Performance benefits of linking R to multithreaded math libraries

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R wasn’t originally designed as a multithreaded application — multiprocessor systems were still rare when the R Project was first conceived in the mid-90’s — and so, by default, R will only use one processor of your dual-core laptop or quad-core desktop machine when doing calculations. For calculations that take a long time, like big simulations or modeling of large data sets, it would be nice to put those other processors to use to speed up the computations. There are several parallel processing libraries for R available that allow you to explicitly run loops in R simultaneously (ideally, each on a different processor), but using them does require you to rewrite your code accordingly. 

But there is a way to make use of all your processing power for many computations in R, without changing a line of code. That’s because R is a statistical computing system, and at the heart of many of the algorithms you use on a daily basis — data restructuring, regressions, classifications, even some graphics functions — is linear algebra. The data are transformed into vector and matrix objects, and the internals of R have been cleverly designed to link to a standard “BLAS” API to perform calculations on vectors and matrices. The binaries provided by the R Core Group on CRAN (with one exception, see below) are linked to an “internal BLAS which is well-tested and will be adequate for most uses of R”, but is not multi-threaded and so only uses one core. But the beauty of linking to the BLAS API is that you can re-compile R to link to a different, multi-threaded BLAS library and, voilà, suddenly many computations are using all cores and therefore run much faster.

The MacOS port of R on CRAN is linked to ATLAS, a “tuned” BLAS that uses multiple cores for computations. As a result, R on a multi-core Mac (as all new Macs are these days) really zooms. But the Windows binaries on CRAN are not linked to an optimized BLAS. It’s possible to compile and link R yourself, but it can be tricky.

That’s what we do at Revolution for our Windows and Linux distributions of Revolution R. When we compile R, we link it to the Intel Math Kernel Libraries, which includes a high-performance BLAS implementation tuned to multi-core Intel chips. “Tuning” here means using efficient algorithms, optimized assembly code that exploits features of the chipset, and multi-threaded algorithms that use all cores simultaneously. As a result, you get some serious speed boosts for many operations in R, especially on a multi-core system. Here are some examples:

CalculationSizeCommandR 2.9.2Revolution R

(1 core)
Revolution R

(4 cores)

Matrix Multiply

10000×5000B <- crossprod(A)243 sec22 sec5.9 sec

Cholesky Factorization5000×5000C <- chol(B)23 sec3.8 sec1.1 sec

Singular Value Decomposition5000×5000S <- svd (A,nu=0,nv=0)62 sec13 sec4.9 sec

Principal Components Analysis10000×5000P <- prcomp(A)237 sec41 sec15.6 sec

As you can see, using the Intel MKL libraries on a 4-core machine gives some dramatic speedups (about a quarter of the 1-core time, as you might expect). Perhaps more surprisingly, using the Intel MKL libraries on a 1-core machine is also faster than using R’s standard BLAS library: this is a result of the optimized algorithms, not additional computing power. You even get improvements on non-Intel chipsets (like AMD).

[A side note: These calculations were actually all run on an 8-core machine, specifically, an Intel Xeon 8-core CPU with 18 GB system RAM running Windows Server 2008 operating system. The complete benchmark code is available on this page. The results for Revolution R 1-core and 4-core were calculated by restricting the Intel MKL library to use 1 thread and 4 threads, using the Revolution-R-specific commands setMKLthreads(1) and setMKLthreads(4) respectively. This has the effect of using only the power of the specific number of cores, even when more cores are available. Note: if you’re using Revolution R and are doing explicit parallel programming with doSMP, it’s a good idea to call setMKLthreads(1) first. Otherwise, your parallel loops and the multi-threaded linear algebra computations will compete for the same processor and actually degrade performance.]

These results are dramatic, but multi-threaded BLAS libraries aren’t a panacea. Not all R commands ultimately link to BLAS code, even ones you might expect. (For example, lm for regression uses a non-BLAS QR decomposition by default. Edit: as pointed out by Doug Bates, lm and glm end up calling the older Linpack routines using level-1 (vector-vector) BLAS instead of the newer, level-3-based (matrix-matrix) Lapack routines because of the need to handle certain rank-deficient cases cleanly.) And if your R code ultimately does not involve linear algebra, you can’t expect any improvement at all. (For example, the “Program Control” R benchmarks by Simon Urbanek show only marginal performance gains in Revolution R.) This is when explicit parallel programming is the route to improved performance. We’re also working on dedicated statistical routines for Revolution R Enterprise that are explicitly multi-threaded for single-machines and also distributable to multiple machines in a cluster or in the cloud, but that’s a topic for another post.

Revolution Analytics: Performance Benchmarks

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