Stream processing is mainstream (again): Widely-used stream libraries are now available for virtually all modern OO and functional languages, from Java to C# to Scala to OCaml to Haskell. Yet expressivity and performance are still lacking. For instance, the popular, well-optimized Java 8 streams do not support the zip operator and are still an order of magnitude slower than hand-written loops. We present the first approach that represents the full generality of stream processing and eliminates overheads, via the use of staging. It is based on an unusually rich semantic model of stream interaction. We support any combination of zipping, nesting (or flat-mapping), sub-ranging, filtering, mapping—of finite or infinite streams. Our model captures idiosyncrasies that a programmer uses in optimizing stream pipelines, such as rate differences and the choice of a “for” vs. “while” loops. Our approach delivers hand-written–like code, but automatically. It explicitly avoids the reliance on black-box optimizers and sufficiently-smart compilers, offering highest, guaranteed and portable performance. Our approach relies on high-level concepts that are then readily mapped into an implementation. Accordingly, we have two distinct implementations: an OCaml stream library, staged via MetaOCaml, and a Scala library for the JVM, staged via LMS. In both cases, we derive libraries richer and simultaneously many tens of times faster than past work. We greatly exceed in performance the standard stream libraries available in Java, Scala and OCaml, including the well-optimized Java 8 streams.

Java generics are compiled by-erasure: all clients reuse the same bytecode, with uses of the unknown type erased. C++ templates are compiled by-expansion: each type-instantiation of a template produces a different code definition. The two approaches offer trade-offs on multiple axes. We propose an extension of Java generics that allows by-expansion translation relative to selected type parameters only. This language design allows sophisticated users to get the best of both worlds at a fine granularity. Furthermore, our proposal is based on Java 8 Type Annotations (JSR 308) and the Checker Framework as an abstraction layer for controlling compilation without changes to the internals of a Java compiler.

To maximize run-time performance, programmers often specialize their code by hand, replacing library collections and containers by custom objects in which data is restructured for efficient access. However, changing the data representation is a tedious and error-prone process that makes it hard to test, maintain and evolve the source code. We present an automated and composable mechanism that allows programmers to safely change the data representation in delimited scopes containing anything from expressions to entire class definitions. To achieve this, programmers define a transformation and our mechanism automatically and transparently applies it during compilation, eliminating the need to manually change the source code. Our technique leverages the type system in order to offer correctness guarantees on the transformation and its interaction with object-oriented language features, such as dynamic dispatch, inheritance and generics. We have embedded this technique in a Scala compiler plugin and used it in four very different transformations, ranging from improving the data layout and encoding, to retrofitting specialization and value class status, and all the way to collection deforestation. On our benchmarks, the technique obtained speedups between 1.8x and 24.5x.

Program generation is indispensable. We propose a novel unification of two existing metaprogramming techniques: multi-stage programming and hygienic generative macros. The former supports runtime code generation and execution in a type-safe manner while the latter offers compile-time code generation. In this work we draw upon a long line of research on metaprogramming, starting with Lisp, MetaML and MetaOCaml. We provide direct support for quotes, splices and top-level splices, all regulated uniformly by a level-counting Phase Consistency Principle. Our design enables the construction and combination of code values for both expressions and types. Moreover, code generation can happen either at runtime à la MetaML or at compile time, in a macro fashion, à la MacroML. We provide an implementation of our design in Scala and we present two case studies. The first implements the Hidden Markov Model, Shonan Challenge for HPC. The second implements the staged streaming library Strymonas.