Abstract-Augmenting a processor with special hardware that is able to apply a Single Instruction to Multiple Data (SIMD) at the same time is a cost effective way of improving processor performance. It also offers a means of improving the ratio of processor performance to power usage due to reduced and more effective data movement and intrinsically lower instruction counts.This paper considers and compares the NEON SIMD instruction set used on the ARM Cortex-A series of RISC processors with the SSE2 SIMD instruction set found on Intel platforms within the context of the Open Computer Vision (OpenCV) library. The performance obtained using compiler auto-vectorization is compared with that achieved using hand-tuning across a range of five different benchmarks and ten different hardware platforms. On the ARM platforms the hand-tuned NEON benchmarks were between 1.05× and 13.88× faster than the auto-vectorized code, while for the Intel platforms the hand-tuned SSE benchmarks were between 1.34× and 5.54× faster.

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It is often the case that many algorithms exist to solve a single problem, each possessing different performance characteristics. The usual approach in this situation is to manually select the algorithm which has the best average performance. However, this strategy has drawbacks in cases where the optimal algorithm changes during an invocation of the program, in response to changes in the program's state and the computational environment. This paper presents a prototype tool that uses reinforcement learning to guide algorithm selection at runtime, matching the algorithm used to the current state of the computation. The tool is applied to a simulation similar to those used in some computational chemistry problems. It is shown that the low dimensionality of the problem enables the optimal choice of algorithm to be determined quickly, and that the learning system can react rapidly to phase changes in the target program.

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Written contracts are a fundamental framework for economic and cooperative transactions in society. Little work has been reported on the application of natural language processing or corpus linguistics to contracts. In this paper we report the design, profiling and initial analysis of a corpus of Australian contract language. This corpus enables a quantitative and qualitative characterisation of Australian contract language as an input to the development of contract drafting tools. Profiling of the corpus is consistent with its suitability for use in language engineering applications. We provide descriptive statistics for the corpus and show that document length and document vocabulary size approximate to log normal distributions. The corpus conforms to Zipf's law and comparative type to token ratios are consistent with lower term sparsity (an expectation for legal language). We highlight distinctive term usage in Australian contract language. Results derived from the corpus indicate a longer prepositional phrase depth in sentences in contract rules extracted from the corpus, as compared to other corpora.