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.

Contification is a compiler optimization that turns a function that always returns to the same place into a continuation. Compilers for functional languages use contification to expose the control-flow information that is required by many optimizations, including traditional loop optimizations.This paper gives a formal presentation of contification in MLton, a whole-program optimizing Standard ML compiler. We present two existing algorithms for contification in our framework, as well as a new algorithm based on the dominator tree of a program's call graph. We prove that the dominator algorithm is optimal. We present benchmark results on realistic SML programs demonstrating that contification has minimal overhead on compile time and significantly improves run time.

Software transactional memory (STM) has proven to be a useful abstraction for developing concurrent applications, where programmers denote transactions with an atomic construct that delimits a collection of reads and writes to shared mutable references. The runtime system then guarantees that all transactions are observed to execute atomically with respect to each other. Traditionally, when the runtime system detects that one transaction conflicts with another, it aborts one of the transactions and restarts its execution from the beginning. This can lead to problems with both execution time and throughput. In this paper, we present a novel approach that uses first-class continuations to restart a conflicting transaction at the point of a conflict, avoiding the re-execution of any work from the beginning of the transaction that has not been compromised. In practice, this allows transactions to complete more quickly, decreasing execution time and increasing throughput. We have implemented this idea in the context of the Manticore project, an ML-family language with support for parallelism and concurrency. Crucially, we rely on constant-time continuation capturing via a continuation-passing-style (CPS) transformation and heap-allocated continuations. When comparing our STM that performs partial aborts against one that performs full aborts, we achieve a decrease in execution time of up to 31% and an increase in throughput of up to 351%.

Data parallelism has proven to be an effective technique for highlevel programming of a certain class of parallel applications, but it is not well suited to irregular parallel computations. Blelloch and others proposed nested data parallelism (NDP) as a language mechanism for programming irregular parallel applications in a declarative data-parallel style. The key to this approach is a compiler transformation that flattens the NDP computation and data structures into a form that can be executed efficiently on a widevector SIMD architecture. Unfortunately, this technique is ill suited to execution on today's multicore machines. We present a new technique, called data-only flattening, for the compilation of NDP, which is suitable for multicore architectures. Data-only flattening transforms nested data structures in order to expose programs to various optimizations while leaving control structures intact. We present a formal semantics of data-only flattening in a core language with a rewriting system. We demonstrate the effectiveness of this technique in the Parallel ML implementation and we report encouraging experimental results across various benchmark applications.