The Lifecycle of a Reference

Over two and half years ago, I described something I called Gradual Memory Management. Inspired by Rust and Pony, my paper proposed that it is feasible and desirable for a systems programming language to allow programs to exploit multiple memory management and permission strategies. Without putting memory and data race safety at risk, doing so would facilitate significant improvements to throughput and latency, even in multi-threaded architectures. It is easy to propose wild ideas in words.

The Fascinating Influence of Cyclone

In 2001, Trevor Jim (AT&T Research) and Greg Morrisett (Cornell) launched a joint project to develop a safe dialect of the C programming language, an outgrowth of earlier work on Typed Assembly Language. After five years of hard work and some published papers, the team (including Dan Grossman, Michael Hicks, Nik Swamy, and others) released Cyclone 1.0. And then the developers moved on to other things. Few have heard of Cyclone and almost no one has used it.

The Power of Lifetimes

The execution of a program unfolds over some interval of time. The lifetime of every temporary resource (e.g., variable or object) is the time span between that resource’s “creation” and “destruction”. This lifetime is wholly contained within the typically-longer lifetime of the program. The goal of this post is to explore how versatile lifetime analysis has increasingly become in managing memory efficiently, safely and with better performance. By the end of this post, we will explore exciting new ways to apply lifetime analysis, beyond their current support in Rust.

Can a compiler guarantee multi-owner memory safety?

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.

Move Mechanics

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.

The Challenge of Counting References

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.

Data Flow Analysis

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. Some of these constraints are completely enforced by the compiler.

Gradual Memory Management

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.