I was reading how Golang internally manages new created goroutine in the application. And I come to know runtime scheduler use to queue to manage the created goroutines.
Global run queue: All newly created goroutine is placed to this queue.
Local run queue: All go routine which is about to run is allocated to local run queue and from there scheduler will assign it to OS thread.
So, Here my question is why scheduler is using two queues to manage goroutine. Why can't they just use global run queue and from there scheduler will map it to OS thread.
CodePudding user response：
First, please note that this blog is an unofficial and old source, so the information in it shouldn't be taken as totally accurate with respect to the current version of Go (or any version, for that matter). You can still learn from it, but the Go scheduler is improved over time, which can make information out of date. For example, the blog says "Go scheduler is not a preemptive scheduler but a cooperating scheduler". As of Go 1.14, this is no longer true as preemption was added to the runtime. As for the other information, I won't vouch for it's accuracy, but here's an explanation of what they say.
Reading the blog post:
There are two different run queues in the Go scheduler: the Global Run Queue (GRQ) and the Local Run Queue (LRQ). Each P is given a LRQ that manages the Goroutines assigned to be executed within the context of a P. These Goroutines take turns being context-switched on and off the M assigned to that P. The GRQ is for Goroutines that have not been assigned to a P yet. There is a process to move Goroutines from the GRQ to a LRQ that we will discuss later.
This means the GRQ is for Goroutines that haven't been assigned to run yet, the LRQ is for Goroutines that have been assigned to a P to run or have already begun executing. Each Goroutine will start on the GRQ, and join a LRQ later to begin executing.
Here is the process that the previous quote was referencing, where Goroutines are moved from the GRQ to LRQ:
In figure 10, P1 has no more Goroutines to execute. But there are Goroutines in a runnable state, both in the LRQ for P2 and in the GRQ. This is a moment where P1 needs to steal work. The rules for stealing work are as follows.
runtime.schedule() { // only 1/61 of the time, check the global runnable queue for a G. // if not found, check the local queue. // if not found, // try to steal from other Ps. // if not, check the global runnable queue. // if not found, poll network. }
This means a P will prioritize running goroutines in their own LRQ, then from other P's LRQ, then from the GRQ, then from network polling. There is also a small chance to immediately run a Goroutine from the GRQ immediately. By having multiple queues, it allows this priority system to be constructed.
Why do we want priority in which goroutines get run? It may have various performance benefits. For example, it could make better use of the CPU cache. If you run a Goroutine that was already running recently, it's more likely that the data it's working with is still in the CPU cache, making it fast to access. When you start up a new Goroutine, it may use or create data that isn't in the cache yet. That data will then enter the cache and could evict the data being used by another Goroutine, which in turn causes that Goroutine to be slower when it resumes again. In the pathological case, this is called cache thrashing, and greatly reduces the effective speed of the processor.
Allowing the CPU cache to work effectively can be one of the most important factors in achieving high performance on modern processors, but it's not the only reason to have such a queue system. In general, the more logical processes that are running at the same time (such as Goroutines in a Go program), the more resource contention will occur. This is because the resources used by a process tend to be fairly stable over the runtime of the process. In other words, every time you start a new process tends to increase the overall resource load, while continuing an already started process tends to maintain the resource load, and finishing a process tends to reduce the resource load. Therefore, prioritizing already running processes over new processes would tend to help keep the resource load in a manageable range.
It's analogous to the practical advice of "finish what you started". If you have a lot of tasks to accomplish, it's more effective to complete them one at a time, or multitask just a handful of things if you can. If you just keep starting new tasks and never finished the previous ones, eventually you have so many things going on at the same time that you feel overwhelmed.