Multithreaded Programming Guide

Data Locking

Data locking guarantees that access to a collection of data is maintained consistently. For data locking, the concept of locking code is still there, but code locking is around references to shared (global) data, only. For a mutual exclusion locking protocol, only one thread can be in the critical section for each collection of data.

Alternatively, in a multiple readers, single writer protocol, several readers can be allowed for each collection of data or one writer. Multiple threads can execute in a single module when they operate on different data collections and do not conflict on a single collection for the multiple readers, single writer protocol. So, data locking typically allows more concurrency than does code locking.

What strategy should you use when using locks (whether implemented with mutexes, condition variables, or semaphores) in a program? Should you try to achieve maximum parallelism by locking only when necessary and unlocking as soon as possible (fine-grained locking)? Or should you hold locks for long periods to minimize the overhead of taking and releasing them (coarse-grained locking)?

The granularity of the lock depends on the amount of data it protects. A very coarse-grained lock might be a single lock to protect all data. Dividing how the data is protected by the appropriate number of locks is very important. Too fine a grain of locking can degrade performance. The overhead associated with acquiring and releasing locks can become significant when there are too many locks.

The common wisdom is to start with a coarse-grained approach, identify bottlenecks, and add finer-grained locking where necessary to alleviate the bottlenecks. This is reasonably sound advice, but use your own judgment about finding the balance between maximizing parallelism and minimizing lock overhead.