An automatic parallelization framework for multicomputers

Several researchers have looked into various issues related to automatic parallelization of sequential programs for multicomputers. But there is a need for a coherent framework which encompasses all these issues. In this paper we present a such a framework which takes best advantage of the multicomputer architecture. We resort to tiling transformation for iteration space partitioning and propose a scheme of automatic data partitioning and dynamic data distribution. We have tried a simple implementation of our scheme on a transputer based multicomputer [1] and the results are encouraging.

Reproductive phenology of a tropical dry forest in Mudumalai, Southern India

1 Flowering and fruiting phenologies of a tropical dry forest in Mudumalai, southern India, were studied between April 1988 and August 1990. Two sites, a wetter site I receiving 1100mm and a drier site II receiving 600mm of rainfall annually, are compared. A total of 286 trees from 38 species at site I and 167 trees from 27 species at site II was marked for phenological observations. There were 11 species common to the two sites. Several hypotheses relating to the evolution of reproductive phenology are tested. 2 Frequency of species flowering attained a peak at site I during the dry season but at site II, where soil moisture may be limiting during the dry months, the peak was during the wet season. At both sites a majority of species flushed leaves and flowered simultaneously. Among various guilds, the bird-pollinated guild showed distinct dry season flowering, which may be related to better advertisement of large flowers to pollinators during the leafless dry phase. The wind-pollinated guild flowered mainly during the wet season, when wind speeds are highest and favourable for pollen transport. The insect-pollinated guild showed no seasonality in flowering in site I but a wet season flowering in site II. 3 Fruiting frequency attained a peak in site I during the late wet season extending into the early dry season; a time-lag correlation showed that fruiting followed rainfall with a lag of about two months. Site II showed a similar fruiting pattern but this was not statistically significant. The dispersal guilds (animal, wind, and explosive passively-dispersed) did not show any clear seasonality in fruiting, except for the animal-dispersed guild which showed wet season fruiting in site I. 4 Hurlbert's overlap index was also calculated in order to look at synchrony in flowering and fruiting irrespective of climatic (dry and wet month) seasonality. In general, overlap in flowering and fruiting guilds was high because of seasonal aggregation. Among the exceptions, at site II the wind-pollinated flowering guild did not show significant overlap between species although flowering aggregated in the wet season. This could be due to the need to avoid heterospecific pollen transfer. 5 Rarer species tended to flower earlier in the dry season and this again could be an adaptation to avoid the risk of heterospecific pollen transfer or competition for pollinators. The more abundant species flowered mainly during the wet season. Species which flower earlier have larger flowers and, having invested more energy in flowers, also have shorter flower to fruit durations.

Status Survey of the Malabar Pied Hornbill in the Dandeli Region, Northern Western Ghats, India

The Malabar Pied Hornbill, Anthracoceros coronatus, is a near threatened species, endemic to the tropical deciduous forests of central and southern India and Sri Lanka. The Dandeli region in Karnataka (India) is believed to be the last stronghold of this species in the Western Ghats biodiversity hotspot. Being a rapidly developing area with a growing human population, the threats to this species and their habitat are mounting, especially due to a large number of hydroelectric projects and habitat fragmentation caused by paper and plywood industries. This study evaluated the change in population status of the Malabar Pied Hornbill over a 23 year period and defined priorities for the long term conservation and monitoring of hornbills in Dandeli. Encounter rates of hornbills were also analysed in relation to the density and species richness of trees and fruiting trees, basal area, canopy cover and distance from river. Hornbill encounters were not significantly different compared to the earlier study carried out by Reddy in 1988, but were significantly different across the five sites in the current study. Higher numbers of hornbills were encountered closer to the river, but these results were only marginally significant. The mean numbers of hornbills recorded at the two roost sites identified in Dandeli were 26 +/- 4.47 (n=16 counts) and 31.78 +/- 3.53 (n=14 counts) respectively. The study also helped build local awareness about the species, train local Forest Department staff in monitoring hornbills and develop a management plan for its conservation.

Fault-Diagnostic System using Support Vector Machines for Power Transmission Systems

This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.

Intelligent Approach for Fault Diagnosis in Power Transmission Systems Using Support Vector Machines

This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.

Phylogeny of the Asian Eutropis (Squamata: Scincidae) reveals an `into India' endemic Indian radiation

Recent generic rearrangement of the circumtropical distributed skink genus `Mabuya' has raised a lot of debate. According to this molecular phylogeny based rearrangement, the tropical Asian members of this genus have been assigned to Eutropis. However, in these studies the Asian members of `Mabuya' were largely sampled from Southeast (SE) Asia with very few species from Indian subcontinent. To test the validity of this assignment and to determine the evolutionary origin of Indian members of this group we sequenced one nuclear and two mitochondrial genes from most of the species from the Indian subregion. The nuclear and mitochondrial trees generated from these sequences confirmed the monophyly of the tropical Asian Eutropis. Furthermore, in the tree based on the combined mitochondrial and nuclear dataset an endemic Indian radiation was revealed that was nested within a larger Asian clade. Results of dispersal-vicariance analysis and molecular dating suggested an initial dispersal of Eutropis from SE Asia into India around 5.5-17 million years ago, giving rise to the extant members of the endemic Indian radiation. This initial dispersal was followed by two back dispersals from India into SE Asia. We also discuss the relationships within the endemic Indian radiation and its taxonomic implications. (c) 2012 Elsevier Inc. All rights reserved.

A Diffusion-Based Processor Reallocation Strategy for Tracking Multiple Dynamically Varying Weather Phenomena

Many meteorological phenomena occur at different locations simultaneously. These phenomena vary temporally and spatially. It is essential to track these multiple phenomena for accurate weather prediction. Efficient analysis require high-resolution simulations which can be conducted by introducing finer resolution nested simulations, nests at the locations of these phenomena. Simultaneous tracking of these multiple weather phenomena requires simultaneous execution of the nests on different subsets of the maximum number of processors for the main weather simulation. Dynamic variation in the number of these nests require efficient processor reallocation strategies. In this paper, we have developed strategies for efficient partitioning and repartitioning of the nests among the processors. As a case study, we consider an application of tracking multiple organized cloud clusters in tropical weather systems. We first present a parallel data analysis algorithm to detect such clouds. We have developed a tree-based hierarchical diffusion method which reallocates processors for the nests such that the redistribution cost is less. We achieve this by a novel tree reorganization approach. We show that our approach exhibits up to 25% lower redistribution cost and 53% lesser hop-bytes than the processor reallocation strategy that does not consider the existing processor allocation.