Save bits, save power, save the world, Part 1

Ok, the title was a little tongue-in-cheek. I did a fairly whimsical experiment yesterday. Since text strings are represented as bits which are either 1 (on) or 0 (off), I was wondering which unused domain names used the minimum number of "on" bits. The answer may be non-intuitive to the non-computer scientist.

Scala Performance

I just found this experiment pitting several Python-based servers versus a Scala one. After "priming the pump" to get the VM to optimize the Scala one, Scala handled 3.6x more requests per second than the Python Twisted web server. It even handled 8.5% more requests than a largely C-infused Python server Fast Asyncronous Python WSGI Server. No memory use information was given, but these results do definitely tilt towards Scala.

Interestingly, at least according to the Computer Language Shootout (which admittedly has some issues), Scala significantly outperforms the listed JVM-hosted languages such as JRuby and Clojure (in execution time). JRuby has a steady lead in terms of code size.

Static Type System and Numerical Computing

The programming languages with which most people are familiar, such C/C++, Java, Python, JavaScript, and Ruby, presents a very stark contrast. On the one hand, you have statically typed languages (C/C++ and Java) which are amenable to a wide assortment of compile-time optimizations due to the presence of types. On the other, you have dynamically typed languages (Python, JavaScript, and Ruby) which are "fun" to program and amenable to rapid prototype due to more succinct syntax partly due to the absence of explicit type annotations. The sweet spot, in my humble opinion, is statically type but with type inference, a subject of my expertise and doctoral studies. I plan to write a few posts on this subject, near and dear to my heart. Heavy duty Numerical computing is one area where dynamically typed languages such as Matlab, R, and NumPy/Python have gained a considerable mindshare. Since NumPy offloads computations to efficient C libraries, this is quite reasonable. I would like to discuss where I think numerical computing and statistical analysis programs can benefit from a more sophisticated static type system with type inference. To whet your appetite, consider the case that C++'s Boost Library contains the functionality needed to statically check for units (i.e., dimensional analysis). The performance difference between code generated from statically typed languages and from dynamically typed languages is not just the absence of dynamic type checks. Types in static type systems enable a considerable array of optimizations to get rid of that last tiny ounce of latency.

Programming Language Gotchas

Looks like I spoke too soon. The October rally was looking a little long in tooth, and a little warning from the credit agencies was enough to get the market to go risk off at least for this afternoon. At least yields did not decline too much (2.01% for the 10-year according to MarketWatch). The Dollar Index (DX), however, has certainly been in an uptrend since the end of October trough.

Coming from the Standard ML world of languages makes me take many things for granted. It also gives me a probably pretty narrow perspective. Hacking on my trading analysis tools in other languages helps me broaden that somewhat, though I have certainly worked in the mainstream languages long before I encountered ML and its brethren. I would like to keep a record of these gotchas as I encounter them. I do not intend this as value judgments but rather a documentation of noteworthy differences from a higher-order typed programming language perspective.