The Essence of Nested Composition

Calculi with disjoint intersection types support an introduction form for intersections called the merge operator, while retaining a coherent semantics. Disjoint intersections types have great potential to serve as a foundation for powerful, flexible and yet type-safe and easy to reason OO languages. This paper shows how to significantly increase the expressive power of disjoint intersection types by adding support for nested subtyping and composition, which enables simple forms of family polymorphism to be expressed in the calculus. The extension with nested subtyping and composition is challenging, for two different reasons. Firstly, the subtyping relation that supports these features is non-trivial, especially when it comes to obtaining an algorithmic version. Secondly, the syntactic method used to prove coherence for previous calculi with disjoint intersection types is too inflexible, making it hard to extend those calculi with new features (such as nested subtyping). We show how to address the first problem by adapting and extending the Barendregt, Coppo and Dezani (BCD) subtyping rules for intersections with records and coercions. A sound and complete algorithmic system is obtained by using an approach inspired by Pierce\u27s work. To address the second problem we replace the syntactic method to prove coherence, by a semantic proof method based on logical relations. Our work has been fully formalized in Coq, and we have an implementation of our calculus

Relational parametricity for higher kinds

Reynolds’ notion of relational parametricity has been extremely influential and well studied for polymorphic programming languages and type theories based on System F. The extension of relational parametricity to higher kinded polymorphism, which allows quantification over type operators as well as types, has not received as much attention. We present a model of relational parametricity for System Fω, within the impredicative Calculus of Inductive Constructions, and show how it forms an instance of a general class of models defined by Hasegawa. We investigate some of the consequences of our model and show that it supports the definition of inductive types, indexed by an arbitrary kind, and with reasoning principles provided by initiality

Safer typing of complex API usage through Java generics

When several incompatible implementations of a single API are in use in a Java program, the danger exists that instances from different implementations may inadvertently be mixed, leading to errors. In this paper we show how to use generics to prevent such mixing. The core idea of the approach is to add a type parameter to the interfaces of the API, and tie the classes that make up an implementation to a unique choice of type parameter. In this way methods of the API can only be invoked with arguments that belong to the same implementation. We show that the presence of a type parameter in the interfaces does not violate the principle of interface-based programming: clients can still completely abstract over the choice of implementation. In addition, we demonstrate how code can be reused between different implementations, how implementations can be defined as extensions of other implementations, and how different implementations may be mixed in a controlled and safe manner. To explore the feasibility of the approach, gauge its usability, and identify any issues that may crop up in practical usage, we have refactored a fairly large existing API-based application suite, and we report on the experience gained in the process

Dynamically typed languages

Dynamically typed languages such as Python and Ruby have experienced a rapid grown in popularity in recent times. However, there is much confusion as to what makes these languages interesting relative to statically typed languages, and little knowledge of their rich history. In this chapter I explore the general topic of dynamically typed languages, how they differ from statically typed languages, their history, and their defining features

Intensional Effect Polymorphism

Type-and-effect systems are a powerful tool for program construction and verification. We describe intensional effect polymorphism, a new foundation for effect systems that integrates static and dynamic effect checking. Our system allows the effect of polymorphic code to be intensionally inspected through a lightweight notion of dynamic typing. When coupled with parametric polymorphism, the powerful system utilizes runtime information to enable precise effect reasoning, while at the same time retains strong type safety guarantees. We build our ideas on top of an imperative core calculus with regions. The technical innovations of our design include a relational notion of effect checking, the use of bounded existential types to capture the subtle interactions between static typing and dynamic typing, and a differential alignment strategy to achieve efficiency in dynamic typing. We demonstrate the applications of intensional effect polymorphism in concurrent programming, security, graphical user interface access, and memoization

PiCo: High-performance data analytics pipelines in modern C++

In this paper, we present a new C++ API with a fluent interface called PiCo (Pipeline Composition). PiCo’s programming model aims at making easier the programming of data analytics applications while preserving or enhancing their performance. This is attained through three key design choices: 1) unifying batch and stream data access models, 2) decoupling processing from data layout, and 3) exploiting a stream-oriented, scalable, efficient C++11 runtime system. PiCo proposes a programming model based on pipelines and operators that are polymorphic with respect to data types in the sense that it is possible to reuse the same algorithms and pipelines on different data models (e.g., streams, lists, sets, etc.). Preliminary results show that PiCo, when compared to Spark and Flink, can attain better performances in terms of execution times and can hugely improve memory utilization, both for batch and stream processing.Author's copy (postprint) of C. Misale, M. Drocco, G. Tremblay, A.R. Martinelli, M. Aldinucci, PiCo: High-performance data analytics pipelines in modern C++, Future Generation Computer Systems (2018), https://doi.org/10.1016/j.future.2018.05.03