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A few multi-core processors have a "power-conscious spin-lock" instruction that puts a processor to sleep, then wakes it up on the next cycle after the lock is freed. A spin-lock using such instructions is more efficient and uses less energy than spin locks with or without a back-off loop. [6]
Busy-waiting itself can be made much less wasteful by using a delay function (e.g., sleep()) found in most operating systems. This puts a thread to sleep for a specified time, during which the thread will waste no CPU time. If the loop is checking something simple then it will spend most of its time asleep and will waste very little CPU time.
Generally, locks are advisory locks, where each thread cooperates by acquiring the lock before accessing the corresponding data. Some systems also implement mandatory locks, where attempting unauthorized access to a locked resource will force an exception in the entity attempting to make the access. The simplest type of lock is a binary ...
The differences between the two approaches are quite small. Read-side locking moves to rcu_read_lock and rcu_read_unlock, update-side locking moves from a reader-writer lock to a simple spinlock, and a synchronize_rcu precedes the kfree. However, there is one potential catch: the read-side and update-side critical sections can now run concurrently.
The downside is that write-preferring locks allows for less concurrency in the presence of writer threads, compared to read-preferring RW locks. Also the lock is less performant because each operation, taking or releasing the lock for either read or write, is more complex, internally requiring taking and releasing two mutexes instead of one.
However, the lock-in effect had only a modest impact on home prices: It pushed costs up by 5.7 percent. At the same time, elevated mortgage rates reduced prices by an estimated 3.3 percent ...
The wake-sleep algorithm [1] is an unsupervised learning algorithm for deep generative models, especially Helmholtz Machines. [2] The algorithm is similar to the expectation-maximization algorithm , [ 3 ] and optimizes the model likelihood for observed data. [ 4 ]
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