A metadata extracting tool for software components in grid applications

Component-based programming aims at producing higher quality software, increasing the reuse of components and permitting late composition. In the context of component-based programming, applications are treated as composition of components. Given an application composition, some of the components might have been developed outside the context of the application or its domain. As a result, the overall efficiency of the composition, in terms of cost and performance, becomes non-deterministic - may not be guaranteed to be efficient enough, even if the individual components have been proven to be efficient. In other words, two primary goals of software practice, efficiency and quality, do conflict with each other. In this paper, we argue that, this problem can partly be overcome by paying more attention to component-specific information, component metadata, during composition. We describe a possible means of extracting and organising the metadata and formats for specifying the metadata. Our scheme is independent of component- and programming-models and extensible. We see our work as a precursor to a possible runtime scheme, where we intend to facilitate extraction, maintenance and usage of component metadata at runtime

Complexity plots

In this paper, we present a novel visualization technique for assisting in observation and analysis of algorithmic\ud complexity. In comparison with conventional line graphs, this new technique is not sensitive to the units of\ud measurement, allowing multivariate data series of different physical qualities (e.g., time, space and energy) to be juxtaposed together conveniently and consistently. It supports multivariate visualization as well as uncertainty visualization. It enables users to focus on algorithm categorization by complexity classes, while reducing visual impact caused by constants and algorithmic components that are insignificant to complexity analysis. It provides an effective means for observing the algorithmic complexity of programs with a mixture of algorithms and blackbox software through visualization. Through two case studies, we demonstrate the effectiveness of complexity plots in complexity analysis in research, education and application

MapReduce Particle Filtering with Exact Resampling and Deterministic Runtime

Particle filtering is a numerical Bayesian technique that has great potential for solving sequential estimation problems involving non-linear and non-Gaussian models. Since the estimation accuracy achieved by particle filters improves as the number of particles increases, it is natural to consider as many particles as possible. MapReduce is a generic programming model that makes it possible to scale a wide variety of algorithms to Big data. However, despite the application of particle filters across many domains, little attention has been devoted to implementing particle filters using MapReduce. In this paper, we describe an implementation of a particle filter using MapReduce. We focus on a component that what would otherwise be a bottleneck to parallel execution, the resampling component. We devise a new implementation of this component, which requires no approximations, has $O\left(N\right)$ spatial complexity and deterministic $O\left(\left(\log N\right)^2\right)$ time complexity. Results demonstrate the utility of this new component and culminate in consideration of a particle filter with $2^{24}$ particles being distributed across $512$ processor cores

Domain-specific metadata for model validation and performance optimisation

Domain-specific metadata for model validation and performance optimisation

MapReduce particle filtering with exact resampling and deterministic runtime

Particle filtering is a numerical Bayesian technique that has great potential for solving sequential estimation problems involving non-linear and non-Gaussian models. Since the estimation accuracy achieved by particle filters improves as the number of particles increases, it is natural to consider as many particles as possible. MapReduce is a generic programming model that makes it possible to scale a wide variety of algorithms to Big data. However, despite the application of particle filters across many domains, little attention has been devoted to implementing particle filters using MapReduce. In this paper, we describe an implementation of a particle filter using MapReduce. We focus on a component that what would otherwise be a bottleneck to parallel execution, the resampling component. We devise a new implementation of this component, which requires no approximations, has O(N) spatial complexity and deterministic O((logN)2) time complexity. Results demonstrate the utility of this new component and culminate in consideration of a particle filter with 224 particles being distributed across 512 processor cores

Advanced Grid programming with components: a biometric identification case study

Component-oriented software development has been attracting increasing attention for building complex distributed applications. A new infrastructure supporting this advanced concept is our prototype component framework based on the Grid component model. This paper provides an overview of the component framework and presents a case study where we utilise the component-oriented approach to develop a business process application for a biometric identification system. We then introduce the tools being developed as part of an integrated development environment to enable graphical component-based development of Grid applications. Finally, we report our initial findings and experiences of efficiently using the component framework and set of software tools

Gene Splicing of an Invertebrate Beta Subunit (LCav?) in the N-Terminal and HOOK Domains and Its Regulation of LCav1 and LCav2 Calcium Channels

The accessory beta subunit (Cavβ) of calcium channels first appear in the same genome as Cav1 L-type calcium channels in single-celled coanoflagellates. The complexity of this relationship expanded in vertebrates to include four different possible Cavβ subunits (β1, β2, β3, β4) which associate with four Cav1 channel isoforms (Cav1.1 to Cav1.4) and three Cav2 channel isoforms (Cav2.1 to Cav2.3). Here we assess the fundamentally-shared features of the Cavβ subunit in an invertebrate model (pond snail Lymnaea stagnalis) that bears only three homologous genes: (LCav1, LCav2, and LCavβ). Invertebrate Cavβ subunits (in flatworms, snails, squid and honeybees) slow the inactivation kinetics of Cav2 channels, and they do so with variable N-termini and lacking the canonical palmitoylation residues of the vertebrate β2a subunit. Alternative splicing of exon 7 of the HOOK domain is a primary determinant of a slow inactivation kinetics imparted by the invertebrate LCavβ subunit. LCavβ will also slow the inactivation kinetics of LCav3 T-type channels, but this is likely not physiologically relevant in vivo. Variable N-termini have little influence on the voltage-dependent inactivation kinetics of differing invertebrate Cavβ subunits, but the expression pattern of N-terminal splice isoforms appears to be highly tissue specific. Molluscan LCavβ subunits have an N-terminal “A” isoform (coded by exons: 1a and 1b) that structurally resembles the muscle specific variant of vertebrate β1a subunit, and has a broad mRNA expression profile in brain, heart, muscle and glands. A more variable “B” N-terminus (exon 2) in the exon position of mammalian β3 and has a more brain-centric mRNA expression pattern. Lastly, we suggest that the facilitation of closed-state inactivation (e.g. observed in Cav2.2 and Cavβ3 subunit combinations) is a specialization in vertebrates, because neither snail subunit (LCav2 nor LCavβ) appears to be compatible with this observed property