code – Observations from Uppsala

(Local) AI, Please Write some Code

My previous blog post in this series tested the ability of a range of large language models to analyze a piece of C code and determine what a mystery function did. That was interesting and entertaining, but possibly not a particularly “fair” test of the models’ capabilities. Most of time, I think people use “AI” to help write code, not to understand some tricky piece of algorithmic code. Thus, I turn the problem around and ask the models to write code for the algorithm I previously asked them to analyze.

(Local) AI, Please Explain This Code

Continuing my exploration of what a local AI model can do, I decided to test them on the task of code analysis. It would be so nice to have an AI model that is tuned and trained on a particular tool or programming system, and that can be distributed for users to run on their own on their local machine, server, or cloud VM. Avoiding the need to run and charge for a custom cloud service and ensuring confidentiality and availability.

Updated 2024-12-12 with Llama-3.3-70B

Blog – Damn the Torpedoes, Full Code Ahead!

I have recently got back to developing training labs for the Simics simulator (and related technologies). During the process of developing a new accelerator model using as many of the latest frameworks and APIs as possible, it was basically guaranteed that I would hit some bugs and unexpected behaviors. That is a natural part of and benefit from creating training materials in the first place. It also provides a good illustration of two fundamentally different ways to look at software development. One is to play it safe and get things done in known ways, and the other is charge ahead, try the unknown, and see what happens. Damn the torpedoes, bugs are a benefit. No bug reports, no glory. In this post, I will share some recent examples of just coding ahead and breaking thing.

FLOSS Weekly: Drizzle: Aggressive Push to Multicore

I listened to episode 35 of FLOSS Weekly that interviewed Brian Aker, creator of the Drizzle fork from MySQL. As most recent episodes of FLOSS Weekly, it is pretty good technical material. What I found interesting was the technical vision behind Drizzle, and how they are aggressively going for quite wide multicore hosts.

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In Praise of Scripting: Something for Modeling as Well?

In the July 2008 issue of IEEE Computer, there is short article called “In Praise of Scripting: Real Programming Pragmatism“, by Ronald P. Loui, a professor at Washington University (WUSTL). The article deals with the issue of what is the appropriate first language to teach new CS (Computer Science) students, and considers that a “scripting” langauge like Python or Ruby might be way better than Java (no doubt about that I think).

The interesting material in the article is the background on WHY he thinks that this is the case. He points to the immense popularity and rise of scripting in much of computing land. In the past ten years, it is clear to him (and I would agree with this too mostly) that languages like Perl, PHP, Awk, Ruby, JavaScript, and Python have eclipse Java and C++ as the most interesting and important programming languages for many practical tasks. Especially for web applications, where Java seems to have a presence but noone would dream of using something as clunky and impractical as C.

What can this teach us for the purpose of simulation and the creation of models of computer system hardware for the purpose of simulation? Maybe a fair bit…

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