Partial Aborts for Transactions via First Class Continuations

Software transactional memory (STM) has proven to be a useful abstraction for developing concurrent applications where programmers denote transactions with an atomic construct that delimits a collection of reads and writes to shared mutable references. The runtime system then guarantees that all transactions are observed to execute atomically with respect to each other. Traditionally, when the runtime system detects that one transaction conflicts with another, it aborts one of the transactions and restarts its execution from the beginning. This can lead to problems with both execution time and throughput. This thesis presents a novel approach that uses first-class continuations to restart a conflicting transaction at the point of a conflict, avoiding the re-execution of any work from the beginning of the transaction that has not been compromised. In practice, this allows transactions to complete more quickly, decreasing execution time and increasing throughput. The ideas presented in this thesis have been implemented in the context of the Manticore project, an ML-family language with support for parallelism and concurrency. Crucially, this work relies on constant-time continuation capturing via a continuation-passing-style (CPS) transformation and heap-allocated continuations. The partial abort scheme has been implemented as a part of three modern STM implementations: TL2, TinySTM, and NOrec. Experimental results show that, while no base STM implementation is universally best, each partial-abort implementation compares favorably to its full-abort counterpart. In addition to an implementation, this thesis presents a formal semantics for partial aborts. A proof of correctness is given by relating the partial-abort semantics to an analogous full-abort semantics via a simulation. All proofs have been formally verified using the Coq Theorem Prover

Building Web Based Programming Environments for Functional Programming

Functional programming offers an accessible and powerful algebraic model for computing. JavaScript is the language of the ubiquitous Web, but it does not support functional programs well due to its single-threaded, asynchronous nature and lack of rich control flow operators. The purpose of this work is to extend JavaScript to a language environment that satisfies the needs of functional programs on the Web. This extended language environment uses sophisticated control operators to provide an event-driven functional programming model that cooperates with the browser\u27s DOM, along with synchronous access to JavaScript\u27s asynchronous APIs. The results of this work are used toward two projects: (1) a programming environment called WeScheme that runs in the web browser and supports a functional programming curriculum, and (2) a tool-chain called Moby that compiles event-driven functional programs to smartphones, with access to phone-specific features

A Transformation-Based Foundation for Semantics-Directed Code Generation

Interpreters and compilers are two different ways of implementing programming languages. An interpreter directly executes its program input. It is a concise definition of the semantics of a programming language and is easily implemented. A compiler translates its program input into another language. It is more difficult to construct, but the code that it generates runs faster than interpreted code. In this dissertation, we propose a transformation-based foundation for deriving compilers from semantic specifications in the form of four rules. These rules give apriori advice for staging, and allow explicit compiler derivation that would be less succinct with partial evaluation. When applied, these rules turn an interpreter that directly executes its program input into a compiler that emits the code that the interpreter would have executed. We formalize the language syntax and semantics to be used for the interpreter and the compiler, and also specify a notion of equality. It is then possible to precisely state the transformation rules and to prove both local and global correctness theorems. And although the transformation rules were developed so as to apply to an interpreter written in a denotational style, we consider how to modify non-denotational interpreters so that the rules apply. Finally, we illustrate these ideas by considering a larger example: a Prolog implementation

Exact Recursive Probabilistic Programming

Recursive calls over recursive data are widely useful for generating probability distributions, and probabilistic programming allows computations over these distributions to be expressed in a modular and intuitive way. Exact inference is also useful, but unfortunately, existing probabilistic programming languages do not perform exact inference on recursive calls over recursive data, forcing programmers to code many applications manually. We introduce a probabilistic language in which a wide variety of recursion can be expressed naturally, and inference carried out exactly. For instance, probabilistic pushdown automata and their generalizations are easy to express, and polynomial-time parsing algorithms for them are derived automatically. We eliminate recursive data types using program transformations related to defunctionalization and refunctionalization. These transformations are assured correct by a linear type system, and a successful choice of transformations, if there is one, is guaranteed to be found by a greedy algorithm

RE-LANG---A Parallel-by-default Programming Language

In recent years, programming language features such as lightweight threads have gained popularity in the software development workflow. Our research takes a critical look at these recent trends, rethinking them through an academic lens. We propose a construct called "smart assignment," supported by rewriting semantics, which enables a novel parallel-by-default programming paradigm. We present a new programming language—RE-LANG—that implements this feature. Specifically, we demonstrate how the design philosophy of RE-LANG makes imperative, parallel programming more developer-friendly. We discuss the implementation of the language and showcase performance benchmarks, as well as overhead analysis, to demonstrate its efficiency.Doctor of Philosoph

Annual Report 1989-1990

An administrative report of statistics and information pertaining to the University of North Florida Thomas G. Carpenter Library for the years 1989-1990. The report includes summaries and charts on library budgets, library collection, serials and cataloging workloads, circulation, interlibrary loan, and public services

Purely functional GLL parsing

Generalised parsing has become increasingly important in the context of software language design and several compiler generators and language workbenches have adopted generalised parsing algorithms such as GLR and GLL. The original GLL parsing algorithms are described in low-level pseudo-code as the output of a parser generator. This paper explains GLL parsing differently, defining the FUN-GLL algorithm as a collection of pure, mathematical functions and focussing on the logic of the algorithm by omitting implementation details. In particular, the data structures are modelled by abstract sets and relations rather than specialised implementations. The description is further simplified by omitting lookahead and adopting the binary subtree representation of derivations to avoid the clerical overhead of graph construction. Conventional parser combinators inherit the drawbacks from the recursive descent algorithms they implement. Based on FUN-GLL, this paper defines generalised parser combinators that overcome these problems. Th