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This paper examines the impact of parallelism models and the Global Interpreter Lock on the performance of Python applications interacting with an analytical DBMS. The study focuses on typical workloads that include data...

Photonic reservoir computing (RC) is a simple and efficient neuromorphic computing framework for human cortical circuits, which is featured with fast training speed and low training cost. Photonic time delay RC, as a sim...

Abstract Cable-driven parallel robots (CDPRs) use cables instead of the rigid limbs of traditional parallel robots, thus processing a large potential workspace, easy to assemble and disassemble characteristics, and with ...

Accurately forecasting the high-dimensional chaotic dynamics of semiconductor laser (SL) networks is essential in photonics research. In this study, we propose a spatiotemporal multiplexed photonic reservoir computing (S...

Images and videos provide a wealth of information for people in production and life. Although most digital information is transmitted via optical fiber, the image acquisition and transmission processes still rely heavily...

IntroductionMalodors in the human body can diminish comfort in living and working environments, potentially increasing stress and dissatisfaction levels. Therefore, there is a growing need to objectively characterize the...

Photonic neural networks are brain-inspired information processing technology using photons instead of electrons to perform artificial intelligence (AI) tasks. However, existing architectures are designed for a single ta...

The development of parallel algorithms for an automatic recognition and classification of objects from an industrial line (either production or packaging) is presented. This kind of problem introduces a temporal restrict...

Features such as fast response, storage efficiency, fault tolerance and graceful degradation in face of scarce or spurious inputs make neural networks appropriate tools for Intelligent Computer Systems. A neural network ...

Molecular Dynamics (MD) simulations can help to utimingnderstand an immense number of phenomena at the nano and microscale. They often require the exploration of large parameter space, and a possible parallelization str...

Parallel performance optimization is being applied and further improvements are studied for parallel linear algebra on clusters. Several parallelization guidelines have been defined and are being used on single clusters ...

In scheduling, a set of machines in parallel is a setting that is important, from both the theoretical and practical points of view. From the theoretical viewpoint, it is a generalization of the single machine scheduling...

Allocation of the components (tasks) of a parallel program to processors in a multiprocessor or a multicomputer system take full advantage of the computational power provided by the system. Evolutionary approaches has b...

Learning algorithms for neural networks involve CPU intensive processing and consequently great effort has been done to develop parallel implemetations intended for a reduction of learning time. This work briefly descri...

A parallel program, when running, can be conceived a set of parallel components (tasks) which can be executed according to sorne precedence relationship. In this case efficient scheduling of tasks permits to take full.a...

Image mining deals with the study and development of new technologies that allow accomplishing this subject. A common mistake about image mining is identifying its scopes and limitations. Clearly it is different from com...

The problem of how to find a schedule on m > 2 processors of equal capacity that minimises the whole processing time of independent tasks has been shown as belonging to the NP-complete class (Horowitz and Sahni [12]). Ev...

This paper analyses the parallel implementation using networks of transputers of a neural structure belonging to a particular class of neural architectures known as GSN neural networks. These architectures, belonging to ...

Parallel machine scheduling, also known as parallel task scheduling, involves the assignment of multiple tasks onto the system architecture’s processing components (a bank of machines in parallel). A basic model invol...

El análisis de imágenes puede revelar información útil para los usuarios El significativo aumento del uso de imágenes en diferentes campos de la ciencia, medicina, negocios, etc., requiere de mayor poder de procesam...

Fast response, storage efficiency, fault tolerance and graceful degradation in face of scarce or spurious inputs make neural networks appropiate tools for Intelligent Computer Systems. But on the other hand, learning alg...

Se desarrolló el modelo TTIGHa utilizado para modelizar y predecir performance de aplicaciones paralelas que se ejecutan sobre arquitecturas heterogéneas. Además, se implementó el algoritmo de asignación de tareas ...

The TTIGHa model has been developed to model and predict the performance of parallel applications run over heterogeneous architectures. In addition, the task assignment algorithm was implemented to MATEHa processors base...

The Self Distributing Virtual Machine (SDVM) is a middleware concept to form a parallel computing machine consisting of a any set of processing units, such as functional units in a processor or FPGA, processing units in ...