10,847 research outputs found
Adaptive Load Balancing: A Study in Multi-Agent Learning
We study the process of multi-agent reinforcement learning in the context of
load balancing in a distributed system, without use of either central
coordination or explicit communication. We first define a precise framework in
which to study adaptive load balancing, important features of which are its
stochastic nature and the purely local information available to individual
agents. Given this framework, we show illuminating results on the interplay
between basic adaptive behavior parameters and their effect on system
efficiency. We then investigate the properties of adaptive load balancing in
heterogeneous populations, and address the issue of exploration vs.
exploitation in that context. Finally, we show that naive use of communication
may not improve, and might even harm system efficiency.Comment: See http://www.jair.org/ for any accompanying file
AiiDA: Automated Interactive Infrastructure and Database for Computational Science
Computational science has seen in the last decades a spectacular rise in the
scope, breadth, and depth of its efforts. Notwithstanding this prevalence and
impact, it is often still performed using the renaissance model of individual
artisans gathered in a workshop, under the guidance of an established
practitioner. Great benefits could follow instead from adopting concepts and
tools coming from computer science to manage, preserve, and share these
computational efforts. We illustrate here our paradigm sustaining such vision,
based around the four pillars of Automation, Data, Environment, and Sharing. We
then discuss its implementation in the open-source AiiDA platform
(http://www.aiida.net), that has been tuned first to the demands of
computational materials science. AiiDA's design is based on directed acyclic
graphs to track the provenance of data and calculations, and ensure
preservation and searchability. Remote computational resources are managed
transparently, and automation is coupled with data storage to ensure
reproducibility. Last, complex sequences of calculations can be encoded into
scientific workflows. We believe that AiiDA's design and its sharing
capabilities will encourage the creation of social ecosystems to disseminate
codes, data, and scientific workflows.Comment: 30 pages, 7 figure
Grid-Brick Event Processing Framework in GEPS
Experiments like ATLAS at LHC involve a scale of computing and data
management that greatly exceeds the capability of existing systems, making it
necessary to resort to Grid-based Parallel Event Processing Systems (GEPS).
Traditional Grid systems concentrate the data in central data servers which
have to be accessed by many nodes each time an analysis or processing job
starts. These systems require very powerful central data servers and make
little use of the distributed disk space that is available in commodity
computers. The Grid-Brick system, which is described in this paper, follows a
different approach. The data storage is split among all grid nodes having each
one a piece of the whole information. Users submit queries and the system will
distribute the tasks through all the nodes and retrieve the result, merging
them together in the Job Submit Server. The main advantage of using this system
is the huge scalability it provides, while its biggest disadvantage appears in
the case of failure of one of the nodes. A workaround for this problem involves
data replication or backup.Comment: 6 pages; document for CHEP'03 conferenc
Feeling what you hear: tactile feedback for navigation of audio graphs
Access to digitally stored numerical data is currently very limited for sight impaired people. Graphs and visualizations are often used to analyze relationships between numerical data, but the current methods of accessing them are highly visually mediated. Representing data using audio feedback is a common method of making data more accessible, but methods of navigating and accessing the data are often serial in nature and laborious. Tactile or haptic displays could be used to provide additional feedback to support a point-and-click type interaction for the visually impaired. A requirements capture conducted with sight impaired computer users produced a review of current accessibility technologies, and guidelines were extracted for using tactile feedback to aid navigation. The results of a qualitative evaluation with a prototype interface are also presented. Providing an absolute position input device and tactile feedback allowed the users to explore the graph using tactile and proprioceptive cues in a manner analogous to point-and-click techniques
Teaching Parallel Programming Using Java
This paper presents an overview of the "Applied Parallel Computing" course
taught to final year Software Engineering undergraduate students in Spring 2014
at NUST, Pakistan. The main objective of the course was to introduce practical
parallel programming tools and techniques for shared and distributed memory
concurrent systems. A unique aspect of the course was that Java was used as the
principle programming language. The course was divided into three sections. The
first section covered parallel programming techniques for shared memory systems
that include multicore and Symmetric Multi-Processor (SMP) systems. In this
section, Java threads was taught as a viable programming API for such systems.
The second section was dedicated to parallel programming tools meant for
distributed memory systems including clusters and network of computers. We used
MPJ Express-a Java MPI library-for conducting programming assignments and lab
work for this section. The third and the final section covered advanced topics
including the MapReduce programming model using Hadoop and the General Purpose
Computing on Graphics Processing Units (GPGPU).Comment: 8 Pages, 6 figures, MPJ Express, MPI Java, Teaching Parallel
Programmin
EAGLE—A Scalable Query Processing Engine for Linked Sensor Data
Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.EC/H2020/732679/EU/ACTivating InnoVative IoT smart living environments for AGEing well/ACTIVAGEEC/H2020/661180/EU/A Scalable and Elastic Platform for Near-Realtime Analytics for The Graph of Everything/SMARTE
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