Towards an Adaptive Skeleton Framework for Performance Portability

The proliferation of widely available, but very different, parallel architectures makes the ability to deliver good parallel performance on a range of architectures, or performance portability, highly desirable. Irregularly-parallel problems, where the number and size of tasks is unpredictable, are particularly challenging and require dynamic coordination. The paper outlines a novel approach to delivering portable parallel performance for irregularly parallel programs. The approach combines declarative parallelism with JIT technology, dynamic scheduling, and dynamic transformation. We present the design of an adaptive skeleton library, with a task graph implementation, JIT trace costing, and adaptive transformations. We outline the architecture of the protoype adaptive skeleton execution framework in Pycket, describing tasks, serialisation, and the current scheduler.We report a preliminary evaluation of the prototype framework using 4 micro-benchmarks and a small case study on two NUMA servers (24 and 96 cores) and a small cluster (17 hosts, 272 cores). Key results include Pycket delivering good sequential performance e.g. almost as fast as C for some benchmarks; good absolute speedups on all architectures (up to 120 on 128 cores for sumEuler); and that the adaptive transformations do improve performance

The Development and Effectiveness of GNVQs: Using Engineering as a Case Study.

This thesis is an investigation into the development and effectiveness of General National Vocational Qualifications (GNVQ), using the Engineering GNVQ as a case study. As part of this investigation I have examined the provision of vocational education and training (VET) since the start of the Industrial Revolution. There has been a continuing problem over the years in supplying enough appropriately trained personnel for the engineering industry in this country. Government policy has been, for most of the time, to leave training policy to the companies themselves. It is only in the second half of the twentieth century that governments have become involved, through statutes, in the provision of training for industry. In the 1970s and 80s there was also a political reason for providing training schemes for young people, which was growing youth unemployment. The method of research chosen for this investigation was: 1. Historical Research. This has involved examining the literature to examine the history of training in this country, though primary sources such Acts of Parliament, White Papers, and Official Reports, and secondary sources through examining the literature base. 2. Interviews and Questionnaires. This part of the research was carried out in two colleges in southern England, using both students and staff in these colleges. By using two methods I was able to confirm the results of the research by triangulation. The results have shown that there has been a continual lack of co-ordination in training between companies and the government until recent times, and the GNVQ was one of the methods introduced to break down the barriers between academic and vocational education. The results have also shown that the effects of the GNVQ to improve vocational education have been limited, and as a consequence changes are being implemented to the GNVQ to improve its record

Costing JIT Traces

Tracing JIT compilation generates units of compilation that are easy to analyse and are known to execute frequently. The AJITPar project aims to investigate whether the information in JIT traces can be used to make better scheduling decisions or perform code transformations to adapt the code for a specific parallel architecture. To achieve this goal, a cost model must be developed to estimate the execution time of an individual trace. This paper presents the design and implementation of a system for extracting JIT trace information from the Pycket JIT compiler. We define three increasingly parametric cost models for Pycket traces. We perform a search of the cost model parameter space using genetic algorithms to identify the best weightings for those parameters. We test the accuracy of these cost models for predicting the cost of individual traces on a set of loop-based micro-benchmarks. We also compare the accuracy of the cost models for predicting whole program execution time over the Pycket benchmark suite. Our results show that the weighted cost model using the weightings found from the genetic algorithm search has the best accuracy

Analysis of Digital Media: Supporting University-Wide Online Learning via Moodle

This report aims to provide an overview of a project which explores teaching and learning within a blended mode of study. Specifically, it looks to analyse the production of digital media and online social networking with a view to enhancing the learning experience. It was the overall aim of the project to contribute to the University’s Learning and Teaching Strategy by developing media content; exploring the production process, analyse digital participation and explore the challenges and opportunities locally within schools. The project has placed emphasis on the production principles which enhance our online courses whilst providing a consistent quality of experience – recognising that our students often access course material produced by staff from across schools and colleges

Sub-Pixel Technique for Time Series Analysis of Shoreline Changes Based on Multispectral Satellite Imagery

The measurement and monitoring of shoreline changes are of great interest to coastal managers and engineers. Shoreline change information can be crucial for the assessment of coastal disasters, design of coastal infrastructure and protection of coastal environment. This chapter presents shoreline change monitoring based on multispectral satellite imagery and sub-pixel technique. Firstly, a brief introduction of shoreline definitions and indicators is given. Sub-pixel techniques for shoreline mapping on multispectral satellite images are then introduced. Following that, a brief review of existing research studies of long-term shoreline change monitoring based on multispectral imagery is given. Subsequently, a case study of sub-pixel shoreline change monitoring at the northern Gold Coast on the east coast of Australia is presented. By comparing the longshore averaged beach widths at seven representative transects from Landsat with those from Argus imaging data, the RMSEs range from 9.1 to 12.3 m and the correlations are all no less than 0.7. Annual means and variabilities of beach widths were estimated without significant differences from the reference data for most of the results. Finally, conclusions and recommendations for future work are given

New Metrics for Spatial and Temporal 3D Urban Form Sustainability Assessment Using Time Series Lidar Point Clouds and Advanced GIS Techniques

Monitoring sustainability of urban form as a 3D phenomenon over time is crucial in the era of smart cities for better planning of the future, and for such a monitoring system, appropriate tools, metrics, methodologies and time series 3D data are required. While accurate time series 3D data are becoming available, a lack of 3D sustainable urban form (3D SUF) metrics, appropriate methodologies and technical problems of processing time series 3D data has resulted in few studies on the assessment of 3D SUF over time. In this chapter, we review volumetric building metrics currently under development and demonstrate the technical problems associated with their validation based on time series airborne lidar data. We propose new metrics for application in spatial and temporal 3D SUF assessment. We also suggest a new approach in processing time series airborne lidar to detect three-dimensional changes of urban form. Using this approach and the developed metrics, we detected a decreased volume of vegetation and new areas prepared for the construction of taller buildings. These 3D changes and the proposed metrics can be used to numerically measure and compare urban areas in terms of trends against or in favor of sustainability goals for caring for the environment

JIT costing adaptive skeletons for performance portability

The proliferation of widely available, but very different, parallel architectures makes the ability to deliver good parallel performance on a range of architectures, or performance portability, highly desirable. Irregular parallel problems, where the number and size of tasks is unpredictable, are particularly challenging and require dynamic coordination. The paper outlines a novel approach to delivering portable parallel performance for irregular parallel programs. The approach combines JIT compiler technology with dynamic scheduling and dynamic transformation of declarative parallelism. We specify families of algorithmic skeletons plus equations for rewriting skeleton expressions. We present the design of a framework that unfolds skeletons into task graphs, dynamically schedules tasks, and dynamically rewrites skeletons, guided by a lightweight JIT trace-based cost model, to adapt the number and granularity of tasks for the architecture. We outline the system architecture and prototype implementation in Racket/Pycket. As the current prototype does not yet automatically perform dynamic rewriting we present results based on manual offline rewriting, demonstrating that (i) the system scales to hundreds of cores given enough parallelism of suitable granularity, and (ii) the JIT trace cost model predicts granularity accurately enough to guide rewriting towards a good adaptive transformation

Building Detection Using LIDAR Data and Multispectral Images

A method the automatic detection of buildings from LIDAR data and multispectral images is presented. A classification technique using various cues derived from these data is applied in a hierarchic way to overcome the problems encountered in areas of heterogeneous appearance of buildings. Both first and last pulse data and the normalised difference vegetation index are used in that process. We describe the algorithms involved, giving examples for a test site in Fairfield (Victoria)

Detecting Buildings and Roof Segments by Combining LIDAR Data and Multispectral Images

A method for the automatic detection of buildings and their roof planes from LIDAR data and multispectral images is presented. For building detection, a classification technique is applied in a hierarchic way to overcome the problems encountered in areas of heterogeneous appearance of buildings. The detection of roof planes is based on a region growing algorithm applied to the LIDAR data, the seed regions detected by a grey-level segmentation of the multispectral images. We describe the algorithms involved, giving examples for a test site in Fairfield (Sydney)