Concurrency and parallelism are key to improving application performance. Go, with its efficient concurrency model, allows developers to create scalable, high-performance applications.

1. Concurrency

Concurrency refers to the ability of a program to make progress on multiple tasks simultaneously, but not necessarily in parallel. Concurrency involves managing multiple tasks that are being executed in an overlapping manner, often on a single CPU core. In essence, concurrency allows a program to handle multiple tasks at the same time by switching between them, making the program appear as if it’s running multiple tasks concurrently.

How Concurrency Works in Go?

Go handles concurrency using Goroutines, which are lightweight threads managed by the Go runtime. Goroutines are multiplexed onto a smaller number of operating system threads, allowing Go to manage many tasks concurrently without the overhead of creating a separate thread for each task.

The Go scheduler plays a key role in concurrency by determining which Goroutine should run at any given time. The scheduler manages task-switching between Goroutines, ensuring that the CPU resources are efficiently utilized.

Example:

func task(id int) {
    fmt.Printf("Task %d is starting\n", id)
    time.Sleep(2 * time.Second)
    fmt.Printf("Task %d is completed\n", id)
}

func main() {
    go task(1)
    go task(2)

    time.Sleep(3 * time.Second) // Waiting for both tasks to complete
    fmt.Println("All tasks completed concurrently!")
}

In this example, two tasks run concurrently. Although they don’t run simultaneously (because there’s only one CPU core in this case), they’re executed in an overlapping manner, giving the appearance of concurrency.

2. Parallelism

Parallelism in computing refers to the execution of multiple tasks simultaneously, typically across multiple processors or CPU cores. Unlike concurrency, where tasks are handled in an overlapping manner, parallelism allows tasks to be executed literally at the same time, each on a separate processing unit.

How Parallelism Works in Go?

In Go, parallelism is achieved by running multiple Goroutines on different processors or CPU cores. Go's runtime system, coupled with the Go scheduler, distributes Goroutines efficiently across the available cores, enabling true parallel execution.

For instance, a task that requires intensive computation, such as processing large datasets or performing complex calculations, can benefit from parallel execution by breaking down the work into smaller tasks that are run simultaneously on multiple CPU cores. Go's ability to handle parallelism effortlessly makes it an ideal choice for high-performance applications, such as data analytics, real-time processing, and scientific computing.

Example:

func main() {
    var wg sync.WaitGroup
    cores := 4

    // Creating 4 Goroutines to simulate parallelism
    for i := 0; i < cores; i++ {
        wg.Add(1)
        go func(id int) {
            defer wg.Done()
            fmt.Printf("Running task on Goroutine %d\n", id)
        }(i)
    }

    wg.Wait() // Wait for all Goroutines to complete
    fmt.Println("All tasks completed in parallel!")
}

In this example, Go schedules four Goroutines to run simultaneously, each performing a task on a separate CPU core.

Concurrency vs Parallelism in Golang

Concurrency and Parallelism

Go (Golang) is designed for concurrency, making it easy to write concurrent applications. It uses goroutines, lightweight functions that run concurrently, and channels, which allow communication and synchronization between goroutines.

• Goroutines: Efficient, independently scheduled tasks managed by Go's runtime.
• Channels: A way for goroutines to communicate and coordinate.

Go also supports parallelism by utilizing multiple CPU cores, distributing goroutines across them for better performance. Go simplifies both concurrency with goroutines and channels, and parallelism using multi-core processing, making it efficient for scalable applications.