Updated R & BLAS Timings
This article is originally published at https://www.avrahamadler.com
With the recent releases of R 3.2.4 and OpenBLAS 2.17, I decided it was time to re-benchmark R speed. I’ve settled on a particular set of tests, based on my experience as well as some of Simon Urbanek’s work which I separated into two groups: those focusing on BLAS-heavy operations and those which do not. I’ve posted the code I use to its own page, but I’ll copy it below for convenience:
set.seed(4987)
library(microbenchmark)
library(Matrix)
A <- matrix(rnorm(1e6, 1e3, 1e2), ncol = 1e3)
B <- matrix(rnorm(1e6, 1e3, 1e2), ncol = 1e3)
A <- crossprod(A, A)
A <- A * 1000 / mean(A)
colnames(A) <- colnames(B) <- NULL
options(scipen=4, digits = 3)
BLAS <- microbenchmark(
sort(c(as.vector(A), as.vector(B))),
det(A),
A %*% B,
t(A) %*% B,
crossprod(A, B),
solve(A),
solve(A, t(B)),
solve(B),
chol(A),
chol(B, pivot = TRUE),
qr(A, LAPACK = TRUE),
svd(A),
eigen(A, symmetric = TRUE),
eigen(A, symmetric = FALSE),
eigen(B, symmetric = FALSE),
lu(A),
fft(A),
Hilbert(3000),
toeplitz(A[1:500, 1]),
princomp(A),
times=25L, unit='ms', control = list(order = 'block')
)
NotBLAS <- microbenchmark(
A + 2,
A - 2,
A * 2,
A / 2,
A * 0.5,
A ^ 2,
sqrt(A[1:10000]),
sin(A[1:10000]),
A + B,
A - B,
A * B,
A / B,
A[1:1e5] %% B[1:1e5],
A[1:1e5] %/% B[1:1e5],
times = 5000L, unit='ms', control = list(order = 'block')
)My machine is a i7-2600K overclocked to 4.65Ghz with 16GB RAM, running Win7 64. I’ve also used RStudio Version 0.99.893 for each of these tests.
I ran the tests on four versions of R:
- Plain vanilla R version 3.2.4 Revised (2016-03-16 r70336) installed from the binary on CRAN
- The same version of R as #1, but with Rblas.dll replaced by one based on OpenBLAS 2.17 compiled specifically for the SandyBridge CPU (as part of the #3)
- R compiled from source using the gcc 4.9.3 toolchain, passing
-march=nativeto EOPTS and linking to a multi-threaded (4 max) SandyBridge-specific OpenBLAS 2.17 - Same as #3, but activating link-time optimization for core R and the BLAS
This time I was a bit wiser, and saved the results to RDS objects so that I could combine them into one R session and compare them. I’ll post the raw output at the bottom of the page, but I think it’s more intuitive to look at graphical comparisons. The takeaway is if you can install a fast BLAS! The other optimization options had minor effects, sometimes better sometimes worse, but using a fast BLAS made a significant difference. Adding a Platform variable to the four sets of BLAS and NotBLAS outputs and then combining them in one session allowed for the generation of comparison plots. The call for these graphs, for those following along at home, is:
ggplot(BLAS) + geom_boxplot(aes(y = time / 1e6, color = Platform, x = expr)) + scale_y_continuous(labels = comma) + ylab('milliseconds') + coord_flip()
ggplot(BLAS) + geom_jitter(aes(y = time / 1e6, color = Platform, x = expr), alpha = 0.3) + scale_y_continuous(labels = comma) + ylab('milliseconds') + coord_flip()
ggplot(NotBLAS) + geom_boxplot(aes(y = time / 1e6, color = Platform, x = expr)) + scale_y_continuous(labels = comma) + ylab('milliseconds') + coord_flip() You may also have to right-click and sellect “View Image” to get a better picture. First is a boxplot comparison for the BLAS related functions:
As there are only 25 samples for each function, a jitter plot may be a bit simpler:
It is clear that just adding the BLAS makes a noticeable difference for matrix-related functionality. The other optimizations tend to provide some benefit, but nothing significant.
The non-BLAS related functions, understandably, don’t respond the same way to the BLAS. As I ran 5000 samples per expression, a jitter plot would not be that clear, even with a low alpha.
What is seen here, in my opinion, that using the local architecture sometimes improves performance, such as for division, and other retards it slightly, such as for sin and sqrt. I think the latter two depend on the optimization as well. Default R calls for -O3 for C code. The magnitude of the improvements seems to be greater than the change in those times where there is an impediment, at least when eyeballing the plot. If I recall from previous tests, strangely, it may be slightly more efficient to pass -mtune = native, at least for my test suite. I’m not sure why, as arch implies tune. I’d have to recompile R yet again to test it, though. Also, with 5000 iterations per expression, garbage collection has to be run on occasion, which accounts for those outlier points.
In summation, if you deal with heavy calculation in R, specifically matrix-related, you will notice significant speed improvement by replacing the vanilla Rblas.dll with a faster one. Compiling or tuning for a specific architecture may show some small increases, but does not return the same results.
While it’s actually easier now to compile OpenBLAS for R and in R on Windows, my instructions are a bit dated, and so I’ll have to update those eventually. I have considered hosting some pre-compiled Rblas files, as Dr. Ei-ji Nakama does, but I’ve held back for two reasons 1) I have to ensure I apply the proper licenses and 2) I’m a bit concerned about someone suing or the like if for whatever reason, a blas didn’t work properly. I have disclaimers and all, but you never know 8-).
In any event, I’m always interested in hearing comments about your experience with compiling R or a fast BLAS for Windows, and especially if you found any of my previous posts helpful. Thanks!
Raw Benchmark Output
R version 3.2.4 Revised (2016-03-16 r70336) (pure vanilla)
Unit: milliseconds
BLAS
expr Min LQ Median UQ Max Mean SD CV n
(fctr) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (int)
1 sort(c(as.vector(A), as.vector(B))) 264.56 266.56 267.13 270.50 327.81 270.39 12.15 0.04492 25
2 det(A) 134.83 135.77 136.47 137.74 206.93 139.83 14.12 0.10095 25
3 A %*% B 465.64 473.39 492.22 504.79 546.33 493.55 22.62 0.04583 25
4 t(A) %*% B 468.96 501.26 506.00 535.00 564.67 514.08 24.71 0.04807 25
5 crossprod(A, B) 737.52 746.47 759.38 768.71 813.29 762.19 18.66 0.02449 25
6 solve(A) 607.80 615.18 621.48 639.45 679.81 628.14 18.80 0.02992 25
7 solve(A, t(B)) 847.49 853.39 855.29 859.28 881.07 858.57 8.89 0.01036 25
8 solve(B) 617.06 621.25 622.45 624.65 683.30 625.33 12.56 0.02008 25
9 chol(A) 116.61 117.19 117.40 118.45 123.19 118.46 1.99 0.01676 25
10 chol(B, pivot = TRUE) 2.38 2.45 2.52 2.59 6.91 3.15 1.51 0.48091 25
11 qr(A, LAPACK = TRUE) 423.32 424.46 425.25 426.35 432.92 425.62 2.07 0.00487 25
12 svd(A) 2219.86 2242.51 2265.10 2294.88 2372.62 2277.14 45.47 0.01997 25
13 eigen(A, symmetric = TRUE) 939.63 946.36 952.16 963.85 1020.42 959.00 19.78 0.02063 25
14 eigen(A, symmetric = FALSE) 3623.95 3650.20 3657.46 3675.48 3740.09 3662.55 28.50 0.00778 25
15 eigen(B, symmetric = FALSE) 4072.00 4127.98 4175.12 4228.25 4344.13 4183.00 77.04 0.01842 25
16 lu(A) 137.73 142.06 144.51 147.26 210.54 156.60 26.47 0.16901 25
17 fft(A) 112.27 116.98 119.89 123.29 135.01 120.63 5.20 0.04308 25
18 Hilbert(3000) 134.83 196.37 197.50 199.45 386.30 201.86 46.23 0.22905 25
19 toeplitz(A[1:500, 1]) 4.92 5.06 5.09 5.45 10.49 5.80 1.72 0.29759 25
20 princomp(A) 1834.20 1855.31 1903.78 1947.02 2127.45 1923.42 81.44 0.04234 25
NotBLAS
expr Min LQ Median UQ Max Mean SD CV n
(fctr) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (int)
1 A + 2 1.719 1.891 1.922 2.012 74.96 2.542 2.5880 1.018 5000
2 A - 2 1.723 1.910 1.965 2.066 72.86 2.628 2.6389 1.004 5000
3 A * 2 1.725 1.900 1.948 2.049 80.89 2.604 2.6526 1.019 5000
4 A/2 3.014 3.162 3.179 3.205 74.44 3.776 2.5372 0.672 5000
5 A * 0.5 1.718 1.884 1.908 1.971 74.80 2.538 2.5780 1.016 5000
6 A^2 1.718 1.896 1.939 2.036 73.41 2.587 2.6111 1.009 5000
7 sqrt(A[1:10000]) 0.110 0.111 0.111 0.112 5.30 0.116 0.0833 0.717 5000
8 sin(A[1:10000]) 0.364 0.365 0.365 0.366 1.35 0.368 0.0429 0.117 5000
9 A + B 1.306 1.384 1.448 1.542 68.83 1.582 1.8367 1.161 5000
10 A - B 1.308 1.355 1.432 1.539 67.87 1.571 1.8412 1.172 5000
11 A * B 1.310 1.410 1.471 1.571 69.73 1.610 1.8548 1.152 5000
12 A/B 4.767 4.779 4.789 4.853 66.93 4.946 1.7605 0.356 5000
13 A[1:100000]%%B[1:100000] 2.528 2.544 2.558 2.583 65.80 2.639 1.2633 0.479 5000
14 A[1:100000]%/%B[1:100000] 2.271 2.296 2.318 2.370 65.33 2.423 1.2646 0.522 5000
----------
R version 3.2.4 Revised (2016-03-16 r70336) (pure vanilla)
With SandyBridge-specific OpenBLAS (2.17)
Unit: milliseconds
BLAS
expr Min LQ Median UQ Max Mean SD CV n
(fctr) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (int)
1 sort(c(as.vector(A), as.vector(B))) 264.10 265.06 265.77 266.93 323.4 268.39 11.52 0.0429 25
2 det(A) 14.31 14.57 15.26 16.67 20.0 15.82 1.43 0.0903 25
3 A %*% B 19.25 21.34 26.83 28.49 84.1 27.78 12.68 0.4563 25
4 t(A) %*% B 26.46 30.04 32.20 38.76 43.6 34.25 5.37 0.1567 25
5 crossprod(A, B) 17.63 21.14 22.82 27.48 35.5 24.68 5.10 0.2066 25
6 solve(A) 40.67 45.92 49.58 51.48 107.1 51.29 12.37 0.2412 25
7 solve(A, t(B)) 46.32 50.88 51.63 56.40 63.2 53.00 4.24 0.0799 25
8 solve(B) 47.09 51.22 51.72 56.44 118.6 56.32 13.71 0.2435 25
9 chol(A) 6.59 6.65 6.77 7.01 10.6 7.38 1.39 0.1890 25
10 chol(B, pivot = TRUE) 2.36 2.47 2.50 2.58 6.8 3.25 1.58 0.4851 25
11 qr(A, LAPACK = TRUE) 70.19 71.59 72.47 74.69 79.1 72.96 2.22 0.0304 25
12 svd(A) 375.74 378.56 389.32 415.33 444.7 399.66 26.19 0.0655 25
13 eigen(A, symmetric = TRUE) 171.72 175.04 176.94 177.97 239.8 179.80 13.20 0.0734 25
14 eigen(A, symmetric = FALSE) 849.09 859.77 872.76 891.57 915.7 876.86 20.89 0.0238 25
15 eigen(B, symmetric = FALSE) 1011.80 1065.85 1070.08 1075.52 1085.1 1065.76 18.14 0.0170 25
16 lu(A) 17.91 18.91 20.78 22.62 82.8 32.68 25.43 0.7782 25
17 fft(A) 110.52 111.17 113.16 113.34 113.8 112.33 1.18 0.0105 25
18 Hilbert(3000) 128.99 194.32 194.70 195.01 377.4 196.80 45.44 0.2309 25
19 toeplitz(A[1:500, 1]) 4.77 4.96 5.03 5.04 10.0 5.58 1.64 0.2937 25
20 princomp(A) 285.96 302.37 316.33 329.08 400.5 322.00 29.13 0.0905 25
NotBLAS
expr Min LQ Median UQ Max Mean SD CV n
(fctr) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (int)
1 A + 2 1.712 1.887 1.904 1.938 72.64 2.514 2.6240 1.044 5000
2 A - 2 1.723 1.893 1.913 1.962 68.52 2.540 2.5268 0.995 5000
3 A * 2 1.721 1.894 1.918 1.974 68.97 2.543 2.5326 0.996 5000
4 A/2 3.016 3.173 3.192 3.229 73.36 3.804 2.5315 0.665 5000
5 A * 0.5 1.724 1.900 1.939 2.049 73.70 2.590 2.6048 1.006 5000
6 A^2 1.722 1.890 1.910 1.974 71.55 2.543 2.5479 1.002 5000
7 sqrt(A[1:10000]) 0.110 0.111 0.111 0.112 5.59 0.116 0.0876 0.753 5000
8 sin(A[1:10000]) 0.364 0.365 0.366 0.366 1.32 0.370 0.0408 0.110 5000
9 A + B 1.310 1.336 1.378 1.447 62.35 1.512 1.7318 1.146 5000
10 A - B 1.310 1.345 1.411 1.491 63.70 1.542 1.7465 1.133 5000
11 A * B 1.312 1.349 1.421 1.501 65.96 1.550 1.7753 1.145 5000
12 A/B 4.766 4.781 4.804 4.959 73.53 4.992 1.8146 0.364 5000
13 A[1:100000]%%B[1:100000] 2.526 2.541 2.550 2.591 63.56 2.638 1.2305 0.467 5000
14 A[1:100000]%/%B[1:100000] 2.272 2.294 2.310 2.361 66.49 2.399 1.2681 0.529 5000
----------
R version 3.2.4 Patched (2016-03-28 r70390) -- "Very Secure Dishes"
Compiled with -march=native
With SandyBridge-specific OpenBLAS (2.17)
BLAS
expr Min LQ Median UQ Max Mean SD CV n
(fctr) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (int)
1 sort(c(as.vector(A), as.vector(B))) 266.79 267.24 268.46 271.46 329.37 272.04 12.36 0.0454 25
2 det(A) 14.24 14.43 15.12 16.94 18.63 15.69 1.43 0.0911 25
3 A %*% B 20.95 24.25 28.79 31.89 34.96 28.26 4.21 0.1490 25
4 t(A) %*% B 26.70 29.53 33.74 41.83 103.68 39.71 18.48 0.4652 25
5 crossprod(A, B) 17.65 19.46 28.94 31.99 36.97 26.53 6.56 0.2474 25
6 solve(A) 41.47 45.97 49.44 52.75 56.85 49.38 4.19 0.0849 25
7 solve(A, t(B)) 46.34 51.00 53.22 55.86 113.34 55.53 12.45 0.2242 25
8 solve(B) 47.90 52.74 54.88 57.05 126.30 58.01 15.05 0.2595 25
9 chol(A) 6.57 6.72 6.93 7.43 12.90 7.74 1.84 0.2377 25
10 chol(B, pivot = TRUE) 2.37 2.47 2.49 2.53 6.26 3.22 1.53 0.4755 25
11 qr(A, LAPACK = TRUE) 70.84 71.54 73.10 74.91 78.28 73.59 2.19 0.0298 25
12 svd(A) 375.93 384.60 399.36 454.94 563.73 421.43 49.29 0.1170 25
13 eigen(A, symmetric = TRUE) 170.29 174.23 177.88 185.33 244.08 182.23 14.50 0.0796 25
14 eigen(A, symmetric = FALSE) 839.89 851.24 861.85 874.42 961.61 870.93 31.10 0.0357 25
15 eigen(B, symmetric = FALSE) 985.55 1045.25 1097.99 1142.28 1200.26 1093.09 56.17 0.0514 25
16 lu(A) 18.25 20.03 21.52 23.14 91.58 34.15 27.10 0.7936 25
17 fft(A) 106.88 110.80 112.50 119.96 132.55 115.51 6.66 0.0576 25
18 Hilbert(3000) 133.10 197.69 201.83 204.57 411.96 205.26 50.69 0.2470 25
19 toeplitz(A[1:500, 1]) 5.04 5.20 5.35 5.49 10.66 5.94 1.65 0.2773 25
20 princomp(A) 326.08 336.14 341.65 349.99 425.50 354.56 31.37 0.0885 25
NotBLAS
expr Min LQ Median UQ Max Mean SD CV n
(fctr) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (int)
1 A + 2 1.731 1.890 1.911 1.956 73.12 2.538 2.6108 1.0286 5000
2 A - 2 1.726 1.884 1.903 1.943 70.36 2.527 2.5691 1.0168 5000
3 A * 2 1.727 1.891 1.910 1.945 70.49 2.531 2.5708 1.0156 5000
4 A/2 2.040 2.193 2.210 2.236 71.35 2.816 2.5552 0.9075 5000
5 A * 0.5 1.726 1.902 1.939 2.016 74.95 2.591 2.6501 1.0227 5000
6 A^2 1.727 1.886 1.907 1.966 71.32 2.541 2.5889 1.0188 5000
7 sqrt(A[1:10000]) 0.509 0.516 0.516 0.517 4.36 0.521 0.0670 0.1285 5000
8 sin(A[1:10000]) 0.715 0.720 0.721 0.722 1.72 0.725 0.0416 0.0573 5000
9 A + B 1.331 1.356 1.378 1.459 65.85 1.522 1.8010 1.1831 5000
10 A - B 1.331 1.357 1.383 1.461 64.68 1.527 1.7970 1.1765 5000
11 A * B 1.332 1.354 1.372 1.431 65.46 1.513 1.8009 1.1905 5000
12 A/B 2.412 2.423 2.432 2.460 68.39 2.580 1.8492 0.7168 5000
13 A[1:100000]%%B[1:100000] 2.917 2.999 3.005 3.027 67.03 3.084 1.2797 0.4150 5000
14 A[1:100000]%/%B[1:100000] 2.683 2.759 2.769 2.816 67.74 2.861 1.2999 0.4543 5000
----------
R version 3.2.4 Patched (2016-03-28 r70390) -- "Very Secure Dishes"
Compiled with -march=native and partial LTO (R core, blas, and lapack only)
With SandyBridge-specific OpenBLAS (2.17)
BLAS
expr Min LQ Median UQ Max Mean SD CV n
(fctr) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (int)
1 sort(c(as.vector(A), as.vector(B))) 263.84 265.83 266.39 268.22 323.52 269.49 11.48 0.0426 25
2 det(A) 16.55 17.15 17.72 19.73 23.69 18.55 1.82 0.0982 25
3 A %*% B 19.55 21.52 25.02 33.33 96.59 29.20 15.17 0.5195 25
4 t(A) %*% B 27.71 33.26 35.59 38.03 45.20 35.47 4.33 0.1220 25
5 crossprod(A, B) 17.81 21.49 25.98 28.99 34.36 25.94 4.49 0.1730 25
6 solve(A) 48.38 51.94 54.40 57.04 112.31 56.72 12.03 0.2122 25
7 solve(A, t(B)) 54.93 56.65 60.59 63.24 72.91 61.05 5.25 0.0859 25
8 solve(B) 55.50 58.85 60.88 62.99 121.31 63.46 12.50 0.1969 25
9 chol(A) 8.57 8.69 8.80 8.98 12.21 9.40 1.28 0.1361 25
10 chol(B, pivot = TRUE) 2.35 2.44 2.51 2.62 6.87 3.18 1.60 0.5035 25
11 qr(A, LAPACK = TRUE) 71.16 72.38 73.57 74.85 83.43 74.41 2.93 0.0393 25
12 svd(A) 376.02 380.71 392.98 437.93 490.97 407.28 34.08 0.0837 25
13 eigen(A, symmetric = TRUE) 173.96 176.57 179.35 181.38 259.73 183.29 16.79 0.0916 25
14 eigen(A, symmetric = FALSE) 852.68 856.26 860.10 864.31 926.66 865.16 16.63 0.0192 25
15 eigen(B, symmetric = FALSE) 987.24 993.14 1001.10 1020.62 1079.71 1010.79 24.32 0.0241 25
16 lu(A) 19.18 20.01 21.76 24.21 26.51 22.11 2.44 0.1103 25
17 fft(A) 106.65 107.09 108.58 110.35 116.92 108.98 2.36 0.0216 25
18 Hilbert(3000) 125.60 191.73 192.18 194.44 314.63 195.15 33.81 0.1732 25
19 toeplitz(A[1:500, 1]) 4.99 5.18 5.21 5.24 10.22 5.79 1.62 0.2801 25
20 princomp(A) 285.97 296.12 300.58 317.75 370.74 315.50 30.97 0.0982 25
NotBLAS
expr Min LQ Median UQ Max Mean SD CV n
(fctr) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (int)
1 A + 2 1.828 1.995 2.022 2.091 74.52 2.615 2.4756 0.9468 5000
2 A - 2 1.807 1.973 1.995 2.064 70.60 2.636 2.5624 0.9721 5000
3 A * 2 1.827 1.991 2.009 2.054 71.57 2.637 2.5618 0.9714 5000
4 A/2 2.147 2.293 2.309 2.335 68.18 2.912 2.4859 0.8537 5000
5 A * 0.5 1.817 1.990 2.007 2.049 68.35 2.628 2.5134 0.9564 5000
6 A^2 1.795 1.964 1.980 2.011 67.96 2.596 2.5082 0.9660 5000
7 sqrt(A[1:10000]) 0.531 0.535 0.536 0.536 2.51 0.540 0.0474 0.0878 5000
8 sin(A[1:10000]) 0.719 0.726 0.726 0.727 1.70 0.728 0.0413 0.0566 5000
9 A + B 1.326 1.355 1.391 1.545 66.25 1.690 1.8104 1.0713 5000
10 A - B 1.321 1.365 1.437 1.602 68.45 1.726 1.8618 1.0788 5000
11 A * B 1.327 1.366 1.439 1.586 63.71 1.719 1.8044 1.0499 5000
12 A/B 2.435 2.463 2.530 2.560 65.87 2.804 1.8057 0.6439 5000
13 A[1:100000]%%B[1:100000] 2.987 3.002 3.007 3.025 63.89 3.092 1.2231 0.3956 5000
14 A[1:100000]%/%B[1:100000] 2.718 2.732 2.736 2.745 63.70 2.809 1.2202 0.4344 5000The post Updated R & BLAS Timings appeared first on Strange Attractors.
Thanks for visiting r-craft.org
This article is originally published at https://www.avrahamadler.com
Please visit source website for post related comments.
