Multithreading issues 2: Fun with locks


Locks and deadlocks

Remember the Scrabble game used to illustrate race conditions? Today we will fix the mess I left the players with using exclusivity. But I will also show you the serious limitations this approach has.

Step one: locks and exclusivity

So, we left the game when everybody was playing simultaneously, leading to a messy board full of meaningless words, plus angry players around. Well, fixing this is easy: we just need to ensure that players take turns! Now, everything falls back into place. No more conflicts, only real words on the board. If we look a bit closer into the process, we realize that a player first wait until nobody is playing before laying its own word, and that no other can proceed until he is finished. Another way to express this is that a player needs to have exclusive access to the board before playing, and this happens whenever a player states ‘it’s my turn’.

Let’s diverge a bit from the standard rules…

Now players are playing one after the other, nicely. To be honest,  there is one point that I will not cover today, which is to ensure they do play in proper order, we will get back to that later.

Now assume I want the players to fill the board as fast as possible. There is one thing that is clear: having only one player laying letters at any given time means I do not leverage having multiple players. Even worse, as player has to check when then can play, it just slows the game down. I would fare better with a single player.

So if I want to rip benefits from having multiple players, I must make sure that more than one can act at the same time, meaning it is time to reassess the rules (= algorithms) we put into place at the previous paragraph, starting with the sub-steps:

    1. read the game board
    2. look at one’s letter
    3. identify the best word
    4. lay down the letter
    5. pick random new letters

Only steps (A) and (D) requires access to the board, and step (A) only requires ‘read’ access.  We can try to leverage on that analysis and change the rule in: only one player can be doing either (A) or (D) at any given time.

Let’s evaluate this proposal:

  1. The rule is more complex (bad)
  2. Exclusive part as been reduced to roughly 40% (2 out of 5) of the activity (good)
  3. The player can do stupid things (bad).

A word as explanation is probably required for item 3. Here is an example:

  1. Alice (player one) reads the board.
  2. Bob (player two) reads the board too.
  3. Alice lays down her word
  4. Bob tries but cannot because Alice took his spot (include sexist comment here :-))

In the real world, we can expect Bob to figure out either another word or another location, but computer algorithms are not that smart, yet, and so they would probably ‘butcher’ the board by force replacing the letters or raising an exception.

The obvious fix is to keep the exclusive access not only during steps A and D, but from A to D. Then we will ensure consistency between when the player look at the board and when he/she lays down his/her word. But we can release the lock for step ‘E’. Exclusive access is now 80%, while only 40% was required. At least, while Alice is picking up random new letters, Bob can start thinking: Alice’s E steps can happen simultaneously with Bob’s A step.

From a performance perspective, we have ripped a 20% speed gain with 2 players instead of one. But adding new players will not give us any further boost as they will just be waiting on one another.

This bring us to an important element of parallel programming: Amdahl’s law which sates how much performance you can get by running an algorithm on multiple CPUs. We will discuss it later.

Conclusion

In this post, I did give a quick view on what locks are all about: removing concurrency related risks by permitting exclusive access to critical data. They were also some hints about the inherent complexity of locks, something I will dug further next.

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