Is it possible to improve on Rust’s single-owner strategy to support more complex data structures? Before digging into this challenge, let’s summarize the story so far… The Promise and Limitations of Single-Owner Rust’s single-owner memory management is a form of automatic memory management; a garbage collection strategy that is distinct from tracing and reference-counting. Fundamentally, it is an improvement on RAII, which automatically finalizes some defined resource at the end of its defined lexical scope.

Most programming languages support only copy semantics. A value passed to a function or stored in a variable is a copy of the original value. We know it is a copy, because any change we make to the copy has no impact on the original value. A few languages, like C++ and Rust, also support move semantics. Unlike a copy, a transfer moves the original value to its new home; that value is no longer accessible at its previous home.

Note: The latter part of this post is outdated in terms of the mechanisms that the Cone compiler uses to de-alias ref-counted references. See this post for an updated description. In the world of automatic memory management, reference counting is considered to be one of the easiest to implement. The rules seem simple: When a reference is created to an allocated memory area, set its counter to 1 When the reference is copied (aliased), increment the counter When an alias is destroyed (de-aliased), decrement the counter When the counter reaches zero, free the reference’s memory area The simplicity of these rules does not always translate to a simple implementation.

Note: This is an outdated post, replaced by this post The Cone compiler performs a data flow analysis pass after name resolution and type checking. Given that this sort of analysis is rarely covered by compiler literature, I thought it might be useful to jot down some thoughts about its purpose and intriguing mechanics. Goals Like Rust (and unlike C), Cone applies constraints to references that ensure they can only access memory safely, even in the face of concurrency.

As mentioned in the previous post, all typed nodes use the TypedNodeHdr common header. It only contains a pointer to the node’s type. This type field applies to every node in the “expression” group. This group holds all node types that return a value, including leaf nodes like literals and variables, function call nodes, and even the block and if nodes. The type check pass focuses largely on this type field, ensuring it specifies a valid, consistent type.

Working out effective name handling mechanisms in Cone’s IR took several tries. The key challenges: Although most node types don’t use names, the ones that do are spread out unevenly across every node group: a few expressions (variables and functions), most types, and some statements (e.g., module nodes). What is the best way to gain access to a node’s name information, given this disparity? Cone’s namespace rules vary depending on the semantic context a name is declared within.

Having spoken about the compiler’s IR tree in general terms, let’s focus in on an important detail: how to represent a node that could be any arbitary type. Cone’s IR makes use of dozens of different types of nodes, each defined using a different struct. However, sometimes the compiler needs to point to a node without restricting which type of node it must be. For example, consider an assignment node.

The Cone compiler uses a traditional pipeline to transform source programs into object files. The Intermediate Representation (IR) plays a central role in this design. It is the glue binding together the parser, semantic analysis passes, and the LLVM IR generator. The literature on effective IR design is relatively sparse as compared to other topics, such as parsing, type inference/theory, and optimization techniques. This is a shame. I have devoted far more time trying to get Cone’s IR “right” than I have on any other aspect of the compiler.

Realtime, quality 3D is punishing on hardware and software alike. At least every 17 milliseconds, the scene needs to be updated and redrawn. Managing memory efficiently across the many large and small data structures that make up an interesting 3D scene is a well-known and important part of that challenge.