Lev Lafayette

High Performance Computing (HPC) is the most effective method to process increasingly large and complex datasets, making them increasingly critical for research organisations. Researchers wanting to use HPC resources often start with low levels of skills in using those systems. Despite this situation, educational programmes coming out of well-informed user needs analysis and/or a widely acknowledged set of required skills, capabilities and knowledge are rare.

For the past several years, I've been an active player of Ingress, a game where two competing factions play a sort of "capture-the-flag" of public locations of note using an augmentation of Google maps. The game, the precursor to Pokémon Go, and Harry Potter: Wizards Unite, has had its fair share of issues over the past six years. But on July 19, 2019, a death knell was sounded by the very company that produces the game; they forced players (albeit temporarily) to adopt the new interface, Ingress Prime, which is passionately hated by the overwhelming majority of the game's players, and for good reason. The interface is a radical change to the old version, has distracting effects, issues with visual accessibility, and is cumbersome to use. These issues have been raised by the player community for months now, but have largely fallen on deaf ears. Why is this? Why would a game company be so inattentive to the player base?

It is perhaps not so well known, but Niantic started off as a Google project, working on the Field Trip algorithm, which would push information to users on what the algorithm thinks you might be interested in, and with integration into Google Glass. There's a fascinating unlisted video on Youtube, with an astounding 22 million views, where you basically witness in all of two and a half minutes of how a person is turned on a thoughtless robot, the ideal consumer. Of course, such an algorithm can't make such decisions randomly, it has to know where a person goes, what their habits are and so forth. Trying to find out this information by surveys and the like would be onerous to the extreme; but Google Location Services can provide that data, and players will willingly give up such privacy for the entertainment of an Augmented Reality game, whether it is Ingress, Pokémon Go, or Harry Potter: Wizards Unite.

Gurobi is an optimisation solver, which describes itself as follows, thus explaining it's increasing popularity:

The Gurobi Optimizer is a state-of-the-art solver for mathematical programming. The solvers in the Gurobi Optimizer were designed from the ground up to exploit modern architectures and multi-core processors, using the most advanced implementations of the latest algorithms.

The following outlines the installation procedure on a Linux cluster, various licensing condundrums, and a sample job using Slurm.

Building software from source is necessary for performance and development reasons. However, this can come with complex dependency and compiler requirements, which have to be explicitly stated in research computing to ensure replication of results. EasyBuild, originally developed by the Julich Supercomputing Centre, the University of Gent, and the Texas Advanced Computing Center, is a tool that allows the building of software with ease, managing the complex dependencies and toolchains, and integrating by default with the Lmod environment modules system.

Last year I gave a presentation at Linux Users of Victoria entitled Being An Acrobat: Linux and PDFs (there was an additional discussion not in the presentation about embedding Javascript in a PDF and some related security issues, but that's for another post). Part of this presentation was singing the praises of Poppler Utilities (named after the Futurama episode, "The Problem with Popplers").

Introduction

More than thirty years ago, Professor Peter Checkland of the University of Lancaster, raised the question whether information systems (IS) and systems thinking could be united (Checkland, 1988). Almost twenty years later, Ray J. Paul, senior lecturer at the London School of Economics and Political Science also raised the disciplinary status of the subject, as editor the European Journal of Information Systems (Paul, 2007). These two papers are both illustrative of several others (e.g., Banville and Laundry, (1988)., George et. al., (2005)., Firth et. al., (2011)., Annabi and McGann, (2015)) from information systems as it attempts to find its own disciplinary boundaries among the crowd of academia, research, and vocational activities (c.f., Abraham et. al., (2006)., Benamati et. al., (2010).

The two papers are selecting not only to provide an at-a-glance illustration of the time-period of foundational issues within Information Systems as a discipline, but also the temporal context of each paper, and the differences in their views which, at least in part, is reflective of those different times. Drawing from these illustrative comments and from other source material mentioned, some critical issues facing the field of information systems is identified. Rather than attempting to enforce a niche for information systems, a philosophical reconstruction is carried out using formal pragmatics, as developed by the philosopher Karl-Otto Apel (1980) and the social theorist Jurgen Habermas (1984).

As is often the case real IT operators in large organisations find themselves having to deal with "enterprise" software which has been imposed upon them. The decision to implement such software is usually determined by perceived business requirements (which is reasonable enough), but with little consideration of the operations and flexibility for new, or even assumed, needs.

Rationale

"Immersion" is an innovative digital product that combines multiple established existing technologies and processes to provide a new product that fills gaps in the higher education and gaming market.

An attempted build of Python-2.7.13 with GCC-8.2.0 led to an unexpected error where the build failed to generation of POSIX vars. This is kind of important and unsurprisingly, others on in the Python community have noticed it as well both this year, and in a directly related matter from late 2016, with a recommended patchfile provided on the Python-Dev mailing list.

Identifying probable dispersal routes and for marine populations is a data and processing intensive task of which traditional high performance computing systems are suitable, even for single-threaded applications. Whilst processing dependencies between the datasets exist, a large level of independence between sets allows for use of job arrays to significantly improve processing time. Identification of bottle-necks within the code base suitable for GPU optimisation however had led to additional performance improvements which can be coupled with the existing benefits from job arrays.