Stream fusion, to completeness

Abstract

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.

Semantics-preserving inlining for metaprogramming

“…The aforementioned syntactic mondalities are introduced to denote where the evaluation needs to be deferred and we already cross the boundary of semantics-preserving code. At this point, we can navigate and guide freely the process of generating code from a quoted domain-speci c language as shown in past work [6,10,13]. The macro system that comes in Scala 3, described in 5.4 essentially gains inspiration from these technologies and completes what we present in this work.…”

Java Stream Fusion

“…Combining these two elements provides a powerful mechanism to manipulate Java collections. However, as reported in [11], expressivity and performance are still lacking. In fact, well-optimized Java 8 streams do not support important operators, like for example the zip operator, and are still an order of magnitude slower than hand-written loops [11].…”

Staged abstract interpreters: fast and modular whole-program analysis via meta-programming

“…Ofenbeck et al [2017] and Amin and Rompf [2018] discussed staging for generic programming as well as stage polymorphism in LMS. Multi-stage programming has been widely used to improve the performance in many other domains, such as optimizing compilers or domain-specific languages [Carette et al 2009;Sujeeth et al , 2013, numerical computation [Aktemur et al 2013;Kiselyov 2018], generic programming [Ofenbeck et al 2017;Yallop 2017], data processing [Jonnalagedda et al 2014;Kiselyov et al 2017], query compilation in databases , etc.…”