High performance garbage collectors build upon performance-critical low-level code, typically exhibit multiple levels of concurrency, and are prone to subtle bugs. Implementing, debugging and maintaining such collectors can therefore be extremely challenging. The choice of implementation language is a crucial consideration when building a collector. Typically, the drive for performance and the need for efficient support of low-level memory operations leads to the use of low-level languages like C or C++, which offer little by way of safety and software engineering benefits. This risks undermining the robustness and flexibility of the collector design. Rust's ownership model, lifetime specification, and reference borrowing deliver safety guarantees through a powerful static checker with little runtime overhead. These features make Rust a compelling candidate for a collector implementation language, but they come with restrictions that threaten expressiveness and efficiency. We describe our experience implementing an Immix garbage collector in Rust and C. We discuss the benefits of Rust, the obstacles encountered, and how we overcame them. We show that our Immix implementation has almost identical performance on micro benchmarks, compared to its implementation in C, and outperforms the popular BDW collector on the gcbench micro benchmark. We find that Rust's safety features do not create significant barriers to implementing a high performance collector. Though memory managers are usually considered low-level, our high performance implementation relies on very little unsafe code, with the vast majority of the implementation benefiting from Rust's safety. We see our experience as a compelling proof-of-concept of Rust as an implementation language for high performance garbage collection.

Yieldpoints are critical to the implementation of high performance garbage collected languages, yet the design space is not well understood. Yieldpoints allow a running program to be interrupted at well-defined points in its execution, facilitating exact garbage collection, biased locking, on-stack replacement, profiling, and other important virtual machine behaviors. In this paper we identify and evaluate yieldpoint design choices, including previously undocumented designs and optimizations. One of the designs we identify opens new opportunities for very low overhead profiling. We measure the frequency with which yieldpoints are executed and establish a methodology for evaluating the common case execution time overhead. We also measure the median and worst case time-to-yield. We find that Java benchmarks execute about 100M yieldpoints per second, of which about 1/20000 are taken. The average execution time overhead for untaken yieldpoints on the VM we use ranges from 2.5% to close to zero on modern hardware, depending on the design, and we find that the designs trade off total overhead with worst case time-to-yield. This analysis gives new insight into a critical but overlooked aspect of garbage collector implementation, and identifies a new optimization and new opportunities for very low overhead profiling.

Many of today’s programming languages are broken. Poor performance, lack of features and hard-to-reason-about semantics can cost dearly in software maintenance and inecient execution. The problem is only getting worse with programming languages proliferating and hardware becoming more complicated. An important reason for this brokenness is that much of language design is implementation-driven. The diculties in implementation and insucient understanding of concepts bake bad designs into the language itself. Concurrency, architectural details and garbage collection are three fundamental concerns that contribute much to the complexities of implementing managed languages. We propose the micro virtual machine, a thin abstraction designed specifically to relieve implementers of managed languages of the most fundamental implementation challenges that currently impede good design. The micro virtual machine targets abstractions over memory (garbage collection), architecture (compiler backend), and concurrency. We motivate the micro virtual machine and give an account of the design and initial experience of a concrete instance, which we call Mu, built over a two year period. Our goal is to remove an important barrier to performant and semantically sound managed language design and implementation.

Program portability is an important software engineering consideration. However, when high-level languages are extended to effectively implement system projects for software engineering gain and safety, portability is compromised--high-level code for low-level programming cannot execute on a stock runtime, and, conversely, a runtime with special support implemented will not be portable across different platforms. We explore the portability pitfall of high-level low-level programming in the context of virtual machine implementation tasks. Our approach is designing a restricted high-level language called RJava, with a flexible restriction model and effective low-level extensions, which is suitable for different scopes of virtual machine implementation, and also suitable for a low-level language bypass for improved portability. Apart from designing such a language, another major outcome from this work is clearing up and sharpening the philosophy around language restriction in virtual machine design. In combination, our approach to solving portability pitfalls with RJava favors virtual machine design and implementation in terms of portability and robustness.

Flexible and efficient runtime design requires an understanding of the dependencies among the components internal to the runtime and those between the application and the runtime. These dependencies are frequently unclear. This problem exists in all runtime design, and is most vivid in a metacircular runtime --- one that is implemented in terms of itself. Metacircularity blurs boundaries between application and runtime implementation, making it harder to understand and make guarantees about overall system behavior, affecting isolation, security, and resource management, as well as reducing opportunities for optimization. Our goal is to shed new light on VM interdependencies, helping all VM designers understand these dependencies and thereby engineer better runtimes. We explore these issues in the context of a high-performance Java-in-Java virtual machine. Our approach is to identify and instrument transition points into and within the runtime, which allows us to establish a dynamic execution context. Our contributions are: 1) implementing and measuring a system that dynamically maintains execution context with very low overhead, 2) demonstrating that such a framework can be used to improve the software engineering of an existing runtime, and 3) analyzing the behavior and runtime characteristics of our runtime across a wide range of benchmarks. Our solution provides clarity about execution state and allowable transitions, making it easier to develop, debug, and understand managed runtimes.