We develop a type-theoretical method for optimizing type directed compilation of polymorphic languages, implement the method in SML#, which is a full-scale compiler of Standard ML extended with several advanced features that require type-passing operational semantics, and report its effectiveness through performance evaluation. For this purpose, we first define a predicative second-order lambda calculus with finitary polymorphism, where each type abstraction is explicitly constrained to a finite type universe, and establishes the type soundness with respect to a type-passing operational semantics. Different from a calculus with stratified type universes, type universes of the calculus are terms that represent a finite set of instance types. We then develop a universe reconstruction algorithm that takes a term of the standard second-order lambda calculus, checks if the term is typable with finitary polymorphism, and, if typable, constructs a term in the calculus of finitary polymorphism. Based on these results, we present a type-based optimization method for polymorphic functions. Since our formalism is based on the second-order lambda calculus, it can be used to optimize various polymorphic languages. We implement the optimization method for native (tag-free) data representation and record polymorphism, and evaluate its effectiveness through benchmarks. The evaluation shows that 83.79% of type passing abstractions are eliminated, and achieves the average of 15.28% speed-up of compiled code.

This paper investigates language constructs for high-level and type-safe manipulation of JSON objects in a typed functional language. A major obstacle in representing JSON in a static type system is their heterogeneous nature: in most practical JSON APIs, a JSON array is a heterogeneous list consisting of, for example, objects having common fields and possibly some optional fields. This paper presents a typed calculus that reconciles static typing constraints and heterogeneous JSON arrays based on the idea of partially dynamic records originally proposed and sketched by Buneman and Ohori for complex database object manipulation. Partially dynamic records are dynamically typed records, but some parts of their structures are statically known. This feature enables us to represent JSON objects as typed data structures. The proposed calculus smoothly extends with ML-style pattern matching and record polymorphism. These results yield a typed functional language where the programmer can directly import JSON data as terms having static types, and can manipulate them with the full benefits of static polymorphic type-checking. The proposed calculus has been embodied in SML#, an extension of Standard ML with record polymorphism and other practically useful features. This paper also reports on the details of the implementation and demonstrates its feasibility through examples using actual Web APIs. The SML# version 3.1.0 compiler includes JSON support presented in this paper, and is available from Tohoku University as open-source software under a BSD-style license.

This paper presents a concurrent garbage collection method for functional programs running on a multicore processor. It is a concurrent extension of our bitmap-marking non-moving collector with Yuasa's snapshot-at-the-beginning strategy. Our collector is unobtrusive in the sense of the Doligez-Leroy-Gonthier collector; the collector does not stop any mutator thread nor does it force them to synchronize globally. The only critical sections between a mutator and the collector are the code to enqueue/dequeue a 32 kB allocation segment to/from a global segment list and the write barrier code to push an object pointer onto the collector's stack. Most of these data structures can be implemented in standard lock-free data structures. This achieves both efficient allocation and unobtrusive collection in a multicore system. The proposed method has been implemented in SML#, a full-scale Standard ML compiler supporting multiple native threads on multicore CPUs. Our benchmark tests show a drastically short pause time with reasonably low overhead compared to the sequential bitmap-marking collector.

This paper reports on our industry-academia project of using a functional language in business software production. The general motivation behind the project is our ultimate goal of adopting an ML-style higher-order typed functional language in a wide range of ordinary software development in industry. To probe the feasibility and identify various practical problems and needs, we have conducted a 15 month pilot project for developing an enterprise resource planning (ERP) system in SML#. The project has successfully completed as we have planned, demonstrating the feasibility of SML#. In particular, seamless integration of SQL and direct C language interface are shown to be useful in reliable and efficient development of a data intensive business application. During the program development, we have found several useful functional programming patterns and a number of possible extensions of an ML-style language with records. This paper reports on the project details and the lessons learned from the project.

Integrating a database query language into a programming language is becoming increasingly important in recently emerging high-level cloud computing and other applications, where efficient and sophisticated data manipulation is required during computation. This paper reports on seamless integration of SQL into SML# - an extension of Standard ML. In the integrated language, the type system always infers a principal type for any type consistent SQL expression. This makes SQL queries first-class citizens, which can be freely combined with any other language constructs definable in Standard ML. For a program involving SQL queries, the compiler separates SQL queries and delegates their evaluation to a database server, e.g. PostgreSQL or MySQL in the currently implemented version.

Motivated by developing a memory management system that allows functional languages to seamlessly inter-operate with C, we propose an efficient non-moving garbage collection algorithm based on bitmap marking and report its implementation and performance evaluation.