Hi, I experience surprisingly large timing differences for the multiplication of matrices of the same dimension. An example is given below. How can this be explained? I posted the question on Stackoverflow: https://stackoverflow.com/questions/58886111/r-why-is-matrix-product-slower-when-matrix-has-very-small-values Somebody could reproduce the behavior but I did not get any useful explanations yet. Many thanks for hints! Florian ## disable openMP library(RhpcBLASctl); blas_set_num_threads(1); omp_set_num_threads(1) A <- exp(-as.matrix(dist(expand.grid(1:60, 1:60)))) summary(c(A)) # Min. 1st Qu. Median Mean 3rd Qu. Max. # 0.000000 0.000000 0.000000 0.001738 0.000000 1.000000 B <- exp(-as.matrix(dist(expand.grid(1:60, 1:60)))*10) summary(c(B)) # Min. 1st Qu. Median Mean 3rd Qu. Max. # 0.0000000 0.0000000 0.0000000 0.0002778 0.0000000 1.0000000 identical(dim(A), dim(B)) ## [1] TRUE system.time(A %*% A) # user system elapsed # 2.387 0.001 2.389 system.time(B %*% B) # user system elapsed # 21.285 0.020 21.310 sessionInfo() # R version 3.6.1 (2019-07-05) # Platform: x86_64-pc-linux-gnu (64-bit) # Running under: Linux Mint 19.2 # Matrix products: default # BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3 # LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
Why is matrix product slower when matrix has very small values?
3 messages · Florian Gerber, Hilmar Berger, Serguei Sokol
Hi Florian, just a guess, but couldn't it be that the multiplication of very small values leads to FP underflow exceptions which have to be handled by BLAS in a less efficient way than "normal" multiplications handled by SIMD instructions ? Best regards, Hilmar
On 19/11/2019 15:09, Florian Gerber wrote:
Hi, I experience surprisingly large timing differences for the multiplication of matrices of the same dimension. An example is given below. How can this be explained? I posted the question on Stackoverflow: https://stackoverflow.com/questions/58886111/r-why-is-matrix-product-slower-when-matrix-has-very-small-values Somebody could reproduce the behavior but I did not get any useful explanations yet. Many thanks for hints! Florian ## disable openMP library(RhpcBLASctl); blas_set_num_threads(1); omp_set_num_threads(1) A <- exp(-as.matrix(dist(expand.grid(1:60, 1:60)))) summary(c(A)) # Min. 1st Qu. Median Mean 3rd Qu. Max. # 0.000000 0.000000 0.000000 0.001738 0.000000 1.000000 B <- exp(-as.matrix(dist(expand.grid(1:60, 1:60)))*10) summary(c(B)) # Min. 1st Qu. Median Mean 3rd Qu. Max. # 0.0000000 0.0000000 0.0000000 0.0002778 0.0000000 1.0000000 identical(dim(A), dim(B)) ## [1] TRUE system.time(A %*% A) # user system elapsed # 2.387 0.001 2.389 system.time(B %*% B) # user system elapsed # 21.285 0.020 21.310 sessionInfo() # R version 3.6.1 (2019-07-05) # Platform: x86_64-pc-linux-gnu (64-bit) # Running under: Linux Mint 19.2 # Matrix products: default # BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3 # LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
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Dr. Hilmar Berger, MD Max Planck Institute for Infection Biology Charit?platz 1 D-10117 Berlin GERMANY Phone: + 49 30 28460 430 Fax: + 49 30 28460 401 E-Mail: berger at mpiib-berlin.mpg.de Web : www.mpiib-berlin.mpg.de
Le 20/11/2019 ? 09:56, Hilmar Berger a ?crit?:
Hi Florian, just a guess, but couldn't it be that the multiplication of very small values leads to FP underflow exceptions which have to be handled by BLAS in a less efficient way than "normal" multiplications handled by SIMD instructions ?
Another guess is that you are caught by what is called "denormal numbers" https://en.wikipedia.org/wiki/Denormal_number. Arithmetic operations on them are different and slower that those on "normal" numbers. Best, Serguei.
Best regards, Hilmar On 19/11/2019 15:09, Florian Gerber wrote:
Hi, I experience surprisingly large timing differences for the multiplication of matrices of the same dimension. An example is given below. How can this be explained? I posted the question on Stackoverflow: https://stackoverflow.com/questions/58886111/r-why-is-matrix-product-slower-when-matrix-has-very-small-values Somebody could reproduce the behavior but I did not get any useful explanations yet. Many thanks for hints! Florian ## disable openMP library(RhpcBLASctl); blas_set_num_threads(1); omp_set_num_threads(1) A <- exp(-as.matrix(dist(expand.grid(1:60, 1:60)))) summary(c(A)) #???? Min.? 1st Qu.?? Median???? Mean? 3rd Qu.???? Max. # 0.000000 0.000000 0.000000 0.001738 0.000000 1.000000 B <- exp(-as.matrix(dist(expand.grid(1:60, 1:60)))*10) summary(c(B)) #????? Min.?? 1st Qu.??? Median????? Mean?? 3rd Qu.????? Max. # 0.0000000 0.0000000 0.0000000 0.0002778 0.0000000 1.0000000 identical(dim(A), dim(B)) ## [1] TRUE system.time(A %*% A) #??? user? system elapsed #?? 2.387?? 0.001?? 2.389 system.time(B %*% B) #??? user? system elapsed #? 21.285?? 0.020? 21.310 sessionInfo() # R version 3.6.1 (2019-07-05) # Platform: x86_64-pc-linux-gnu (64-bit) # Running under: Linux Mint 19.2 # Matrix products: default # BLAS:?? /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3 # LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
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