How does Go support parallelism, and what are the various techniques and strategies for improving the performance of concurrent programs by leveraging parallelism?
In Go, parallelism is achieved by dividing a problem into smaller tasks that can be executed simultaneously across multiple CPUs or processor cores. This can improve the performance of concurrent programs by allowing them to execute multiple tasks in parallel, rather than sequentially.
Go provides several mechanisms for achieving parallelism, including:
Goroutines: Goroutines allow multiple functions to execute concurrently within a single program. By default, Goroutines are multiplexed onto multiple OS threads, which allows them to run in parallel.
Channels: Channels can be used to synchronize the execution of Goroutines, allowing them to communicate and share data safely and efficiently.
WaitGroups: WaitGroups can be used to wait for all Goroutines to finish before continuing with the rest of the program.
Mutexes and RWMutexes: Mutexes and RWMutexes can be used to synchronize access to shared resources, ensuring that only one Goroutine can access a resource at a time.
Atomic operations: Atomic operations can be used to perform basic read-modify-write operations atomically, ensuring that multiple Goroutines can modify the same data without causing race conditions.
To improve the performance of concurrent programs, it is important to carefully manage the number of Goroutines and threads being used, and to balance the workload across multiple CPUs or processor cores. This can be achieved using techniques such as load balancing and parallelization, which involve dividing the workload into smaller, independent tasks that can be executed in parallel across multiple cores. Other techniques such as memoization, caching, and pipelining can also be used to improve the performance of concurrent programs.