Hong Ooi — written Apr 18, 2018 — source
Besides outstanding support for dense matrices, the Armadillo library also provides a great way to manipulate sparse matrices in C++. However, the performance characteristics of dealing with sparse matrices may be surprising if one is only familiar with dense matrices. This is a collection of observations on getting best performance with sparse matrices in Armadillo.
All the timings in this article were generated using Armadillo version 8.500. This version adds a number of substantial optimisations for sparse matrix operations, in some cases speeding things up by as much as two orders of magnitude.
Perhaps the most important thing to note is that the efficiency of sparse algorithms can depend strongly on the level of sparsity in the data. If your matrices and vectors are very sparse (most elements equal to zero), you will often see better performance even if the nominal sizes of those matrices remain the same. This isn’t specific to C++ or Armadillo; it applies to sparse algorithms in general, including the code used in the Matrix package for R. By contrast, algorithms for working with dense matrices usually aren’t affected by sparsity.
Similarly, the pattern of accesssing elements, whether by rows or by columns, can have a significant impact on performance. This is due to caching, which modern CPUs use to speed up memory access: accessing elements that are already in the cache is much faster than retrieving them from main memory. If one iterates or loops over the elements of a matrix in Armadillo, one should try to iterate over columns first, then rows, to maximise the benefits of caching. This applies to both dense and sparse matrices. (Technically, at least for dense matrices, whether to iterate over rows or columns first depends on how the data is stored in memory. Both R and Armadillo store matrices in column-major order, meaning that elements in the same column are contiguous in memory. Sparse matrices are more complex but the advice to iterate by columns is basically the same; see below.)
We start with a simple concrete example: multiplying two matrices together. In R, this can be done
%*% operator which (via the Matrix package) is able to handle any combination of sparse
and dense inputs. However, let us assume we want to do the multiplication in Armadillo: for example
if the inputs are from other C++ functions, or if we want more precise control of the output.
In Armadillo, the
* operator multiplies two matrices together, and this also works for any
combination of sparse and dense inputs. However, the speed of the operation can vary tremendously,
depending on which of those inputs is sparse. To see this, let us define a few simple functions:
The outputs of these functions are all the same, but they take different types of inputs: either two sparse matrices, or a sparse and a dense matrix, or a dense and a sparse matrix (the order matters). Let us call them on some randomly generated data:
user system elapsed 0.230 0.091 0.322
user system elapsed 0.407 0.036 0.442
user system elapsed 1.081 0.100 1.181
user system elapsed 0.826 0.087 0.913
While all the times are within an order of magnitude of each other, multiplying a dense and a sparse matrix takes about twice as long as multiplying two sparse matrices together, and multiplying a sparse and dense matrix takes about three times as long. (The sparse-dense multiply is actually one area where Armadillo 8.500 makes massive gains over previous versions. This operation used to take much longer due to using an unoptimised multiplication algorithm.)
Let us see if we can help the performance of the mixed-type functions by creating a temporary sparse copy of the dense input. This forces Armadillo to use the sparse-sparse version of matrix multiply, which as seen above is much more efficient. For example, here is the result of tweaking the dense-sparse multiply. Creating the sparse copy does take some extra time and memory, but not enough to affect the result.
user system elapsed 0.401 0.047 0.448
This shows that mixing sparse and dense inputs can have significant effects on the efficiency of your code. To avoid unexpected slowdowns, consider sticking to either sparse or dense classes for all your objects. If one decides to mix them, it is worth remembering to test and profile the code.
Consider another simple computation: multiply the elements of a matrix by their row number, then sum them up. (The multiply by row number is to make it not completely trivial.) That is, we want to obtain:
Armadillo lets us extract individual rows and columns from a matrix, using the
member functions. We can use row extraction to do this computation:
user system elapsed 1.708 0.000 1.707
For a large matrix, this takes a not-insignificant amount of time, even on a fast machine. To speed it up, we will make use of the fact that Armadillo uses the compressed sparse column (CSC) format for storing sparse matrices. This means that the data for a matrix is stored as three vectors: the nonzero elements; the row indices of these elements (ordered by column); and a vector of offsets for the first row index in each column. Since the vector of row indices is ordered by column, and we have the starting offsets for each column, it turns out that extracting a column slice is actually very fast. We only need to find the offset for that column, and then pull out the elements and row indices up to the next column offset. On the other hand, extracting a row is much more work; we have to search through the indices to find those matching the desired row.
We can put this knowledge to use on our problem. Row slicing a matrix is the same as transposing it and then slicing by columns, so let us define a new function to return a column slice. (Transposing the matrix takes only a tiny fraction of a second.)
user system elapsed 0.766 0.000 0.766
The time taken has come down by quite a substantial margin. This reflects the ease of obtaining column slices for sparse matrices.
We can take the previous example further. Each time R calls a C++ function that takes a sparse matrix as input, it makes a copy of the data. Similarly, when the C++ function returns, its sparse outputs are copied into R objects. When the function itself is very simple—as it is here—all this copying and memory shuffling can be a significant proportion of the time taken.
Rather than calling
sapply in R to iterate over rows, let us do the same entirely in C++:
user system elapsed 0.933 0.000 0.935
This is again a large improvement. But what if we do the same with column slicing?
user system elapsed 0.005 0.000 0.006
Now the time is less than a tenth of a second, which is faster than the original code by roughly
three orders of magnitude. The moral to the story is: rather than constantly switching between C++
and R, you should try to stay in one environment for as long as possible. If your code involves a
loop with a C++ call inside (including functional maps like
lapply and friends), consider writing
the loop entirely in C++ and combine the results into a single object to return to R.
(It should be noted that this interface tax is less onerous for built-in Rcpp classes such as
NumericMatrix, which do not require making copies of the data. Sparse data
types are different, and in particular Armadillo’s sparse classes do not provide constructors that
can directly use auxiliary memory.)
Rather than taking explicit slices of the data, let us try using good old-fashioned loops over the matrix elements. This is easily coded up in Armadillo, and it turns out to be quite efficient, relatively speaking. It is not as fast as using column slicing, but much better than row slicing.
user system elapsed 0.176 0.000 0.176
However, we can still do better. In Armadillo, the iterators for sparse matrix classes iterate only over the nonzero elements. Let us compare this to our naive double loop:
user system elapsed 0.002 0.000 0.002
This is the best time achieved so far, to the extent that
system.time might have difficulty
capturing it. The timings are now so low that we should use the microbenchmark package to get
Unit: milliseconds expr min lq mean median uq max neval col 4.78921 4.88444 5.05229 4.99184 5.18450 5.50579 20 elem 172.84830 177.20431 179.87007 179.06447 182.08075 188.11256 20 iter 1.02268 1.05447 1.12611 1.12627 1.16482 1.30800 20
Thus, using iterators represents a greater than three-order-of-magnitude speedup over the original row-slicing code.Tweet