Stream Fusion, to Completeness

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

Combinatory logic: from philosophy and mathematics to computer science

In 1920, Moses Schönfinkel provided the first rough details of what later became known as combinatory logic. This endeavour was part of Hilbert’s program to formulate mathematics as a consistent logic system based on a finite set of axioms and inference rules. This program’s importance to the foundations and philosophical aspects of mathematics is still celebrated today. In the 1930s, Haskell Curry furthered Schönfinkel’s work on combinatory logic, attempting – and failing – to show that it can be used as a foundation for mathematics. However, in 1947, he described a high-level functional programming language based on combinatory logic. Research on functional programming languages continued, reaching a high point in the eighties. However, by this time, object-oriented programming languages began taking over and functional languages started to lose their appeal. Lately, however, a resurgence of functional languages is being noted. Indeed, many of the commonly-used programming languages nowadays incorporate functional programming elements in them, while functional languages such as Haskell, OCaml and Erlang are gaining in popularity. Thanks to this revival, it is appropriate to breathe new life into combinatory logic by presenting its main ideas and techniques in this paper.peer-reviewe

StreamJIT: A Commensal Compiler for High-Performance Stream Programming

There are many domain libraries, but despite the performance benefits of compilation, domain-specific languages are comparatively rare due to the high cost of implementing an optimizing compiler. We propose commensal compilation, a new strategy for compiling embedded domain-specific languages by reusing the massive investment in modern language virtual machine platforms. Commensal compilers use the host language's front-end, use host platform APIs that enable back-end optimizations by the host platform JIT, and use an autotuner for optimization selection. The cost of implementing a commensal compiler is only the cost of implementing the domain-specific optimizations. We demonstrate the concept by implementing a commensal compiler for the stream programming language StreamJIT atop the Java platform. Our compiler achieves performance 2.8 times better than the StreamIt native code (via GCC) compiler with considerably less implementation effort.United States. Dept. of Energy. Office of Science (X-Stack Award DE-SC0008923)Intel Corporation (Science and Technology Center for Big Data)SMART3 Graduate Fellowshi

Achieving High-Performance the Functional Way: A Functional Pearl on Expressing High-Performance Optimizations as Rewrite Strategies

Optimizing programs to run efficiently on modern parallel hardware is hard but crucial for many applications. The predominantly used imperative languages - like C or OpenCL - force the programmer to intertwine the code describing functionality and optimizations. This results in a portability nightmare that is particularly problematic given the accelerating trend towards specialized hardware devices to further increase efficiency. Many emerging DSLs used in performance demanding domains such as deep learning or high-performance image processing attempt to simplify or even fully automate the optimization process. Using a high-level - often functional - language, programmers focus on describing functionality in a declarative way. In some systems such as Halide or TVM, a separate schedule specifies how the program should be optimized. Unfortunately, these schedules are not written in well-defined programming languages. Instead, they are implemented as a set of ad-hoc predefined APIs that the compiler writers have exposed. In this functional pearl, we show how to employ functional programming techniques to solve this challenge with elegance. We present two functional languages that work together - each addressing a separate concern. RISE is a functional language for expressing computations using well known functional data-parallel patterns. ELEVATE is a functional language for describing optimization strategies. A high-level RISE program is transformed into a low-level form using optimization strategies written in ELEVATE . From the rewritten low-level program high-performance parallel code is automatically generated. In contrast to existing high-performance domain-specific systems with scheduling APIs, in our approach programmers are not restricted to a set of built-in operations and optimizations but freely define their own computational patterns in RISE and optimization strategies in ELEVATE in a composable and reusable way. We show how our holistic functional approach achieves competitive performance with the state-of-the-art imperative systems Halide and TVM

Jparsec - a parser combinator for Javascript

Parser combinators have been a popular parsing approach in recent years. Compared with traditional parsers, a parser combinator has both readability and maintenance advantages. This project aims to construct a lightweight parser construct library for Javascript called Jparsec. Based on the modular nature of a parser combinator, the implementation uses higher-order functions. JavaScript provides a friendly and simple way to use higher-order functions, so the main construction method of this project will use JavaScript\u27s lambda functions. In practical applications, a parser combinator is mainly used as a tool, such as parsing JSON files. In order to verify the utility of parser combinators, this project uses a parser combinator to parse a partial Lua grammar. Lua is a widely used programming language, serving as a good test case for my parser combinator

An Embedded Domain Specific Language to Model, Transform and Quality Assure Business Processes in Business-Driven Development

In Business-Driven Development (BDD), business process models are produced by business analysts. To ensure that the business requirements are satisfied, the IT solution is directly derived through a process of model refinement. If models do not contain all the required technical details or contain errors, the derived implementation would be incorrect and the BDD lifecycle would have to be repeated. In this project we present a functional domain specific language embedded in Haskell, with which: 1) models can rapidly be produced in a concise and abstract manner, 2) enables focus on the specifications rather than the implementation, 3) ensures that all the required details, to generate the executable code, are specified, 4) models can be transformed, analysed and interpreted in various ways, 5) quality assures models by carrying out three types of checks; by Haskell.s type checker, at construction-time and by functions that analyse the soundness of models, 6) enables users to define quality assured composite model transformations

RVSDG: An Intermediate Representation for Optimizing Compilers

Intermediate Representations (IRs) are central to optimizing compilers as the way the program is represented may enhance or limit analyses and transformations. Suitable IRs focus on exposing the most relevant information and establish invariants that different compiler passes can rely on. While control-flow centric IRs appear to be a natural fit for imperative programming languages, analyses required by compilers have increasingly shifted to understand data dependencies and work at multiple abstraction layers at the same time. This is partially evidenced in recent developments such as the MLIR proposed by Google. However, rigorous use of data flow centric IRs in general purpose compilers has not been evaluated for feasibility and usability as previous works provide no practical implementations. We present the Regionalized Value State Dependence Graph (RVSDG) IR for optimizing compilers. The RVSDG is a data flow centric IR where nodes represent computations, edges represent computational dependencies, and regions capture the hierarchical structure of programs. It represents programs in demand-dependence form, implicitly supports structured control flow, and models entire programs within a single IR. We provide a complete specification of the RVSDG, construction and destruction methods, as well as exemplify its utility by presenting Dead Node and Common Node Elimination optimizations. We implemented a prototype compiler and evaluate it in terms of performance, code size, compilation time, and representational overhead. Our results indicate that the RVSDG can serve as a competitive IR in optimizing compilers while reducing complexity