Home Backend Development Golang Goroutines vs. Threads: How Do Go's Goroutines Map to Kernel Threads and Affect CPU Utilization?

Goroutines vs. Threads: How Do Go's Goroutines Map to Kernel Threads and Affect CPU Utilization?

Dec 11, 2024 pm 02:35 PM

Goroutines vs. Threads: How Do Go's Goroutines Map to Kernel Threads and Affect CPU Utilization?

Goroutines vs. Threads: Clarifying the Kernel and User State Relationship

In the realm of programming, understanding the concepts of goroutines, user threads, and kernel threads is crucial. To shed light on these concepts, let's explore the following questions:

  1. What is the relationship between OS threads and goroutines?

The "effective Go" document introduces goroutines, leading to confusion about the meaning of OS threads. Within the context of the paper, OS threads refer to kernel threads.

  1. Why does the number of processors (P) equal the number of CPUs?

According to the "go-scheduler" paper, the number of processors (P) reflects the scheduling contexts available to the operating system. These contexts are tied to CPU cores to ensure efficient resource utilization. However, other programs in the system can still access CPU time as the kernel scheduler manages the distribution of resources.

  1. How many kernel threads are generated by the OS?

The number of kernel threads generated by the operating system varies based on demand. The "ps -eL" command can be used to verify the actual number of kernel threads running on a specific system.

Further Explanation

Goroutines, or user threads, are lightweight, concurrent entities that reside within a Go program. They are mapped to OS threads (kernel threads) through a mechanism known as "P," ensuring that they can run on multiple processors simultaneously. The number of "P" is typically set to the number of available CPUs.

It's important to note that the number of available CPUs does not restrict the number of tasks that can be executed concurrently. Concurrency often involves significant waiting for input/output (IO) operations. Even highly compute-intensive tasks will be interrupted by the kernel scheduler to allow other processes to run.

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