> What I would like is a CPU with a highly parallel array of "worker cores" all addressing the same memory...
I too am very interested in this model. The Linux kernel supports up to 4,096 cores [1] on a single machine. In practice, you can rent a c7a.metal-48xl [2] instance on AWS EC2 with 192 vCPU cores. As for programming models, I personally find the Java Streams API [3] extremely versatile for many programming workloads. It effectively gives a linear speedup on serial streams for free (with some caveats). If you need something more sophisticated, you can look into OpenMP [4], an API for shared-memory parallelization.
I agree it is time for some new ideas in this space.
Wouldn't every Turing complete cellular automaton have this property? What would be an example of a nontrivial (i.e., sufficiently expressive) CA that is "predictable"?
I also experienced an irregular heartbeat for several months after receiving the Moderna vaccine (June, 2021) up to this month (December, 2021). It seems to be getting better, but is accompanied by a mild pain, tightness in the chest and shallow breath. This is despite regular diet, exercise and otherwise normal health. I am 31 years old.
There are many bizarre visualizations that are solely cosmetic and serve no purpose other than to attract the eyes. I've noticed the same pattern with many other videos churned out by popular science channels. The visual editing is delirious and the narration is devoid of much intellectual substance. Hard to pinpoint exactly what's wrong with it, it's just extremely bland and borderline uncanny valley.
The author frames this as a contest between two architectures: either spend a lot of effort building custom developer tools, or repurpose the compiler as a server for multiple clients. Both approaches have their tradeoffs: the first, as the author mentions, violates DRY [1]. The second violates the Unix philosophy [2].
However, there is a third way. Both developer tools and compilers can be seen as special cases of a much simpler and more general pattern known as a graph database [3, 4, 5].
And when your surpassing creations find the answers you asked for, you can’t understand their analysis and you can’t verify their answers. You have to take their word on faith—Or you use information theory to flatten it for you, to squash the tesseract into two dimensions and the Klein bottle into three, to simplify reality and pray to whatever Gods survived the millennium that your honorable twisting of the truth hasn’t ruptured any of its load-bearing pylons. You hire people like me; the crossbred progeny of profilers and proof assistants and information theorists…
Space filling curves have some super important applications for indexing and information retrieval. I recently stumbled on an fascinating library called Uzaygezen for multidimensional Hilbert space filling curves. Extremely high quality code and documentation:
Anyone with a software engineering background interested probabilistic model checking and learning automata should check out the work from ICSAS led by Prof. Lijun Zhang, Yong Li and Andrea Turrini. In particular their work on ePMC and ROLL are really excellent:
Storm is another probabilistic model checker from RWTH Aachen, developed Christian Hensel, Sebastian Junges, Tim Quatmann, Matthias Volk et al. at , which has a nice Python API:
> The fact that there is no actual methodology published for how this index of popularity is actually generated makes me highly sceptical of the whole idea.
I recently learned there are a few groups at ETZH working on traffic simulation and fleet planning in transportation networks. Are you affiliated with the Autonomous Mobility on Demand / IDSC folks?
> Computers will never fully automate mathematical reasoning, because mathematical reasoning cannot be fully automated.
One possible reason why mathematics cannot be automated is because some important piece of contemporary mathematics is fundamentally unsound, e.g. ∞, LEM, AOC. Mathematicians are very clever at building and manipulating formal systems, but are prone to mistakes and must accept on faith some foundations to make any progress. If you spent your entire career building a castle and someone like Gödel or Brouwer came along and claimed the whole thing is built on sand, naturally you (and all your venerable castle-building colleagues) would be unwilling to simply accept this development and move on.
I suspect what many people call mathematics today lost its course somewhere after Cantor, who was a supremely clever human being, but either mentally unstable or driven to madness trying to operationalize infinite sets. If our universe were infinite, maybe his ideas would be valid, but even then we could never build machines to verify those claims. While it has produced an abundance of useful ideas, it also generates a number of paradoxes (e.g. Zeno, Banach-Tarski), which are simply incompatible with the universe in which we live.
As a grad student who has been using Kotlin in research for the last few years, it can also be a great language for data science. Kotlin is one of the few statically typed languages with scripting and Jupyter notebook support, and offers some great features for parallel and asynchronous data processing. Once you set up a project and import the right dependencies, the workflow really clicks. It works smoothly with the JVM and JS ecosystems, which have a bunch of fantastic libraries for data processing and visualization. Happy to answer any questions about using Kotlin for DS/ML if anyone’s curious.
Definitely the product of an over-excited grad student who’s fallen in love with these ideas for the first time. Thanks for reading, I hope you were able to get something from it (if not what to avoid)!
Yes, I was originally inspired by GraalVM’s work! They were one of the first to realize a practical implementation of Futamura’s ideas. I briefly discuss it towards the end:
Sure, I made plenty of bad predictions too! I have written about some of the harmful effects of trust and bias in automation, feel free to check it out:
I too am very interested in this model. The Linux kernel supports up to 4,096 cores [1] on a single machine. In practice, you can rent a c7a.metal-48xl [2] instance on AWS EC2 with 192 vCPU cores. As for programming models, I personally find the Java Streams API [3] extremely versatile for many programming workloads. It effectively gives a linear speedup on serial streams for free (with some caveats). If you need something more sophisticated, you can look into OpenMP [4], an API for shared-memory parallelization.
I agree it is time for some new ideas in this space.
[1]: https://www.phoronix.com/news/Perf-Support-2048-To-4096-Core...
[2]: https://aws.amazon.com/ec2/instance-types/c7a/
[3]: https://docs.oracle.com/en/java/javase/24/docs/api/java.base...
[4]: https://docs.alliancecan.ca/wiki/OpenMP