading and locks, if a thread blocks waiting for a lock for any significant time, the scheduler / lock implementation will reassign the core to do something else.
But since the exam question is asking if it is ever possible to use more processors than are strictly necessary, the answer is that it depends on the implementation of threads / locks / scheduling. For instance, there is a kind of lock called a spinlock where the lock implementation does NOT surrender control of the processor while waiting to acquire a lock. Instead, it polls the lock in a tight loop trying to acquire it.
Why would you do that? Well, if the lock is likely to become available in a short enough period of time, then the CPU wasted "spinning" on the lock is less than would be spent on performing a full context switch.
So I don't think your exam question has a simple yes / no answer.
We have code that makes use of sched_yield inside a loop. When we do this we seem to get a slower performance of other threads, in particular those involving kernel calls (like IO and mutex/event handling). I'm trying to determine the exact cause of this behaviour.
Can excessive calls to sched_yield lead to a bottleneck in the kernel?
My suspicion is if we keep asking the kernel to check its process list then other threads will suffer as key data structures may be continually locked -- whereas if we didn't call sched_yield those kernel locks would tend to be uncontested. Does this make sense, or should it be totally okay to repeatedly call sched_yield.
Have a look at the sched_yield man page for Linux:
Avoid calling sched_yield()
unnecessarily or inappropriately (e.g., when resources needed by other
schedulable threads are still held by the caller), since doing so will result
in unnecessary context switches, which will degrade system performance.
Calling it in a tight loop will cause problems. Reduce the rate at which you're calling it.
(And check that you need to call it in the first place. The scheduler often does the Right Thing all by itself.)
Other options you could find interesting to investigate if you have a low priority thread:
sched_setscheduler - with SCHED_IDLE or SCHED_BATCH maybe (affects the whole process)
thread_setschedparam - per thread, but might have restrictions on what policies you can use (can't find it right now).
Or the good old nice command of course.
The Windows API provides critical sections in which a waiting thread will spin a limited amount of times before context switching, but only on a multiprocessor system. These are implemented using InitializeCriticalSectionAndSpinCount. (See http://msdn.microsoft.com/en-us/library/ms682530.aspx.) This is efficient when you have a critical section that will often only be locked for a short period of time and therefore contention should not immediately trigger a context switch. Two related questions:
For a high-level, cross-platform threading library or an implementation of a synchronized block, is having a small amount of spinning before triggering a context switch a good default?
What, if anything, is the equivalent to InitializeCriticalSectionAndSpinCount on other OS's, especially Posix?
Edit: Of course no spin count will be optimal for all cases. I'm only interested in whether using a nonzero spin count would be a better default than not using one.
My opinion is that the optimal "spin-count" for best application performance is too hardware-dependent for it to be an important part of a cross-platform API, and you should probably just use mutexes (in posix, pthread_mutex_init / destroy / lock / trylock) or spin-locks (pthread_spin_init / destroy / lock / trylock). Rationale follows.
What's the point of the spin count? Basically, if the lock owner is running simultaneously with the thread attempting to acquire the lock, then the lock owner might release the lock quickly enough that the EnterCriticalSection caller could avoid giving up CPU control in acquiring the lock, improving that thread's performance, and avoiding context switch overhead. Two things:
1: obviously this relies on the lock owner running in parallel to the thread attempting to acquire the lock. This is impossible on a single execution core, which is almost certainly why Microsoft treats the count as 0 in such environments. Even with multiple cores, it's quite possible that the lock owner is not running when another thread attempts to acquire the lock, and in such cases the optimal spin count (for that attempt) is still 0.
2: with simultaneous execution, the optimal spin count is still hardware dependent. Different processors will take different amounts of time to perform similar operations. They have different instruction sets (the ARM I work with most doesn't have an integer divide instruction), different cache sizes, the OS will have different pages in memory... Decrementing the spin count may take a different amount of time on a load-store architecture than on an architecture in which arithmetic instructions can access memory directly. Even on the same processor, the same task will take different amounts of time, depending on (at least) the contents and organization of the memory cache.
If the optimal spin count with simultaneous execution is infinite, then the pthread_spin_* functions should do what you're after. If it is not, then use the pthread_mutex_* functions.
For a high-level, cross-platform threading library or an
implementation of a synchronized block, is having a small amount of
spinning before triggering a context switch a good default?
One would think so. Many moons ago, Solaris 2.x implemented adaptive locks, which did exactly this - spin for a while, if the mutex is held by a thread executing on another CPU or block otherwise.
Obviously, it makes no sense to spin on single-CPU systems.
As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, visit the help center for guidance.
Closed 10 years ago.
I am applying my new found knowledge of threading everywhere and getting lots of surprises
Example:
I used threads to add numbers in an
array. And outcome was different every
time. The problem was that all of my
threads were updating the same
variable and were not synchronized.
What are some known thread issues?
What care should be taken while using
threads?
What are good multithreading resources.
Please provide examples.
sidenote:(I renamed my program thread_add.java to thread_random_number_generator.java:-)
In a multithreading environment you have to take care of synchronization so two threads doesn't clobber the state by simultaneously performing modifications. Otherwise you can have race conditions in your code (for an example see the infamous Therac-25 accident.) You also have to schedule the threads to perform various tasks. You then have to make sure that your synchronization and scheduling doesn't cause a deadlock where multiple threads will wait for each other indefinitely.
Synchronization
Something as simple as increasing a counter requires synchronization:
counter += 1;
Assume this sequence of events:
counter is initialized to 0
thread A retrieves counter from memory to cpu (0)
context switch
thread B retrieves counter from memory to cpu (0)
thread B increases counter on cpu
thread B writes back counter from cpu to memory (1)
context switch
thread A increases counter on cpu
thread A writes back counter from cpu to memory (1)
At this point the counter is 1, but both threads did try to increase it. Access to the counter has to be synchronized by some kind of locking mechanism:
lock (myLock) {
counter += 1;
}
Only one thread is allowed to execute the code inside the locked block. Two threads executing this code might result in this sequence of events:
counter is initialized to 0
thread A acquires myLock
context switch
thread B tries to acquire myLock but has to wait
context switch
thread A retrieves counter from memory to cpu (0)
thread A increases counter on cpu
thread A writes back counter from cpu to memory (1)
thread A releases myLock
context switch
thread B acquires myLock
thread B retrieves counter from memory to cpu (1)
thread B increases counter on cpu
thread B writes back counter from cpu to memory (2)
thread B releases myLock
At this point counter is 2.
Scheduling
Scheduling is another form of synchronization and you have to you use thread synchronization mechanisms like events, semaphores, message passing etc. to start and stop threads. Here is a simplified example in C#:
AutoResetEvent taskEvent = new AutoResetEvent(false);
Task task;
// Called by the main thread.
public void StartTask(Task task) {
this.task = task;
// Signal the worker thread to perform the task.
this.taskEvent.Set();
// Return and let the task execute on another thread.
}
// Called by the worker thread.
void ThreadProc() {
while (true) {
// Wait for the event to become signaled.
this.taskEvent.WaitOne();
// Perform the task.
}
}
You will notice that access to this.task probably isn't synchronized correctly, that the worker thread isn't able to return results back to the main thread, and that there is no way to signal the worker thread to terminate. All this can be corrected in a more elaborate example.
Deadlock
A common example of deadlock is when you have two locks and you are not careful how you acquire them. At one point you acquire lock1 before lock2:
public void f() {
lock (lock1) {
lock (lock2) {
// Do something
}
}
}
At another point you acquire lock2 before lock1:
public void g() {
lock (lock2) {
lock (lock1) {
// Do something else
}
}
}
Let's see how this might deadlock:
thread A calls f
thread A acquires lock1
context switch
thread B calls g
thread B acquires lock2
thread B tries to acquire lock1 but has to wait
context switch
thread A tries to acquire lock2 but has to wait
context switch
At this point thread A and B are waiting for each other and are deadlocked.
There are two kinds of people that do not use multi threading.
1) Those that do not understand the concept and have no clue how to program it.
2) Those that completely understand the concept and know how difficult it is to get it right.
I'd make a very blatant statement:
DON'T use shared memory.
DO use message passing.
As a general advice, try to limit the amount of shared state and prefer more event-driven architectures.
I can't give you examples besides pointing you at Google. Search for threading basics, thread synchronisation and you'll get more hits than you know.
The basic problem with threading is that threads don't know about each other - so they will happily tread on each others toes, like 2 people trying to get through 1 door, sometimes they will pass though one after the other, but sometimes they will both try to get through at the same time and will get stuck. This is difficult to reproduce, difficult to debug, and sometimes causes problems. If you have threads and see "random" failures, this is probably the problem.
So care needs to be taken with shared resources. If you and your friend want a coffee, but there's only 1 spoon you cannot both use it at the same time, one of you will have to wait for the other. The technique used to 'synchronise' this access to the shared spoon is locking. You make sure you get a lock on the shared resource before you use it, and let go of it afterwards. If someone else has the lock, you wait until they release it.
Next problem comes with those locks, sometimes you can have a program that is complex, so much that you get a lock, do something else then access another resource and try to get a lock for that - but some other thread has that 2nd resource, so you sit and wait... but if that 2nd thread is waiting for the lock you hold for the 1st resource.. it's going to sit and wait. And your app just sits there. This is called deadlock, 2 threads both waiting for each other.
Those 2 are the vast majority of thread issues. The answer is generally to lock for as short a time as possible, and only hold 1 lock at a time.
I notice you are writing in java and that nobody else mentioned books so Java Concurrency In Practice should be your multi-threaded bible.
-- What are some known thread issues? --
Race conditions.
Deadlocks.
Livelocks.
Thread starvation.
-- What care should be taken while using threads? --
Using multi-threading on a single-processor machine to process multiple tasks where each task takes approximately the same time isn’t always very effective.For example, you might decide to spawn ten threads within your program in order to process ten separate tasks. If each task takes approximately 1 minute to process, and you use ten threads to do this processing, you won’t have access to any of the task results for the whole 10 minutes. If instead you processed the same tasks using just a single thread, you would see the first result in 1 minute, the next result 1 minute later, and so on. If you can make use of each result without having to rely on all of the results being ready simultaneously, the single
thread might be the better way of implementing the program.
If you launch a large number of threads within a process, the overhead of thread housekeeping and context switching can become significant. The processor will spend considerable time in switching between threads, and many of the threads won’t be able to make progress. In addition, a single process with a large number of threads means that threads in other processes will be scheduled less frequently and won’t receive a reasonable share of processor time.
If multiple threads have to share many of the same resources, you’re unlikely to see performance benefits from multi-threading your application. Many developers see multi-threading as some sort of magic wand that gives automatic performance benefits. Unfortunately multi-threading isn’t the magic wand that it’s sometimes perceived to be. If you’re using multi-threading for performance reasons, you should measure your application’s performance very closely in several different situations, rather than just relying on some non-existent magic.
Coordinating thread access to common data can be a big performance killer. Achieving good performance with multiple threads isn’t easy when using a coarse locking plan, because this leads to low concurrency and threads waiting for access. Alternatively, a fine-grained locking strategy increases the complexity and can also slow down performance unless you perform some sophisticated tuning.
Using multiple threads to exploit a machine with multiple processors sounds like a good idea in theory, but in practice you need to be careful. To gain any significant performance benefits, you might need to get to grips with thread balancing.
-- Please provide examples. --
For example, imagine an application that receives incoming price information from
the network, aggregates and sorts that information, and then displays the results
on the screen for the end user.
With a dual-core machine, it makes sense to split the task into, say, three threads. The first thread deals with storing the incoming price information, the second thread processes the prices, and the final thread handles the display of the results.
After implementing this solution, suppose you find that the price processing is by far the longest stage, so you decide to rewrite that thread’s code to improve its performance by a factor of three. Unfortunately, this performance benefit in a single thread may not be reflected across your whole application. This is because the other two threads may not be able to keep pace with the improved thread. If the user interface thread is unable to keep up with the faster flow of processed information, the other threads now have to wait around for the new bottleneck in the system.
And yes, this example comes directly from my own experience :-)
DONT use global variables
DONT use many locks (at best none at all - though practically impossible)
DONT try to be a hero, implementing sophisticated difficult MT protocols
DO use simple paradigms. I.e share the processing of an array to n slices of the same size - where n should be equal to the number of processors
DO test your code on different machines (using one, two, many processors)
DO use atomic operations (such as InterlockedIncrement() and the like)
YAGNI
The most important thing to remember is: do you really need multithreading?
I agree with pretty much all the answers so far.
A good coding strategy is to minimise or eliminate the amount of data that is shared between threads as much as humanly possible. You can do this by:
Using thread-static variables (although don't go overboard on this, it will eat more memory per thread, depending on your O/S).
Packaging up all state used by each thread into a class, then guaranteeing that each thread gets exactly one state class instance to itself. Think of this as "roll your own thread-static", but with more control over the process.
Marshalling data by value between threads instead of sharing the same data. Either make your data transfer classes immutable, or guarantee that all cross-thread calls are synchronous, or both.
Try not to have multiple threads competing for the exact same I/O "resource", whether it's a disk file, a database table, a web service call, or whatever. This will cause contention as multiple threads fight over the same resource.
Here's an extremely contrived OTT example. In a real app you would cap the number of threads to reduce scheduling overhead:
All UI - one thread.
Background calcs - one thread.
Logging errors to a disk file - one thread.
Calling a web service - one thread per unique physical host.
Querying the database - one thread per independent group of tables that need updating.
Rather than guessing how to do divvy up the tasks, profile your app and isolate those bits that are (a) very slow, and (b) could be done asynchronously. Those are good candidates for a separate thread.
And here's what you should avoid:
Calcs, database hits, service calls, etc - all in one thread, but spun up multiple times "to improve performance".
Don't start new threads unless you really need to. Starting threads is not cheap and for short running tasks starting the thread may actually take more time than executing the task itself. If you're on .NET take a look at the built in thread pool, which is useful in a lot of (but not all) cases. By reusing the threads the cost of starting threads is reduced.
EDIT: A few notes on creating threads vs. using thread pool (.NET specific)
Generally try to use the thread pool. Exceptions:
Long running CPU bound tasks and blocking tasks are not ideal run on the thread pool cause they will force the pool to create additional threads.
All thread pool threads are background threads, so if you need your thread to be foreground, you have to start it yourself.
If you need a thread with different priority.
If your thread needs more (or less) than the standard 1 MB stack space.
If you need to be able to control the life time of the thread.
If you need different behavior for creating threads than that offered by the thread pool (e.g. the pool will throttle creating of new threads, which may or may not be what you want).
There are probably more exceptions and I am not claiming that this is the definitive answer. It is just what I could think of atm.
I am applying my new found knowledge of threading everywhere
[Emphasis added]
DO remember that a little knowledge is dangerous. Knowing the threading API of your platform is the easy bit. Knowing why and when you need to use synchronisation is the hard part. Reading up on "deadlocks", "race-conditions", "priority inversion" will start you in understanding why.
The details of when to use synchronisation are both simple (shared data needs synchronisation) and complex (atomic data types used in the right way don't need synchronisation, which data is really shared): a lifetime of learning and very solution specific.
An important thing to take care of (with multiple cores and CPUs) is cache coherency.
I am surprised that no one has pointed out Herb Sutter's Effective Concurrency columns yet. In my opinion, this is a must read if you want to go anywhere near threads.
a) Always make only 1 thread responsible for a resource's lifetime. That way thread A won't delete a resource thread B needs - if B has ownership of the resource
b) Expect the unexpected
DO think about how you will test your code and set aside plenty of time for this. Unit tests become more complicated. You may not be able to manually test your code - at least not reliably.
DO think about thread lifetime and how threads will exit. Don't kill threads. Provide a mechanism so that they exit gracefully.
DO add some kind of debug logging to your code - so that you can see that your threads are behaving correctly both in development and in production when things break down.
DO use a good library for handling threading rather than rolling your own solution (if you can). E.g. java.util.concurrency
DON'T assume a shared resource is thread safe.
DON'T DO IT. E.g. use an application container that can take care of threading issues for you. Use messaging.
In .Net one thing that surprised me when I started trying to get into multi-threading is that you cannot straightforwardly update the UI controls from any thread other than the thread that the UI controls were created on.
There is a way around this, which is to use the Control.Invoke method to update the control on the other thread, but it is not 100% obvious the first time around!
Don't be fooled into thinking you understand the difficulties of concurrency until you've split your head into a real project.
All the examples of deadlocks, livelocks, synchronization, etc, seem simple, and they are. But they will mislead you, because the "difficulty" in implementing concurrency that everyone is talking about is when it is used in a real project, where you don't control everything.
While your initial differences in sums of numbers are, as several respondents have pointed out, likely to be the result of lack of synchronisation, if you get deeper into the topic, be aware that, in general, you will not be able to reproduce exactly the numeric results you get on a serial program with those from a parallel version of the same program. Floating-point arithmetic is not strictly commutative, associative, or distributive; heck, it's not even closed.
And I'd beg to differ with what, I think, is the majority opinion here. If you are writing multi-threaded programs for a desktop with one or more multi-core CPUs, then you are working on a shared-memory computer and should tackle shared-memory programming. Java has all the features to do this.
Without knowing a lot more about the type of problem you are tackling, I'd hesitate to write that 'you should do this' or 'you should not do that'.
Why lock may become a bottleneck of multithreaded program?
If I want my queue frequently pop() and push() by multithread,
which lock should I use?
The lock you use depends on your platform but will generally be some flavour of mutex. On windows, you would use a critical section and in .NET, you'd use a monitor. I'm not very familiar with locking mechanisms on other platforms. I'd stay away from lock free approaches. They are very difficult to program correctly and the performance gains are often not as great as you would expect.
Locks become a bottleneck in your program when they are under heavy contention. That is, a very large number of threads all try to acquire the lock at the same time. This wastes a lot of CPU cycles as threads become blocked and the OS spends a greater and greater portion of its time switching between threads. This sort of problem most frequently manifests itself in the server world. For desktop applications, it's rare that locks will cause a performance issue.
"Why lock may become a bottleneck of multithreaded program?" - think of a turnstile (also called a baffle gate), which only lets one person through at a time, with a crowd of people waiting to go through it.
For a queue, use the simplest lock your environment has to offer.
For a queue, it is easy to write a lock-free implementation (google away)
Locks are bottlenecks because they force all other threads which encounter them to stop doing what they're doing and wait for the lock to open, thus wasting time. One of the ideas behind multithreading is to use as many processors as possible at any given time. By forcing threads to wait on the locks the application essentially gives up processing power which it might have used.
"Why lock may become a bottleneck of multithreaded program?"
Because waiting threads remain blocked until shared memory is unlocked.
Suggest you read this article on "Concurrency: What Every Dev Must Know About Multithreaded Apps" http://msdn.microsoft.com/en-au/magazine/cc163744.aspx
Locks are expensive both because they require operating system calls in the middle of your algorithm and because they are hard to do properly when creating the CPU.
As a programmer, it is best to leave the locks in the middle of your data structures to the experts and instead use a good multithreaded library such as Intel's TBB
For Queues, you would want to use Atomic instructions (hard) or a spinlock (easier) if possible because they are cheap compared to a mutex. Use a mutex if you are doing a lot of work that needs to be locked, i.e modify a complex tree structure
In the threading packages that I'm familiar with, your options for mutexes are recursive and non-recursive. You should opt for non-recursive -- all of your accesses will be lock(); queue_op(); unlock(), so there's no need to be able to acquire the lock twice.