Exploring carrier agents in swarm-array computing

How can a bridge be built between autonomic computing approaches and parallel computing systems? The work reported in this paper is motivated towards bridging this gap by proposing a swarm-array computing approach based on ‘Intelligent Agents’ to achieve autonomy for distributed parallel computing systems. In the proposed approach, a task to be executed on parallel computing cores is carried onto a computing core by carrier agents that can seamlessly transfer between processing cores in the event of a predicted failure. The cognitive capabilities of the carrier agents on a parallel processing core serves in achieving the self-ware objectives of autonomic computing, hence applying autonomic computing concepts for the benefit of parallel computing systems. The feasibility of the proposed approach is validated by simulation studies using a multi-agent simulator on an FPGA (Field-Programmable Gate Array) and experimental studies using MPI (Message Passing Interface) on a computer cluster. Preliminary results confirm that applying autonomic computing principles to parallel computing systems is beneficial.

A cluster-based implementation of a fault-tolerant parallel reduction algorithm using swarm-array computing

Recent research in multi-agent systems incorporate fault tolerance concepts. However, the research does not explore the extension and implementation of such ideas for large scale parallel computing systems. The work reported in this paper investigates a swarm array computing approach, namely ‘Intelligent Agents’. In the approach considered a task to be executed on a parallel computing system is decomposed to sub-tasks and mapped onto agents that traverse an abstracted hardware layer. The agents intercommunicate across processors to share information during the event of a predicted core/processor failure and for successfully completing the task. The agents hence contribute towards fault tolerance and towards building reliable systems. The feasibility of the approach is validated by simulations on an FPGA using a multi-agent simulator and implementation of a parallel reduction algorithm on a computer cluster using the Message Passing Interface.

Distributed acceleration of mobile radio network optimisation algorithms

The past decade has witnessed explosive growth of mobile subscribers and services. With the purpose of providing better-swifter-cheaper services, radio network optimisation plays a crucial role but faces enormous challenges. The concept of Dynamic Network Optimisation (DNO), therefore, has been introduced to optimally and continuously adjust network configurations, in response to changes in network conditions and traffic. However, the realization of DNO has been seriously hindered by the bottleneck of optimisation speed performance. An advanced distributed parallel solution is presented in this paper, as to bridge the gap by accelerating the sophisticated proprietary network optimisation algorithm, while maintaining the optimisation quality and numerical consistency. The ariesoACP product from Arieso Ltd serves as the main platform for acceleration. This solution has been prototyped, implemented and tested. Real-project based results exhibit a high scalability and substantial acceleration at an average speed-up of 2.5, 4.9 and 6.1 on a distributed 5-core, 9-core and 16-core system, respectively. This significantly outperforms other parallel solutions such as multi-threading. Furthermore, augmented optimisation outcome, alongside high correctness and self-consistency, have also been fulfilled. Overall, this is a breakthrough towards the realization of DNO.

Neurofuzzy mixture of experts network parallel learning and model construction algorithms

A connection between a fuzzy neural network model with the mixture of experts network (MEN) modelling approach is established. Based on this linkage, two new neuro-fuzzy MEN construction algorithms are proposed to overcome the curse of dimensionality that is inherent in the majority of associative memory networks and/or other rule based systems. The first construction algorithm employs a function selection manager module in an MEN system. The second construction algorithm is based on a new parallel learning algorithm in which each model rule is trained independently, for which the parameter convergence property of the new learning method is established. As with the first approach, an expert selection criterion is utilised in this algorithm. These two construction methods are equivalent in their effectiveness in overcoming the curse of dimensionality by reducing the dimensionality of the regression vector, but the latter has the additional computational advantage of parallel processing. The proposed algorithms are analysed for effectiveness followed by numerical examples to illustrate their efficacy for some difficult data based modelling problems.

Precision of quasi-optical null-balanced bridge techniques for transmission and reflection coefficient measurements

The precision of quasioptical null-balanced bridge instruments for transmission and reflection coefficient measurements at millimeter and submillimeter wavelengths is analyzed. A Jones matrix analysis is used to describe the amount of power reaching the detector as a function of grid angle orientation, sample transmittance/reflectance and phase delay. An analysis is performed of the errors involved in determining the complex transmission and reflection coefficient after taking into account the quantization error in the grid angle and micrometer readings, the transmission or reflection coefficient of the sample, the noise equivalent power of the detector, the source power and the post-detection bandwidth. For a system fitted with a rotating grid with resolution of 0.017 rad and a micrometer quantization error of 1 μm, a 1 mW source, and a detector with a noise equivalent power 5×10−9 W Hz−1/2, the maximum errors at an amplitude transmission or reflection coefficient of 0.5 are below ±0.025.

Parallel pipelined histogram architectures

Proposed is a unique cell histogram architecture which will process k data items in parallel to compute 2q histogram bins per time step. An array of m/2q cells computes an m-bin histogram with a speed-up factor of k; k ⩾ 2 makes it faster than current dual-ported memory implementations. Furthermore, simple mechanisms for conflict-free storing of the histogram bins into an external memory array are discussed.

Hyper-parallel tempering Monte Carlo simulations of Ar adsorption in new models of microporous non-graphitizing activated carbon: effect of microporosity

The adsorption of gases on microporous carbons is still poorly understood, partly because the structure of these carbons is not well known. Here, a model of microporous carbons based on fullerene- like fragments is used as the basis for a theoretical study of Ar adsorption on carbon. First, a simulation box was constructed, containing a plausible arrangement of carbon fragments. Next, using a new Monte Carlo simulation algorithm, two types of carbon fragments were gradually placed into the initial structure to increase its microporosity. Thirty six different microporous carbon structures were generated in this way. Using the method proposed recently by Bhattacharya and Gubbins ( BG), the micropore size distributions of the obtained carbon models and the average micropore diameters were calculated. For ten chosen structures, Ar adsorption isotherms ( 87 K) were simulated via the hyper- parallel tempering Monte Carlo simulation method. The isotherms obtained in this way were described by widely applied methods of microporous carbon characterisation, i. e. Nguyen and Do, Horvath - Kawazoe, high- resolution alpha(a)s plots, adsorption potential distributions and the Dubinin - Astakhov ( DA) equation. From simulated isotherms described by the DA equation, the average micropore diameters were calculated using empirical relationships proposed by different authors and they were compared with those from the BG method.

Simultaneous bridge-localized and mixed-valence character in diruthenium radical cations featuring diethynylaromatic bridging ligands

A series of bimetallic ruthenium complexes [{Ru(dppe)Cp*}2(μ-C≡CArC≡C)] featuring diethynylaromatic bridging ligands (Ar = 1,4-phenylene, 1,4-naphthylene, 9,10-anthrylene) have been prepared and some representative molecular structures determined. A combination of UV–vis–NIR and IR spectroelectrochemical methods and density functional theory (DFT) have been used to demonstrate that one-electron oxidation of compounds [{Ru(dppe)Cp*}2(μ-C≡CArC≡C)](HC≡CArC≡CH = 1,4-diethynylbenzene; 1,4-diethynyl-2,5-dimethoxybenzene; 1,4-diethynylnaphthalene; 9,10-diethynylanthracene) yields solutions containing radical cations that exhibit characteristics of both oxidation of the diethynylaromatic portion of the bridge, and a mixed-valence state. The simultaneous population of bridge-oxidized and mixed-valence states is likely related to a number of factors, including orientation of the plane of the aromatic portion of the bridging ligand with respect to the metal d-orbitals of appropriate π-symmetry.

Parallel pipelined array architectures for real-time histogram computation in consumer devices

The real-time parallel computation of histograms using an array of pipelined cells is proposed and prototyped in this paper with application to consumer imaging products. The array operates in two modes: histogram computation and histogram reading. The proposed parallel computation method does not use any memory blocks. The resulting histogram bins can be stored into an external memory block in a pipelined fashion for subsequent reading or streaming of the results. The array of cells can be tuned to accommodate the required data path width in a VLSI image processing engine as present in many imaging consumer devices. Synthesis of the architectures presented in this paper in FPGA are shown to compute the real-time histogram of images streamed at over 36 megapixels at 30 frames/s by processing in parallel 1, 2 or 4 pixels per clock cycle.