19,468 research outputs found
Producing Scheduling that Causes Concurrent Programs to Fail
A noise maker is a tool that seeds a concurrent program with conditional synchronization primitives (such as yield()) for the purpose of increasing the likelihood that a bug manifest itself. This work explores the theory and practice of choosing where in the program to induce such thread switches at runtime. We introduce a novel fault model that classifies locations as .good., .neutral., or .bad,. based on the effect of a thread switch at the location. Using the model we explore the terms in which efficient search for real-life concurrent bugs can be carried out. We accordingly justify the use of probabilistic algorithms for this search and gain a deeper insight of the work done so far on noise-making. We validate our approach by experimenting with a set of programs taken from publicly available multi-threaded benchmark. Our empirical evidence demonstrates that real-life behavior is similar to what our model predicts
Probabilistic Graphical Models on Multi-Core CPUs using Java 8
In this paper, we discuss software design issues related to the development
of parallel computational intelligence algorithms on multi-core CPUs, using the
new Java 8 functional programming features. In particular, we focus on
probabilistic graphical models (PGMs) and present the parallelisation of a
collection of algorithms that deal with inference and learning of PGMs from
data. Namely, maximum likelihood estimation, importance sampling, and greedy
search for solving combinatorial optimisation problems. Through these concrete
examples, we tackle the problem of defining efficient data structures for PGMs
and parallel processing of same-size batches of data sets using Java 8
features. We also provide straightforward techniques to code parallel
algorithms that seamlessly exploit multi-core processors. The experimental
analysis, carried out using our open source AMIDST (Analysis of MassIve Data
STreams) Java toolbox, shows the merits of the proposed solutions.Comment: Pre-print version of the paper presented in the special issue on
Computational Intelligence Software at IEEE Computational Intelligence
Magazine journa
Logic programming in the context of multiparadigm programming: the Oz experience
Oz is a multiparadigm language that supports logic programming as one of its
major paradigms. A multiparadigm language is designed to support different
programming paradigms (logic, functional, constraint, object-oriented,
sequential, concurrent, etc.) with equal ease. This article has two goals: to
give a tutorial of logic programming in Oz and to show how logic programming
fits naturally into the wider context of multiparadigm programming. Our
experience shows that there are two classes of problems, which we call
algorithmic and search problems, for which logic programming can help formulate
practical solutions. Algorithmic problems have known efficient algorithms.
Search problems do not have known efficient algorithms but can be solved with
search. The Oz support for logic programming targets these two problem classes
specifically, using the concepts needed for each. This is in contrast to the
Prolog approach, which targets both classes with one set of concepts, which
results in less than optimal support for each class. To explain the essential
difference between algorithmic and search programs, we define the Oz execution
model. This model subsumes both concurrent logic programming
(committed-choice-style) and search-based logic programming (Prolog-style).
Instead of Horn clause syntax, Oz has a simple, fully compositional,
higher-order syntax that accommodates the abilities of the language. We
conclude with lessons learned from this work, a brief history of Oz, and many
entry points into the Oz literature.Comment: 48 pages, to appear in the journal "Theory and Practice of Logic
Programming
Darwinian Data Structure Selection
Data structure selection and tuning is laborious but can vastly improve an
application's performance and memory footprint. Some data structures share a
common interface and enjoy multiple implementations. We call them Darwinian
Data Structures (DDS), since we can subject their implementations to survival
of the fittest. We introduce ARTEMIS a multi-objective, cloud-based
search-based optimisation framework that automatically finds optimal, tuned DDS
modulo a test suite, then changes an application to use that DDS. ARTEMIS
achieves substantial performance improvements for \emph{every} project in
Java projects from DaCapo benchmark, popular projects and uniformly
sampled projects from GitHub. For execution time, CPU usage, and memory
consumption, ARTEMIS finds at least one solution that improves \emph{all}
measures for () of the projects. The median improvement across
the best solutions is , , for runtime, memory and CPU
usage.
These aggregate results understate ARTEMIS's potential impact. Some of the
benchmarks it improves are libraries or utility functions. Two examples are
gson, a ubiquitous Java serialization framework, and xalan, Apache's XML
transformation tool. ARTEMIS improves gson by \%, and for
memory, runtime, and CPU; ARTEMIS improves xalan's memory consumption by
\%. \emph{Every} client of these projects will benefit from these
performance improvements.Comment: 11 page
SICStus MT - A Multithreaded Execution Environment for SICStus Prolog
The development of intelligent software agents and other
complex applications which continuously interact with their
environments has been one of the reasons why explicit concurrency has
become a necessity in a modern Prolog system today. Such applications
need to perform several tasks which may be very different with respect
to how they are implemented in Prolog. Performing these tasks
simultaneously is very tedious without language support.
This paper describes the design, implementation and evaluation of a
prototype multithreaded execution environment for SICStus Prolog. The
threads are dynamically managed using a small and compact set of
Prolog primitives implemented in a portable way, requiring almost no
support from the underlying operating system
Garbage collection auto-tuning for Java MapReduce on Multi-Cores
MapReduce has been widely accepted as a simple programming pattern that can form the basis for efficient, large-scale, distributed data processing. The success of the MapReduce pattern has led to a variety of implementations for different computational scenarios. In this paper we present MRJ, a MapReduce Java framework for multi-core architectures. We evaluate its scalability on a four-core, hyperthreaded Intel Core i7 processor, using a set of standard MapReduce benchmarks. We investigate the significant impact that Java runtime garbage collection has on the performance and scalability of MRJ. We propose the use of memory management auto-tuning techniques based on machine learning. With our auto-tuning approach, we are able to achieve MRJ performance within 10% of optimal on 75% of our benchmark tests
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