Slowing down matrix multiplication in R

After I realized that some aspects of R’s implementation are rather inefficient, one of the first things I looked at was matrix multiplication.  There I found a huge performance penalty for many matrix multiplies, a penalty which remains in the current version, 2.13.0.  As discussed below, eliminating this penalty speeds up long vector dot products by a factor of 9.5 (on my new machine), and other operations where the result matrix has at least one small dimension are sped up by factors that are somewhat smaller, but still substantial. There’s a long story behind why R’s matrix multiplies are so slow…

Speed tests for R — and a look at the compiler

I’ve gotten back to work on speeding up R, starting with improving my suite of speed tests.  Among other new features, this suite allows one to easily try out the “byte-code” compiler that is now a standard part of the latest release of R, version 2.13.0. You can get the suite here.

I’ve been running these tests on my new workstation, which has a six-core Intel X5680 processor, running at 3.33GHz.  Unfortunately, it’s clear that thing runs somewhat slower when you use all the cores at once, so for consistency one needs to do the speed tests using just one core.  (Or one needs some more elaborate, and unclear, protocol for testing the speed of R in a muticore environment.)  I haven’t figured out how to get Red Hat Linux to compile 32-bit applications yet, so all the tests are in a 64-bit environment.

I’ve started with comparing the speed of R-2.13.0 with and without functions being compiled, and with comparing R-2.13.0 (without the compiler) to R-2.11.1, which was the last release before some of my speed improvements were incorporated.  A plot of the results is here.