Welcome CS teachers and students!

[Be sure to visit Living Computers in Seattle. SIGCSE 2017 attendees are admitted free during the conference. I visited the museum today and it was a lot of fun! K-12 teachers will enjoy the hands on exhibits.]

The annual ACM Special Interest Group on Computer Science Education (SIGCSE 2017) Technical Symposium is next week (March 8 – 11) in Seattle, Washington. The symposium brings together educators at all levels (K-12 and higher ed) to exchange and discuss the latest methods, practices and results in computer science education.

I don’t often advertise it, but the Sand, Software, Sound site has many resources for educators and students alike. You can browse these resources by clicking on one of the WordPress topic buttons (Raspberry Pi, PERF, Courseware, etc.) above. You can also search for a topic or choose from one of the categories listed in the right sidebar.

Here are a few highlights.

I taught many computer-related subjects during my career and have posted course notes, slides and old projects. The four main sections are:

  • CS2 data structures: Undergraduate data structures course suitable for advanced placement students.
  • Computer design: Undergraduate computer architecture and design which uses a multi-level modeling approach.
  • VLSI systems: Graduate course on VLSI architecture, design and circuits which is suitable for undergraduate seniors.
  • Topics in computer architecture: Material for a special topics seminar about computer architecture (somewhat historical).

Please feel free to dig through these materials and make use of them.

Software and hardware performance analysis formed a major thread throughout my professional life. I recommend reading my series of tutorials on the Linux PERF tool set for software performance analysis:

The ARM11 microarchitecture summary is background material for the PERF tutorial. Program profiling is a good way to bring computer architecture to life and to teach students how to analyze and assess the execution speed of their programs.

There are two additional tutorials and getting started guides for teachers and students working on Raspberry Pi:

Music technology and computer-based music-making have been two of my chief interests over the years. The Arduino section of the site has several of my past projects using the Arduino for music-making. You should also check out my recent blog posts about the littleBits synth modules and littleBits Arduino. Please click on the tags and links at the bottom of each post in order to chase down material.

You might also enjoy my tutorial on software synthesizers for Linux and Raspberry Pi. The tutorial is a getting started guide for musicians of all stripes — music teachers and students are certainly welcome, too!

Topics in computer architecture

A new courseware page — topics in computer architecture — is now available. This page covers a wide mix of topics from RISC vs. CISC to non-von Neumann languages and architectures. You might just find an interesting lecture or two for a special topics seminar about computer architecture.

Way back when in the early eighties, I had a chance to work with Al Davis at the University of Utah. Al was a proponent of data-driven dataflow computers. His ideas showed me that there are alternatives to centralized synchronous control, sequential programming and the von Neumann memory bottleneck. The topics page has links to information about some of this interesting work.

Looking back, I’m struck at how mired we are in the von Neumann architecture! Sure, there are a few projects underway to investigate alternatives like quantum computing, functional programming languages, and so forth. However, the world remains dominated by the centralized, sequential von Neumann model and programming languages in which parallelism is glued on through a (class) library or two. General purpose GPU (GPGPU) computation is not really that radical. Yes, GPGPU computation is data parallel, but the underlying model is the sequential execution of SIMD instructions. Speculative execution is a bit of a kludge to allow conditional execution in highly sequential SIMD programs. Further, data transfer from memory to execution units remains a limiting factor in performance.

I think we need to introduce students to a wide range of computing models. Hopefully, some young, clever soul will find a way to pull us out of our rut!