the same time and find it unexpectedly changing underfoot. Bugs caused by race conditions can be very difficult to reproduce and isolate. To prevent this, threading application programming interfaces (APIs) offer synchronization primitives such as mutexes to lock data structures against concurrent access. On uniprocessor systems, a thread running into a locked mutex must sleep and hence trigger a context switch. On multi-processor systems, the thread may instead poll the mutex in a spinlock. Both of these may sap performance and force processors in symmetric multiprocessing (SMP) systems to contend for the memory bus, especially if the granularity of the locking is too fine. Other synchronization APIs include condition variables, critical sections, semaphores, and monitors. Thread pools A popular programming pattern involving threads is that of thread pools where a set number of threads are created at startup that then wait for a task to be assigned. When a new task arrives, it wakes up, completes the task and goes back to waiting. This avoids the relatively expensive thread creation and destruction functions for every task performed and takes thread management out of the application developer's hand and leaves it to a library or the operating system that is better suited to optimize thread management. Multithreaded programs vs single-threaded programs pros and cons Multithreaded applications have the following advantages vs single-threaded ones: Responsiveness: multithreading can allow an application to remain responsive to input. In a one-thread program, if the main execution thread blocks on a long-running task, the entire application can appear to freeze. By moving such long-running tasks to a worker thread that runs concurrently with the main execution thread, it is possible for the application to remain responsive to user input while executing tasks in the background. On the other hand, in most cases multithreading is not the only way to keep a program responsive, with non-blocking I/O and/or Unix signals being available for obtaining similar results. Parallelization: applications looking to use multicore or multi-CPU systems can use multithreading to split data and tasks into parallel subtasks and let the underlying architecture manage how the threads run, either concurrently on one core or in parallel on multiple cores. GPU computing environments like CUDA and OpenCL use the multithreading model where dozens to hundreds of threads run in parallel across data on a large number of cores. This, in turn, enables better system utilization, and (provided that synchronization costs