kmscube Running on Orange Pi PC with Mainline Kernel

Christof Meerwald from blog

Managed to get kmscube running on my Orange Pi PC with a mainline Linux 5.3 kernel and an updated mesa package from Ubuntu's ubuntu-x-swat PPA. The amazing thing is that it's all just mainline now, no board-specific patches needed. Interestingly, a Raspberry Pi 3 still needs a kernel built from its own branch to get that level of hardware support.

On a slightly related note I have been looking at what level of support I get for my ODROID-C1 now. Unfortunately, there is still no HDMI output and no USB OTG support. Interestingly, HDMI output does work on NetBSD.

Cost ratio for bespoke hardware+software

Derek Jones from The Shape of Code

What percentage of the budget for a bespoke hardware/software system is spent on software, compared to hardware?

The plot below has become synonymous with this question (without the red line, which highlights 1973), and is often used to claim that software costs are many times more than hardware costs.

USAF bespoke hardware/Software cost ratio from 1955 to 1980.

The paper containing this plot was published in 1973 (the original source is a Rome period report), and is an extrapolation of data I assume was available in 1973, into what was then the future. The software and hardware costs are for bespoke command and control systems delivered to the U.S. Air Force, not commercial off-the-shelf solutions or even bespoke commercial systems.

Does bespoke software cost many times more than the hardware it runs on?

I don’t have any data that might be used to answer this questions, to any worthwhile degree of accuracy. I know of situations where I believe the bespoke software did cost a lot more than the hardware, and I know of some where the hardware cost more (I have never been privy to exact numbers on large projects).

Where did the pre-1973 data come from?

The USAF funded the creation of lots of source code, and the reports cite hardware and software figures from 1972.

To summarise: the above plot is for USAF spending on bespoke command and control hardware and software, and is extrapolated from 1973 into the future.

A Little Bit Slinky – a.k.

a.k. from thus spake a.k.

For several months we've for been taking a look at cluster analysis which seeks to partition sets of data into subsets of similar data, known as clusters. Most recently we have focused our attention on hierarchical clusterings, which are sequences of sets of clusters in which pairs of data that belong to the same cluster at one step belong to the same cluster in the next step.
A simple way of constructing them is to initially place each datum in its own cluster and then iteratively merge the closest pairs of clusters in each clustering to produce the next one in the sequence, stopping when all of the data belong to a single cluster. We have considered three ways of measuring the distance between pairs of clusters, the average distance between their members, the distance between their closest members and the distance between their farthest members, known as average linkage, single linkage and complete linkage respectively, and implemented a reasonably efficient algorithm for generating hierarchical clusterings defined with them, using a min-heap structure to cache the distances between clusters.
Finally, I claimed that there is a more efficient algorithm for generating single linkage hierarchical clusterings that would make the sorting of clusters by size in our ak.clustering type too expensive and so last time we implemented the ak.rawClustering type to represent clusterings without sorting their clusters which we shall now use in the implementation of that algorithm.