In the previous post I looked at running some MCMC codes in C and Scala on the Parallella. In that post I explained how the Parallella was significantly faster than the Raspberry Pi, and how it represented better “bang for buck” than the Raspberry Pi for computationally intensive MCMC codes. However, since that post was written, the Raspberry Pi 2 has been released. This board has a much better processor than the old Pi, and double the RAM, for the same price. This changes things, considerably. The processor is an ARM7 quad core. Each core is around twice as fast as the single core on the original Pi, and there are 4 of them. In this post I will re-run the codes from the previous post and compare against the Parallella.
Gibbs sampler in C
I’m using the new Raspbian image for the Pi 2. This includes gcc by default, but not the GSL library. This can be installed with sudo apt-get install libgsl0-dev. Then the file gibbs.c from the previous post will compile and run. On the Pi 2 this runs in around 75 seconds – very similar to the time on the Parallella, and around twice as fast as all of the previous Raspberry Pis.
Gibbs sampler in Scala
The Raspbian image ships with Oracle’s fast and efficient ARM-optimised JVM by default, so there’s no issue with installing Java at all. As usual, installing “sbt” is a simple matter of copying the launcher script (and jar) into your ~/bin directory. Then the Scala version of the Gibbs sampler can be run with a command like time sbt run > /dev/null. Again, it runs in around 4 minutes 40 seconds, just like on the Parallella. So, the ARM cores on the Parallella and the Pi 2 have very similar performance. However, the Parallella ARM chip has just two cores, whereas the Pi 2 is quad core.
Parallel Monte Carlo in Scala
Again, as for the previous post, I next ran the Monte Carlo example from this github repo. This gives output like:
$ sbt run [info] Set current project to monte-carlo (in build file:/home/pi/src/git/statslang-scala/monte-carlo/) [info] Running MonteCarlo Running with 1000000 iterations Idiomatic vectorised solution 1.00136 time: 6768.504487ms Fast efficient (serial) tail call 1.000705 time: 2473.331672ms Parallelised version 1.0007 time: 1391.2828ms Done [success]
Here again we see that the single threaded versions run in a similar time to the Parallella (and around twice as fast as the old Pis), but that the parallelised version runs significantly faster on the Pi 2 than on the Parallella (due to having 4 ARM cores rather than 2).
For my test MCMC codes, the cores on the Pi 2 are around twice as fast as the single core on the old Raspberry Pis, and a similar speed to the cores on the Parallella. However, multi-threaded codes run faster still, due to there being 4 cores on the Pi 2 (versus 2 on the Parallella and one on the old Pis). Furthermore, the Pi 2 is the same price as the old Pis (which are still being sold), and around a quarter of the price of the cheapest Parallella. So for standard single and multi-threaded codes running on the ARM cores, the Pi 2 wins hands down in terms of “bang for buck”, and is sufficiently quick and cheap that it starts looking like a credible platform for building cheap clusters for compute-intensive jobs like MCMC. Now to be fair to the Parallella, really the whole point of it is that it has a multi-core Epiphany co-processor that I’ve not been using or factoring in to the discussion at all so far. That said, the Pi 2 is so much cheaper than the Parallella (not to mention, less “fragile”), that I suspect that even for codes which effectively exploit the Epiphany chip it is unlikely that the Parallella will outperform the Pi 2 in terms of “bang for buck”. Now “bang per watt” is another matter entirely, and the Parallella may well outperform the Pi 2 in that regard if efficient use can be made of the Epiphany chip. But development time costs money too, and it’s really not clear that it’s going to be easy for me to run my multi-threaded Scala codes effectively on the Epiphany chip any time soon. So the Pi 2 currently looks like a real winner from my personal perspective.