Automatic Parallelization of Data-Driven JStar Programs

Data-driven problems have common characteristics: a large number of small objects with complex dependencies. This makes the traditional parallel programming approaches more difficult to apply as pipe-lining the task dependencies may require to rewrite or recompile the program into efficient parallel implementations. This thesis focuses on data-driven JStar programs that have rules triggered by the tuples from a bulky CSV file or from other sources of complex data, and making those programs run fast in parallel. JStar is a new declarative language for parallel programming that encourages programmers to write their applications with implicit parallelism. The thesis briefly introduces the JStar language and the implicit default parallelism of the JStar compiler. It describes the root causes of the poor performance of the naive parallel JStar programs and defines a performance tuning process to increase the speed of JStar programs as the number of cores increases and to minimize the memory usage in the Java Heap. Several graphic analysis tools were developed to allow easier analysis of bottlenecks in parallel programs. The JStar compiler and runtime were extended so that it is easy to apply a variety of optimisations to a JStar program without changing the JStar source code. This process was applied to four case studies which were benchmarked on different multi-core machines to measure the performance and scalability of JStar programs

The JStar language philosophy

This paper introduces the JStar parallel programming language, which is a Java-based declarative language aimed at discouraging sequential programming, en-couraging massively parallel programming, and giving the compiler and runtime maximum freedom to try alternative parallelisation strategies. We describe the execution semantics and runtime support of the language, several optimisations and parallelism strategies, with some benchmark results

Efficient compilation of a verification-friendly programming language

This thesis develops a compiler to convert a program written in the verification friendly programming language Whiley into an efficient implementation in C. Our compiler uses a mixture of static analysis, run-time monitoring and a code generator to and faster integer types, eliminate unnecessary array copies and de-allocate unused memory without garbage collection, so that Whiley programs can be translated into C code to run fast and for long periods on general operating systems as well as limited-resource embedded devices. We also present manual and automatic proofs to verify memory safety of our implementations, and benchmark on a variety of test cases for practical use. Our benchmark results show that, in our test suite, our compiler effectively reduces the time complexity to the lowest possible level and stops all memory leaks without causing double-freeing problems. The performance of implementations can be further improved by choosing proper integer types within the ranges and exploiting parallelism in the programs

Convergence of public participation, participatory design and NLP to co-develop circular economy

The concept of a circular economy is at a crossroads. To date, it has been largely driven by top-down national or trans-national legislation such as EU Circular Economy Package or Chinese Circular Economy Promotion. Bottom-up or grassroots engagement has been minimal and innovation has been largely restricted to use of 3R or 4R frameworks to reduce, reuse, recycle materials and products or recover waste as energy. Greater implementation of a circular economy needs a paradigm shift in public attitudes coupled with greater innovation that moves from 3R to 9R framework. Building on the insights gained from the successful transition of two Scandinavian cities of Växjö and Sønderborg to becoming fossil fuel-free economies, the article explores use of natural language processing tools to create shared narratives and stories from large format public engagement that expresses common interests, values and priorities that in turn can contribute to a participatory design of a circular economy. The article uses published data from Christchurch ‘Share an Idea’ as a case study

Static techniques for reducing memory usage in the C implementation of Whiley programs

Languages that use call-by-value semantics, such as Whiley, can make program verification easier. But effcient implementation becomes harder, due to the overhead of copying and garbage collection. This paper describes how a mixture of static analysis and runtime-monitoring can be used to eliminate unnecessary copying and deallocate memory with- out garbage collection. We show that this allows Whiley programs to be translated into effcient C implementations

AI augmented approach to identify shared ideas from large format public consultation

Public data, contributed by citizens, stakeholders and other potentially affected parties, are becoming increasingly used to collect the shared ideas of a wider community. Having collected large quantities of text data from public consultation, the challenge is often how to interpret the dataset without resorting to lengthy time-consuming manual analysis. One approach gaining ground is the use of Natural Language Processing (NLP) technologies. Based on machine learning technology applied to analysis of human natural languages, NLP provides the opportunity to automate data analysis for large volumes of texts at a scale that would be virtually impossible to analyse manually. Using NLP toolkits, this paper presents a novel approach for identifying and visualising shared ideas from large format public consultation. The approach analyses grammatical structures of public texts to discover shared ideas from sentences comprising subject + verb + object and verb + object that express public options. In particular, the shared ideas are identified by extracting noun, verb, adjective phrases and clauses from subjects and objects, which are then categorised by urban infrastructure categories and terms. The results are visualised in a hierarchy chart and a word tree using cascade and tree views. The approach is illustrated using data collected from a public consultation exercise called “Share an Idea” undertaken in Christchurch, New Zealand, after the 2011 earthquake. The approach has the potential to upscale public participation to identify shared design values and associated qualities for a wide range of public initiatives including urban planning

Urban narrative: Value based urban design

Urban Narrative focuses on the social dimension within urban design which is often overlooked in practice which examines human values and needs as a means to inform design practice. The project has been working in partnership with two councils in New Zealand in relation to the social dimension

miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions

MicroRNAs (miRNAs) are small non-coding RNA molecules capable of negatively regulating gene expression to control many cellular mechanisms. The miRTarBase database (http://mirtarbase.mbc.nctu.edu.tw/) provides the most current and comprehensive information of experimentally validated miRNA-target interactions. The database was launched in 2010 with data sources for >100 published studies in the identification of miRNA targets, molecular networks of miRNA targets and systems biology, and the current release (2013, version 4) includes significant expansions and enhancements over the initial release (2010, version 1). This article reports the current status of and recent improvements to the database, including (i) a 14-fold increase to miRNA-target interaction entries, (ii) a miRNA-target network, (iii) expression profile of miRNA and its target gene, (iv) miRNA target-associated diseases and (v) additional utilities including an upgrade reminder and an error reporting/user feedback system

AI Augmented Approach to Identify Shared Ideas from Large Format Public Consultation

Public data, contributed by citizens, stakeholders and other potentially affected parties, are becoming increasingly used to collect the shared ideas of a wider community. Having collected large quantities of text data from public consultation, the challenge is often how to interpret the dataset without resorting to lengthy time-consuming manual analysis. One approach gaining ground is the use of Natural Language Processing (NLP) technologies. Based on machine learning technology applied to analysis of human natural languages, NLP provides the opportunity to automate data analysis for large volumes of texts at a scale that would be virtually impossible to analyse manually. Using NLP toolkits, this paper presents a novel approach for identifying and visualising shared ideas from large format public consultation. The approach analyses grammatical structures of public texts to discover shared ideas from sentences comprising subject + verb + object and verb + object that express public options. In particular, the shared ideas are identified by extracting noun, verb, adjective phrases and clauses from subjects and objects, which are then categorised by urban infrastructure categories and terms. The results are visualised in a hierarchy chart and a word tree using cascade and tree views. The approach is illustrated using data collected from a public consultation exercise called “Share an Idea” undertaken in Christchurch, New Zealand, after the 2011 earthquake. The approach has the potential to upscale public participation to identify shared design values and associated qualities for a wide range of public initiatives including urban planning