Amdahl’s Law

Gene Amdahl once applied the law of diminishing returns to computation. He pointed out that when optimizing part of a computer program or computer system, one must take into account what percent of the overall task at hand that optimization affects.

I recently read some articles comparing the speed of Python to Java, most of which concluded that about the only place that Java beats Python is in actual interpreter speed (i.e. how fast statements and parsed and executed), and that since Python opts to provide thin wrappers for standard C libraries, Python performance ends up being really good.

A good comparison of the language features between Java and Python can be found online, along with a nice comparison of code simplicity and efficiency.

I think I agree with the first author: Python is a better high-level language, and should thus be used for higher-level tasks, and especially for one-offs. What’s interesting is that a lot of people look at Python, say “Man, Python is slow, I could do this better in C,” but then forget about Amdahl’s Law. If your program is accessing the network, the disk, or any other non-CPU/non-memory resource, no amount of optimization through lower-level languages will save you an order of magnitude on performance. So why waste the programmer time, when it can be done in a few lines of Python?

(I think one forgotten benefit of Python in both these articles is SWIG: if you’re truly a performance-oriented engineer, you can always profile your code, find the bottleneck algortihm/code fragment, rewrite it in C, and wrap it with SWIG so that you can access it as an object in Python. Not hard to do, and potentially huge performance gains. OTOH, you can even write Python-accessible code directly in C, using the same abstractions the Python interpreter uses.)