Some thirteen years ago I worked with Xen virtual machines as part of my day job, and gave a presentation at Linux Users of Victoria on the subject (with additional lecture notes). A few years after that I gave another presentation on the Unified Extensible Firmware Interface (UEFI), itself which (indirectly) led to a post on Linux and MS-Windows 8 dual-booting. All of this now leads to a some notes on using MS-Windows as a host for Ubuntu Linux guest machines.
Almost two years ago I made a short blog post about how the Net Promoter Score (NPS), commonly used in business settings, is The Most Useless Metric of All. My reasons at the time is that it doesn't capture the reasons for a low score, it doesn't differentiate between subjective values in its scores, and it is mathematically incoherent (a three-value grade from an 11-point range of 0-10).
The translation of arithmetic to physical hardware with using the IEEE standard employed numerical representation is fraught with difficulty. As is well known by any who have used even a pocket calculator, computer processors are imprecise with dangerous rounding errors, which vary on different systems. Further, the standard representation method, IEEE 754 "Standard for Floating-Point Arithmetic" (1985, revised 2008), is extremely inefficient from an engineering perspective with increasing physical cost when additional precision is sought.
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 alogorithm, 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.
Following examples of a successful IS implementation, the Bankers Trust of Australia, and an on-going failure with the example of the UK's Universal Credit project, it is opportune to consider a subset of IS that is common to both projects and determine whether there is a general rule that can be applied.
When reviewing practical implementations of information systems (IS), incredible failures provide very valuable lessons even if they are ongoing. At an estimated £12.8bn, far in excess of the originally estimated £2.2bn (Ballard, 2013), the UK's Universal Credit project will be the single-most expensive failed or overbudget custom software projects, although when adjusted for inflation the UK's NHS Connecting for Health project (mostly abandoned in 2011), also cost around £12bn. Apparently if one wishes to study exceptional failures in IS, government in general, the UK in particular, and the subcategory of health and welfare is a good place to start. Whilst this may seem slightly snide, it is backed by empirical evidence. Only the US's FAA Advanced Automation System (c$4.5b USD, 1994) is really within a similar sort of scale of failure.
Universal Credit, like many such projects, on a prima facie level, seems to designed on reasonable principles. Announced in 2010, with the objective to simplify working-age welfare benefits and encourage taking up paid work, it would replace and combine six different means-tested enefits, and roll them into a single monthly payment and which, as paid work was taken up, would be gradually be reduced, rather than having an all-or-nothing approach, following the "negative income tax" proposals, as proposed by Juliet Rhys William and Milton Friedman (Forget, 2012). The project was meant to start in 2013 and completed by 2017. Under current plans (there have been at least seven timetable completion changes), this has been pushed out to 2023 (Butler, Walker, 2016).