156,118 research outputs found
Towards Theory of Massive-Parallel Proofs. Cellular Automata Approach
In the paper I sketch a theory of massively parallel proofs using cellular
automata presentation of deduction. In this presentation inference rules play
the role of cellular-automatic local transition functions. In this approach we
completely avoid axioms as necessary notion of deduction theory and therefore
we can use cyclic proofs without additional problems. As a result, a theory of
massive-parallel proofs within unconventional computing is proposed for the
first time.Comment: 13 page
Genetic Algorithm Modeling with GPU Parallel Computing Technology
We present a multi-purpose genetic algorithm, designed and implemented with
GPGPU / CUDA parallel computing technology. The model was derived from a
multi-core CPU serial implementation, named GAME, already scientifically
successfully tested and validated on astrophysical massive data classification
problems, through a web application resource (DAMEWARE), specialized in data
mining based on Machine Learning paradigms. Since genetic algorithms are
inherently parallel, the GPGPU computing paradigm has provided an exploit of
the internal training features of the model, permitting a strong optimization
in terms of processing performances and scalability.Comment: 11 pages, 2 figures, refereed proceedings; Neural Nets and
Surroundings, Proceedings of 22nd Italian Workshop on Neural Nets, WIRN 2012;
Smart Innovation, Systems and Technologies, Vol. 19, Springe
Some Considerations about Modern Database Machines
Optimizing the two computing resources of any computing system - time and space - has al-ways been one of the priority objectives of any database. A current and effective solution in this respect is the computer database. Optimizing computer applications by means of database machines has been a steady preoccupation of researchers since the late seventies. Several information technologies have revolutionized the present information framework. Out of these, those which have brought a major contribution to the optimization of the databases are: efficient handling of large volumes of data (Data Warehouse, Data Mining, OLAP – On Line Analytical Processing), the improvement of DBMS – Database Management Systems facilities through the integration of the new technologies, the dramatic increase in computing power and the efficient use of it (computer networks, massive parallel computing, Grid Computing and so on). All these information technologies, and others, have favored the resumption of the research on database machines and the obtaining in the last few years of some very good practical results, as far as the optimization of the computing resources is concerned.Database Optimization, Database Machines, Data Warehouse, OLAP – On Line Analytical Processing, OLTP – On Line Transaction Processing, Parallel Processing
Extensible Context-aware Stream Processing on the Cloud
Rationale and Challenges for Massive Data Stream Processing on the CloudThe ubiquity of mobile devices, location services, and sensor pervasiveness, e.g., as in smart city initiatives, call for scalable computing platforms and massively parallel architectures to process the vast amounts of the generated streamed data. Cloud computing provides some of the features needed for these massive data streaming applications. For example, the dynamic allocation of resources on an as-needed basis addresses the variability in sensor and location data distributions over time. However, today’s cloud computing platforms lack very important features that are necessary in order to support the massive amounts of data streams envisioned by the massive and ubiquitous dissemination of sensors and mobile devices of all sorts in smart-city-scale applications
A Review of Lightweight Thread Approaches for High Performance Computing
High-level, directive-based solutions are becoming the programming models (PMs) of the multi/many-core architectures. Several solutions relying on operating system (OS) threads perfectly work with a moderate number of cores. However, exascale systems will spawn hundreds of thousands of threads in order to exploit their massive parallel architectures and thus conventional OS threads are too heavy for that purpose. Several lightweight thread (LWT) libraries have recently appeared offering lighter mechanisms to tackle massive concurrency. In order to examine the suitability of LWTs in high-level runtimes, we develop a set of microbenchmarks consisting of commonly-found patterns in current parallel codes. Moreover, we study the semantics offered by some LWT libraries in order to expose the similarities between different LWT application programming interfaces. This study reveals that a reduced set of LWT functions can be sufficient to cover the common parallel code patterns andthat those LWT libraries perform better than OS threads-based solutions in cases where task and nested parallelism are becoming more popular with new architectures.The researchers from the Universitat Jaume I de CastellĂł were supported by project TIN2014-53495-R of the MINECO, the Generalitat Valenciana fellowship programme Vali+d 2015, and FEDER. This work was partially supported by the U.S. Dept. of Energy, Office of Science, Office of Advanced
Scientific Computing Research (SC-21), under contract DEAC02-06CH11357. We gratefully acknowledge the computing resources provided and operated by the Joint Laboratory for System Evaluation (JLSE) at Argonne National Laboratory.Peer ReviewedPostprint (author's final draft
Efficient Generation of Parallel Spin-images Using Dynamic Loop Scheduling
High performance computing (HPC) systems underwent a significant increase in
their processing capabilities. Modern HPC systems combine large numbers of
homogeneous and heterogeneous computing resources. Scalability is, therefore,
an essential aspect of scientific applications to efficiently exploit the
massive parallelism of modern HPC systems. This work introduces an efficient
version of the parallel spin-image algorithm (PSIA), called EPSIA. The PSIA is
a parallel version of the spin-image algorithm (SIA). The (P)SIA is used in
various domains, such as 3D object recognition, categorization, and 3D face
recognition. EPSIA refers to the extended version of the PSIA that integrates
various well-known dynamic loop scheduling (DLS) techniques. The present work:
(1) Proposes EPSIA, a novel flexible version of PSIA; (2) Showcases the
benefits of applying DLS techniques for optimizing the performance of the PSIA;
(3) Assesses the performance of the proposed EPSIA by conducting several
scalability experiments. The performance results are promising and show that
using well-known DLS techniques, the performance of the EPSIA outperforms the
performance of the PSIA by a factor of 1.2 and 2 for homogeneous and
heterogeneous computing resources, respectively
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