What goes on in a computer in a millisecond?

In a 2001 Q&A session (around 1h30m) Knuth is asked about unsolved challenges in the field of compute science, à la Hilbert's problems. Knuth's responds by referring to a prompt he posed to the World Computer Congress in 1989, in a keynote titled "Theory and Practice, IV" (Arxiv has a version, though this version appears to have the slides as well):

"Make a thorough analysis of everything your computer does during one second of computation"

Does the prompt require adjustment for context, some thirty years later? Knuth noted the practical challenges associated with monitoring and interpreting the computer's activities, whose difficulty would appear to remain on the same order of magnitude today. On the other hand, Knuth associated 1 second with approximately 250,000 instructions. Given that CPU clock speeds have jumped from standard units in MHz to GHz (wikipedia notes clock speeds in 1989 were in the 16-35 MHz range, in line with Knuth's approximation), maybe today's equivalent of the question is:

"What goes on in a computer in a millisecond"?

In the same keynote, Knuth provides a set of guiding questions, including the correctness of the observed activities and their correspondence to theory -- that is, whether they reflect existing theory, and whether new theory would yield practical improvements.

Despite the daunting scope of the problem, the fundamental idea of mapping theory to some atomic observations of computing practice seems widely applicable and useful for students and practitioners alike.

Compilers, Project Euler 100

It was sad to see Stanford retire Lagunita (apparently due to a decision to retire their Open edX implementation). Many of their classes appear to be slated for addition to edX, including the Compilers class -- at least according to an email sent to Lagunita participants. I seem to recall the content originally being available on Coursera. It certainly has the look, feel, and substances of the original batch of very high quality courses (like Tim Roughgarden's Algorithms courses, Andrew Ng's AI course, Dan Boneh's Cryptography Part I... we're still waiting on Part II after many years, apparently because he is trying to finish his book). There seems to be a great deal more of content on the MOOCs now, but also maybe some more noise.

In any case, I spent most of my study time in March speeding through the Compilers videos and finishing the first programming assignment on lexing (yacc / flex) to make sure all the mechanics work. Fortunately, there are offline graders that can be run in the class VM, so it will be possible to complete the other programming assignments at a more leisurely pace. Given that there isn't currently an active forum for unaffiliated students like me to ask questions, GitHub has been in an invaluable resource to reference other students' implementations and try to triangulate certain issues.

Speaking of using reference materials... seven years after starting Project Euler and learning about the Sieve of Eratosthenes, I've finally completed more than 100 problems. About 65 problems have been solved in Haskell over the past year (the balance at some point during prior years, in Python).

I had no qualms referencing the internet for many problems, since it's unlikely I would have independently stumbled upon many of the math theorems required to solve some problems efficiently (like the Sierpinski triangle and Lucas' Theorum behind one of the problems on Pascal's triangle). In that regard, I'm glad of folks who have ignored Proect Euler's rules of the road and posted their research online. At least for autodidactic purposes, it seems like more information is (almost?) always better. How to use and internalize the information, how to differentiate research from shortcuts, how to set boundaries of information taken vs internally synthesized -- those are all the responsibility of the student, with decisions ultimately depending on one's goals. My (recent) goals have been in rough descending order to learn functional programming paradigms, to learn Haskell, to enjoy problem solving, and last + least to learn some underlying math. As far as the first two go, continuing with more problems probably offers diminishing returns (especially compared to pursuing orthogonal problem spaces), but I'll likely continue with it on the slow path for the enjoyment.