Location decision-making of 'creative' industries: the media and computer games sectors in Dublin, Ireland

There has been significant research undertaken examining the “creative class” thesis within the context of the locational preferences of creative workers. However, relatively little attention has been given to the locational preferences of creative companies within the same context. This paper reports on research conducted to qualitatively analyse the location decision making of companies in two creative sectors: media and computer games. We address the role of the so-called “hard” and “soft” factors in company location decision making within the context of the creative class thesis, which suggests that company location is primarily determined by “soft” factors rather than “hard” factors. The study focuses upon “core” creative industries in the media and computer game sectors and utilises interview data with company managers and key elite actors in the sector to investigate the foregoing questions. The results show that “hard” factors are of primary importance for the location decision making in the sectors analysed, but that “soft” factors play quite an important role when “hard” factors are satisfactory in more than one competing city-region.

A green perspective on Structured parallel programming

Structured parallel programming, and in particular programming models using the algorithmic skeleton or parallel design pattern concepts, are increasingly considered to be the only viable means of supporting effective development of scalable and efficient parallel programs. Structured parallel programming models have been assessed in a number of works in the context of performance. In this paper we consider how the use of structured parallel programming models allows knowledge of the parallel patterns present to be harnessed to address both performance and energy consumption. We consider different features of structured parallel programming that may be leveraged to impact the performance/energy trade-off and we discuss a preliminary set of experiments validating our claims.

The Loop-of-Stencil-Reduce Paradigm

In this paper we advocate the Loop-of-stencil-reduce pattern as a way to simplify the parallel programming of heterogeneous platforms (multicore+GPUs). Loop-of-Stencil-reduce is general enough to subsume map, reduce, map-reduce, stencil, stencil-reduce, and, crucially, their usage in a loop. It transparently targets (by using OpenCL) combinations of CPU cores and GPUs, and it makes it possible to simplify the deployment of a single stencil computation kernel on different GPUs. The paper discusses the implementation of Loop-of-stencil-reduce within the FastFlow parallel framework, considering a simple iterative data-parallel application as running example (Game of Life) and a highly effective parallel filter for visual data restoration to assess performance. Thanks to the high-level design of the Loop-of-stencil-reduce, it was possible to run the filter seamlessly on a multicore machine, on multi-GPUs, and on both.