- Version: 0.3-1
- License: BSD 2-Clause
- Project home: https://github.com/wrathematics/float
- Bug reports: https://github.com/wrathematics/float/issues
float is a single precision (aka float) matrix framework for R. Base R has no single precision type. Its "numeric" vectors/matrices are double precision (or possibly integer, but you know what I mean). Floats have half the precision of double precision data, for a pretty obvious performance vs accuracy tradeoff.
A matrix of floats should use about half as much memory as a matrix of doubles, and your favorite matrix routines will generally compute about twice as fast on them as well. However, the results will not be as accurate, and are much more prone to roundoff error/mass cancellation issues. Statisticians have a habit of over-hyping the dangers of roundoff error in this author's opinion. If your data is well-conditioned, then using floats is "probably" fine for many applications.
The package requires the single precision BLAS/LAPACK routines which are not included in the default libRblas and libRlapack shipped from CRAN. If your BLAS/LAPACK libraries do not have what is needed, then they will be built (note that a fortran compiler is required in this case). However, these can take a very long time to compile, and will have much worse performance than optimized libraries. The topic of which BLAS/LAPACK to use and how to use them has been written about many times.
To install the R package, run:
install.packages("float")The development version is maintained on GitHub:
remotes::install_github("wrathematics/float")If you are installing on Windows and wish to get the best performance, then you will need to install from source after editing some files. After installing high-performance BLAS and LAPACK libraries, delete the text $(LAPACK_OBJS) from line in src/Makevars.win beginning with OBJECTS = . You will also need to add the appropriate link line. This will ensure that on building, the package links with your high-performance libraries instead of compiling the reference versions. This is especially important for 32-bit Windows where the internal LAPACK and BLAS libraries are built without compiler optimization because of a compiler bug.
Also, if you are using Windows on big endian hardware (I'm not even sure if this is possible), then you will need to change the 0 in src/windows/endianness.h to a 1. Failure to do so will cause very bizarre things to happen with the NA handlers.
Before we get to the main usage of the package and its methods,
- To cast TO a float (convert an existing numeric vector/matrix), use
as.float()(or its shorthandfl()). - To cast FROM a float, use
as.double()oras.integer()(or their shorthands,dbl()andint()). - To pre-allocate a float vector of 0's (like
integer(5)), usefloat(). - To construct a float32 object (developes only; see the vignette), use
float32().
R has a generic number type "numeric" which encompasses integers and doubles. The function is.numeric() will FALSE for float vectors/matries. Similarly, as.numeric() will return the data cast as double.
The goal of the package is to recreate the matrix algebra facilities of the base package, but with floats. So we do not include higher statistical methods (like lm() and prcomp()).
Is something missing? Please let me know.
| Method | Status |
|---|---|
[ |
done |
c() |
done |
cbind() and rbind() |
done |
diag() |
done |
is.na() |
done |
is.float() |
done |
min() and max() |
done |
na.omit(), na.exclude() |
done |
nrow(), ncol(), dim() |
done |
object.size() |
done |
print() |
done |
rep() |
done |
scale() |
Available for logical center and scale |
str() |
done |
sweep() |
Available for FUN's "+", "-", "*", and "/". Others impossible(?) |
typeof() and storage.mode() |
No storage.mode<- method. |
which.min() and which.max() |
done |
| Method | Status |
|---|---|
+ |
done |
* |
done |
- |
done |
/ |
done |
^ |
done |
> |
done |
>= |
done |
== |
done |
< |
done |
<= |
done |
| Method | Status |
|---|---|
dbl() |
done |
int() |
done |
fl() |
done |
as.vector() and as.matrix() |
done |
| Method | Status |
|---|---|
%*% |
done |
backsolve() and forwardsolve() |
done |
chol(), chol2inv() |
done |
crossprod() and tcrossprod() |
done |
eigen() |
only for symmetric inputs |
isSymmetric() |
done |
La.svd() and svd() |
done |
norm() |
done |
qr(), qr.Q(), qr.R() |
done |
rcond() |
done |
solve() |
done |
t() |
done |
| Method | Status |
|---|---|
abs(), sqrt() |
done |
ceiling(), floor(), trunc(), round() |
done |
exp(), exp1m() |
done |
gamma(), lgamma() |
done |
is.finite(), is.infinite(), is.nan() |
done |
log(), log10(), log2() |
done |
sin(), cos(), tan(), asin(), acos(), atan() |
done |
sinh(), cosh(), tanh(), asinh(), acosh(), atanh() |
done |
| Method | Status |
|---|---|
.Machine_float |
float analogue of .Machine. everything you'd actually want is there |
| Method | Status |
|---|---|
colMeans() |
done |
colSums() |
done |
rowMeans() |
done |
rowSums() |
done |
sum() |
done |
Memory consumption is roughly half when using floats:
library(float)
m = 10000
n = 2500
memuse::howbig(m, n)
## 190.735 MiB
x = matrix(rnorm(m*n), m, n)
object.size(x)
## 200000200 bytes
s = fl(x)
object.size(s)
## 100000784 bytesAnd the runtime performance is (generally) roughly 2x better:
library(rbenchmark)
cols <- cols <- c("test", "replications", "elapsed", "relative")
reps <- 5
benchmark(crossprod(x), crossprod(s), replications=reps, columns=cols)
## test replications elapsed relative
## 2 crossprod(s) 5 3.185 1.000
## 1 crossprod(x) 5 7.163 2.249However, the accuracy is better in the double precision version:
cpx = crossprod(x)
cps = crossprod(s)
all.equal(cpx, dbl(cps))
## [1] "Mean relative difference: 3.478718e-07"For this particular example, the difference is fairly small; but for some operations/data, the difference could be significantly larger due to roundoff error.
Because of the use of S4 for the nice syntax, there is some memory overhead which is noticeable for small vectors/matrices. This cost is amortized quickly for reasonably large vectors/matrices. But storing many very small float vectors/matrices can be surprisingly costly.
For example, consider the cost for a single float vector vs a double precision vector:
object.size(fl(1))
## 632 bytes
object.size(double(1))
## 48 bytesHowever once we get to 147 elements, the storage is identical:
object.size(fl(1:147))
## 1216 bytes
object.size(double(147))
## 1216 bytesAnd for vectors/matrices with many elements, the size of the double precision data is roughly twice that of the float data:
object.size(fl(1:10000))
## 40624 bytes
object.size(double(10000))
## 80040 bytesThe above analysis assumes that your float and double values are conforming to the IEEE-754 standard (which is required to build this package). It specifies that a float requires 4 bytes, and a double requires 8. The size of an int is actually system dependent, but is probably 4 bytes. This means that for most, a float matrix should always be larger than a similarly sized integer matrix, because the overhead for our float matrix is simply larger. However, for objects with many elements, the sizes will be roughly equal:
object.size(fl(1:10000))
## 40624 bytes
object.size(1:10000)
## 40040 bytesIt's (generally) twice as fast and uses half the RAM compared to double precision. For a some data analysis tasks, that's more important than having (roughly) twice as many decimal digits.
Type promotion always defaults to the highest type available. If you want the arithmetic to be carried out in single precision, cast the 1 with fl(1) first.
Yes.
If you can formulate the method in terms of existing functionality from the float package, then you're good. If not, you will likely have to write your own C/C++ code. See the For Developers section of the package vignette.
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