Cyclone is a type-safe programming language derived from C. The primary design goal of Cyclone is to let programmers control data representation and memory management without sacrificing type-safety. In this paper, we focus on the region-based memory management of Cyclone and its static typing discipline. The design incorporates several advancements, including support for region subtyping and a coherent integration with stack allocation and a garbage collector. To support separate compilation, Cyclone requires programmers to write some explicit region annotations, but a combination of default annotations, local type inference, and a novel treatment of region effects reduces this burden. As a result, we integrate C idioms in a region-based framework. In our experience, porting legacy C to Cyclone has required altering about 8% of the code; of the changes, only 6% (of the 8%) were region annotations.

Intensional polymorphism, the ability to dispatch to different routines based on types at run time, enables a variety of advanced implementation techniques for polymorphic languages, including tag-free garbage collection, unboxed function arguments, polymorphic marshalling, and flattened data structures. To date, languages that support intensional polymorphism have required a type-passing (as opposed to type-erasure) interpretation where types are constructed and passed to polymorphic functions at run time. Unfortunately, type-passing suffers from a number of drawbacks: it requires duplication of constructs at the term and type levels, it prevents abstraction, and it severely complicates polymorphic closure conversion.We present a type-theoretic framework that supports intensional polymorphism, but avoids many of the disadvantages of type passing. In our approach, run-time type information is represented by ordinary terms. This avoids the duplication problem, allows us to recover abstraction, and avoids complications with closure conversion. In addition, our type system provides another improvement in expressiveness; it allows unknown types to be refined in place thereby avoiding certain beta-expansions required by other frameworks.

The problem of probabilistic modeling and inference, at a high-level, can be viewed as constructing a ( model , query , inference ) tuple, where an inference algorithm implements a query on a model . Notably, the derivation of inference algorithms can be a difficult and error-prone task. Hence, researchers have explored how ideas from probabilistic programming can be applied. In the context of constructing these tuples, probabilistic programming can be seen as taking a language-based approach to probabilistic modeling and inference. For instance, by using (1) appropriate languages for expressing models and queries and (2) devising inference techniques that operate on encodings of models (and queries) as program expressions, the task of inference can be automated. In this paper, we describe a compiler that transforms a probabilistic model written in a restricted modeling language and a query for posterior samples given observed data into a Markov Chain Monte Carlo (MCMC) inference algorithm that implements the query. The compiler uses a sequence of intermediate languages (ILs) that guide it in gradually and successively refining a declarative specification of a probabilistic model and the query into an executable MCMC inference algorithm. The compilation strategy produces composable MCMC algorithms for execution on a CPU or GPU.

The concept of a "unique" object arises in many emerging programming languages such as Clean, CQual, Cyclone, TAL, and Vault. In each of these systems, unique objects make it possible to perform operations that would otherwise be prohibited (e.g., deallocating an object) or to ensure that some obligation will be met (e.g., an opened file will be closed). However, different languages provide different interpretations of "uniqueness" and have different rules regarding how unique objects interact with the rest of the language.Our goal is to establish a common model that supports each of these languages, by allowing us to encode and study the interactions of the different forms of uniqueness. The model we provide is based on a substructural variant of the polymorphic λ-calculus, augmented with four kinds of mutable references: unrestricted, relevant, affine, and linear. The language has a natural operational semantics that supports deallocation of references, strong (type-varying) updates, and storage of unique objects in shared references. We establish the strong soundness of the type system by constructing a novel, semantic interpretation of the types.

Many high-level language compilers generate C code and then invoke a C compiler for code generation. To date, most of these compilers link the resulting code against a conservative mark-sweep garbage collector in order to reclaim unused memory. We i n troduce a new collector, MCC, based on an extension of mostly-copying collection. We analyze the various design decisions made in MCC and provide a performance comparison to the most widely used conservative mark-sweep collector the Boehm-Demers-Weiser collector. Our results show that a good mostlycopying collector can outperform a mature highly-optimized mark-sweep collector when physical memory is large relative to the live data. A surprising result of our analysis is that cache behavior can have a greater impact on overall performance than either collector time or allocation time.