One challenge I’ve often faced when writing asynchronous code is trying to figure out how to break the problem down in a reasonable way. How far do I go in breaking up my code? How many steps do I want to take? How do I deal with tasks that branch or fork? How do I deal with the interdependencies I’ve created. My hope in this article is to give some guidelines I’ve learned and some tools for answering these questions.
Posts with the tag advent-2019:
Previously, I discussed the compare-and-swap operation as an operation to perform on atomicint variables. That’s just the tip of the iceberg. While most of the atomic emoji ⚛️ operators are only for atomicints, the cas function, atomic-fetch (or prefix ⚛️ operator), and atomic-assign (or ⚛️= operator) can all be used on any kind of Scalar variable. First, we need to make sure we know what a Scalar is. In Raku, every variable name is associated with a container.
Today I want to discuss the use of locks to make an object thread safe. That is, by employing a simple pattern for locking access to your object, you can effectively guarantee that only a single thread has access to any part of the object at a time. This, therefore, guarantees that the state of the object can never be corrupted, even when multiple threads attempt to access it concurrently.
In Raku, there are different ways to pause your code. The simplest and most obvious way to do so is to use sleep: my $before = now; sleep 1; my $after = now - $before; say $after; Assuming your system is not bogged down at the moment, the output of $after should be a number pretty close to 1. Not very exciting: you didn’t do anything for a second. Woo. Woo.
Map-reduce is a common way of solving problems in functional programming. You have a list of items, you iterate through them to process them, and then you take the set and summarize them. We call this map-reduce because the iteration step is mapping values to new values and the summarize step reduces the number of values. In Raku, map-reduce is a common programming pattern: my $fibonacci = (1, 1, * + * .
Let’s consider now how to solve a big problem with concurrency. If you have an algorithmic problem with lots of data that needs to be processed, you want to maximize the amount of load you can place on the available CPU cores to process it as quickly as possible. To demonstrate how we can go about this in Raku, we will consider Conway’s Game of Life, played on an effectively infinite game board.
What’s faster than locks? Compare-and-swap. Modern CPUs have multiple cores. As such, all modern CPUs must have tools for performing absolutely atomic operations to allow those multiple cores to work together. One of those operations is the compare-and-swap or cas operation. In the abstract, a cas operation takes three arguments, a variable to modify, a given value, and a new value. The variable is set to the new value modified only if the current value held by the variable is equal to the given value.
When writing concurrent code in Raku, we want to avoid sharing data between tasks. This is because code that shares no data is automatically safe and doesn’t have to worry about interdependencies with other code. So, when you can, you should do your work through Supply, Promise, and Channel objects that are then synchronized together by a central thread. That way, all the state changes are safe. This is not always practical, though.
The react block in Raku is the primary means of re-synchronizing asynchronous coding activity. Using it, you can easily pull together promises, supplies, and channels to make a coherent whole of your program or a subsystem. A react block itself can run any code you want plus one or more whenever blocks. The code in the block will run once and the block will exit either when a done subroutine is called or when all the objects associated with whenever blocks are finished (i.
A large number of concurrency-oriented coding in Raku depends on the use of a Scheduler. Many async operations depend on the default scheduler created by the VM at the start of runtime. You can access this via the dynamic variable named $*SCHEDULER. The most important feature of a Scheduler is the .cue method. Calling that method with a code reference will schedule the work for execution. The type of scheduler will determine what exactly that means.