speed

I bought my first copy of Agile Software Development with Scrum, by Schwarber and Beedle back around 2002, I think. I was just thumbing through it last night when I saw that they use Function Points as a metric to demonstrate the velocity that agile software teams achieve, and more specifically use it to show that some teams develop software much faster using Scrum.

I didn’t know about Function Point Analysis back in 2002 — I didn’t become a Certified Function Point Specialist until about two years later — so I probably just skimmed over that line then, but when I saw it last night I thought it was cool that they used function points as a metric for software team development speed.

I was reading this post by Martin Odersky (Make the Scala runtime independent of the standard library) and came across this comment by Li Haoyi: “This would also make it more feasible to use Scala for tiny bootstrap scripts; current Mill’s launcher is written in Java because the added classloading needed to use scala.Predef (even just println) easily adds a 200-400ms of initialization overhead.” I haven’t written anything where the startup time of a Scala application was a huge problem, but that was interesting to read.

(Though I should say that I wish all Scala/Java command-line apps started faster. It’s one reason I occasionally think about using Haskell for small scripts, so I can compile them to an executable.)

Back in my day, aerospace engineering undergrad students had very little time to work in the wind tunnels at Texas A&M, but in the limited time I had I tried to look at what makes a knuckleball move erratically. Barton Smith at Utah State University did the same thing (presumably with much more wind tunnel time) looking at a baseball’s spin rate, spin axis, and orientation of the ball.

scala-lang.org has an article titled Speeding Up Compilation Time with `scalac-profiling` where they demonstrate how they reduced a project’s compilation time from 32.5 seconds down to 4 seconds. In addition to all of the scalac and profiling details, it demonstrates a nice use of flamegraphs.

I haven’t tried it yet, but as a note to self, Scala 2.12.5 introduced a new -Ybackend-parallelism N compiler flag with which “the backend can now run in parallel on N threads.”