Locks and concurrency

This is the fifth post in my series on Grokking xv6. We look at concurrent processes, what locks are and how they can used, and finally we look at how locks are implemented in xv6.

In the most straightforward mental model of how code executes, you probably think of lines of code as executing one after another, serially. This is correct for single-processor CPUs, but as soon as you have multiple cores this assumption is no longer true.

Let’s say we have two threads of execution, A and B, executing two statements each.

Thread A    Thread B
a1          b1
a2          b2

The only guarantees we can make are that a1 happens before a2, a1 < a2, and b1 < b2. We have many possibilties for the event ordering between all of the events:

a1 < a2 < b1 < b2
a1 < b1 < a2 < b2
a1 < b1 < b2 < a2
b1 < a1 < a2 < b2
b1 < a1 < b2 < b2
b1 < b2 < a1 < a2

All of these possible event orderings can - and probably will - happen, given that the program is running for a long enough time period. This non-determinsm makes it hard to reason about and debug concurrent code.

There are many ways to be clever and rigorous about concurrent code using various concurrency primitives, but we are just going to talk about one: the lock, or mutex (from mutual exclusion). What do all these concurrency primitives do? They provide concurrent control, just like there are if-statements and while loops in non-concurrent code.


Normally it’s a good idea to write code that doesn’t use shared memory. In the example above, if none of A’s code depends on B’s code and vice versa, it doesn’t matter what the exact ordering is. This is the common case when we have multiple processes running.

In user-space, the only reason to run a program on multiple cores is performance, and specifically when your program is CPU-bound as opposed to being disk or I/O-bound.

In the kernel there is shared memory everywhere - process tables, file tables, memory free lists, etc. Even if we have only a single core, hardware interrupts (for example, when you press a key) change the flow of execution in non-obvious ways. By using locks we can protect a piece of code by maintaining important invariants over it.

How do locks work? Let’s say we have a chained hash table with N buckets that each have a linked lists of entries, and we want to run with multiple threads for performance reasons. We might notice that there’s a problem with missing entries when we let multiple threads modify the hash table, so we can put a lock around this critical section:

    // code from ph.c in my xv6 repo

    put(keys[b*n + i], n);

Here the critical section is the line between lock and unlock statements. Because the lock is locked only a single thread can enter into the critical section at any given time. This way we don’t get any lost updates but we still get performance gains, because we can get entries in parallel.

Problem for the author: In a lecture Robert Morris seems to say there are problems with not locking the getting part too. I don’t see how. Did I misunderstand what he was saying or is there an insight I’m missing? See note at the end for an answer.

This is what is called a coarse-grained lock. We can do fine-grained locks to increase performance, at the cost of making the implementation a bit trickier. For example, in the hash table example, we can put a lock on each bucket in the put function:

  int i = key % NBUCKET;
  insert(key, value, &table[i], table[i]);

This way multiple cores can update the hash table simultaneously, without disrupting each other.

A final word of warning. Reasoning about concurrent code can be tricky, and you can’t even assume a single line will happen sequentially. For example count++ is actually two (assembly) instructions in one. In C we might say it corresponds to:

temp  = count
count = temp + 1

We can’t make any guarantees that these lines will happen sequentially, and if another process is using the same variable you are in for trouble. This highlights the importance of atomicity, and in this case we could’ve solved the problem with a simple lock.

How are locks implemented in xv6?

There are many ways to implement locks, and xv6 implements them as spinlocks. This means the CPU is spinning in a loop, waiting for the lock to be released. This is great for short-term locks, but horrible for long-term locks, which is why other implementations of locks are frequently used. Here’s an excerpt from the relevant code in xv6:

// Mutual exclusion lock.
struct spinlock {
  uint locked;       // Is the lock held?

As you can see it’s really just an (unsigned) integer that signifies if it’s held or not. If it is zero the lock is available. The magic is in its two primary functions - acquire and release (or lock and unlock):

void acquire(struct spinlock *lk) {
  pushcli(); // disable interrupts to avoid deadlock.
  if(holding(lk)) // checks if the lock is locked.
  panic("acquire"); // we can't acquire an already acquired lock.
  while(xchg(&lk->locked, 1) != 0) // xchg is atomic.

There are two main things to note here. The first is the pushcli call. This disables hardware interrupts, which we do to ensure that we execute the code in acquire sequentially. If we didn’t, a hardware interrupt might change the flow of execution so that another piece of code tries to acquire the lock, and things would quickly get messy.

The release function essentially does the opposite of acquire, and it ends by enabling hardware interrupts again. Because locks can be nested - we can hold a lock around a function which has another lock inside of it - it’s not enough to just enable and disable hardware interrupts directly. We maintain the ordering of locking and unlocking by using a stack.

The second thing to note is the xcgh instruction, which is a compare-and-swap instruction. Two CPUs could both notice that the lock is free, and both try to grab it. To avoid this, the check and changing of the locked field has to be atomic, i.e. happen in one single step. xchg is a special instruction that provides this guarantee. The while-loop is spinning, checking if it can acquire the lock and constantly “updating” the locked field to 1. Once the locked field is set to 0, i.e. the lock has been released somewhere else, xchg checks this field and updates the field to 1, all in one step.

There’s a lot more to concurrency and locks than what we have covered in this post, but hopefully this has given you a taste of what the landscape looks like and what some of the challanges are. If you want to learn more about concurrency primitives from a conceptual and programming point of view, I highly recommend The Little Book of Semaphores by Downey, which is available for free online.

(If you liked this, you might enjoy A short overview of the file system.)

NOTE: Kamal Marhubi pointed out that rtm seems to be saying three things: (1) In this program, all gets happen after all puts so it’s definitely not an issue. (2) There’s a problem with get operations in the general case, because we could get an entry in a non-consistent state (3) In this specific implementation it’s not a problem, because the pointer to the newly-created entry is changed atomically, so the worst we can do is get a stale item. Regardless, these things are tricky and it’s probably better to lock the get operations as well to avoid subtle bugs.

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