275 research outputs found
A flexible model for dynamic linking in Java and C#
Dynamic linking supports flexible code deployment, allowing partially linked code to link further code on the fly, as needed.
Thus, end-users enjoy the advantage of automatically receiving any updates, without any need for any explicit actions on their side,
such as re-compilation, or re-linking. On the down side, two executions of a program may link in different versions of code, which
in some cases causes subtle errors, and may mystify end-users.
Dynamic linking in Java and C# are similar: the same linking phases are involved, soundness is based on similar ideas, and
executions which do not throw linking errors give the same result. They are, however, not identical: the linking phases are combined
differently, and take place in different order. Consequently, linking errors may be detected at different times by Java and C# runtime
systems.
We develop a non-deterministic model, which describes the behaviour of both Java and C# program executions. The nondeterminism
allows us to describe the design space, to distill the similarities between the two languages, and to use one proof of
soundness for both. We also prove that all execution strategies are equivalent with respect to terminating executions that do not
throw link errors: they give the same results
Parallel evaluation strategies for lazy data structures in Haskell
Conventional parallel programming is complex and error prone. To improve programmer
productivity, we need to raise the level of abstraction with a higher-level
programming model that hides many parallel coordination aspects. Evaluation
strategies use non-strictness to separate the coordination and computation aspects
of a Glasgow parallel Haskell (GpH) program. This allows the specification of high
level parallel programs, eliminating the low-level complexity of synchronisation and
communication associated with parallel programming.
This thesis employs a data-structure-driven approach for parallelism derived through
generic parallel traversal and evaluation of sub-components of data structures. We
focus on evaluation strategies over list, tree and graph data structures, allowing
re-use across applications with minimal changes to the sequential algorithm.
In particular, we develop novel evaluation strategies for tree data structures, using
core functional programming techniques for coordination control, achieving more
flexible parallelism. We use non-strictness to control parallelism more flexibly. We
apply the notion of fuel as a resource that dictates parallelism generation, in particular,
the bi-directional flow of fuel, implemented using a circular program definition,
in a tree structure as a novel way of controlling parallel evaluation. This is the first
use of circular programming in evaluation strategies and is complemented by a lazy
function for bounding the size of sub-trees.
We extend these control mechanisms to graph structures and demonstrate performance
improvements on several parallel graph traversals. We combine circularity
for control for improved performance of strategies with circularity for computation
using circular data structures. In particular, we develop a hybrid traversal strategy
for graphs, exploiting breadth-first order for exposing parallelism initially, and
then proceeding with a depth-first order to minimise overhead associated with a full
parallel breadth-first traversal.
The efficiency of the tree strategies is evaluated on a benchmark program, and
two non-trivial case studies: a Barnes-Hut algorithm for the n-body problem and
sparse matrix multiplication, both using quad-trees. We also evaluate a graph search
algorithm implemented using the various traversal strategies.
We demonstrate improved performance on a server-class multicore machine with
up to 48 cores, with the advanced fuel splitting mechanisms proving to be more
flexible in throttling parallelism. To guide the behaviour of the strategies, we develop
heuristics-based parameter selection to select their specific control parameters
Shared Arrangements: practical inter-query sharing for streaming dataflows
Current systems for data-parallel, incremental processing and view
maintenance over high-rate streams isolate the execution of independent
queries. This creates unwanted redundancy and overhead in the presence of
concurrent incrementally maintained queries: each query must independently
maintain the same indexed state over the same input streams, and new queries
must build this state from scratch before they can begin to emit their first
results. This paper introduces shared arrangements: indexed views of maintained
state that allow concurrent queries to reuse the same in-memory state without
compromising data-parallel performance and scaling. We implement shared
arrangements in a modern stream processor and show order-of-magnitude
improvements in query response time and resource consumption for interactive
queries against high-throughput streams, while also significantly improving
performance in other domains including business analytics, graph processing,
and program analysis
Shared-Environment Call-by-Need
Call-by-need semantics formalize the wisdom that work should be done at most once. It frees programmers to focus more on the correctness of their code, and less on the operational details. Because of this property, programmers of lazy functional languages rely heavily on their compiler to both preserve correctness and generate high-performance code for high level abstractions. In this dissertation I present a novel technique for compiling call-by-need semantics by using shared environments to share results of computation. I show how the approach enables a compiler that generates high-performance code, while staying simple enough to lend itself to formal reasoning. The dissertation is divided into three main contributions. First, I present an abstract machine, the \ce machine, which formalizes the approach. Second, I show that it can be implemented as a native code compiler with encouraging performance results. Finally, I present a verified compiler, implemented in the Coq proof assistant, demonstrating how the simplicity of the approach enables formal verification
Programming with narrowing: A tutorial
AbstractNarrowing is a computation implemented by some declarative programming languages. Research in the last decade has produced significant results on the theory and foundation of narrowing, but little has been published on the use of narrowing in programming. This paper introduces narrowing from a programmer’s viewpoint; shows, by means of examples, when, why and how to use narrowing in a program; and discusses the impact of narrowing on software development activities such as design and maintenance. The examples are coded in the programming language Curry, which provides narrowing as a first class feature
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