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We present a unifying framework for information theoretic feature selection, bringing almost two decades of research on heuristic filter criteria under a single theoretical interpretation. This is in response to the question: " what are the implicit statistical assumptions of feature selection criteria based on mutual information? ". To answer this, we(More)
While transactional memory (TM) research on shared-memory chip multiprocessors has been flourishing over the last years,limited research has been conducted in the cluster domain. In this paper,we introduce a research platform for exploiting software TMon clusters. The distributed software transactional memory (DiSTM) system has been designed for easy(More)
Transactional Memory (TM) is a concurrent programming paradigm that aims to make concurrent programming easier than fine-grain locking, whilst providing similar performance and scalability. Several TM systems have been made available for research purposes. However , there is a lack of a wide range of non-trivial benchmarks with which to thoroughly evaluate(More)
In transactional memory, aborted transactions reduce performance , and waste computing resources. Ideally, concurrent execution of transactions should be optimally ordered to minimise aborts, but such an ordering is often either complex, or unfeasible, to obtain. This paper introduces a new technique called steal-on-abort, which aims to improve transaction(More)
Concurrency control for Transactional Memory (TM) is investigated as a means for improving resource usage by adjusting dynamically the number of threads concurrently executing transactions. The proposed control system takes as feedback the measured Transaction Commit Rate to adjust the concurrency. Through an extensive evaluation, a new Concurrency Control(More)
Real-time dense computer vision and SLAM offer great potential for a new level of scene modelling, tracking and real environmental interaction for many types of robot, but their high computational requirements mean that use on mass market embedded platforms is challenging. Meanwhile, trends in low-cost, low-power processing are towards massive parallelism(More)
Sorting has tremendous usage in the applications that handle massive amount of data. Existing techniques accelerate sorting using multiprocessors or GPGPUs where a data set is partitioned into disjunctive subsets to allow multiple sorting threads working in parallel. Hardware sorters implemented in FPGAs have the potential of providing high-speed and(More)
Transactional memory proposes an alternative synchronization primitive to traditional locks. Its promise is to simplify the software development of multi-threaded applications while at the same time delivering the performance of parallel applications using (complex and error prone) fine grain locking. This study reports our experience implementing a(More)
SpiNNaker is a massively parallel architecture designed to model large-scale spiking neural networks in (biological) real-time. Its design is based around <i>ad-hoc</i> multi-core System-on-Chips which are interconnected using a two-dimensional toroidal triangular mesh. Neurons are modeled in software and their spikes generate packets that propagate through(More)
Transactional applications may exhibit fluctuating amounts of contention during execution. Excessive numbers of threads executing transactions can produce phases with a high transaction abort ratio. while few threads executing transactions will under-perform in phases with low contention. This paper presents the first application of adaptive concurrency(More)