indexByRow - HenrikBengtsson/matrixStats GitHub Wiki

matrixStats: Benchmark report

indexByRow() benchmarks

This report benchmark the performance of indexByRow() against alternative methods:

  • indexByRow_R1() based in matrix(..., byrow = TRUE)
  • indexByRow_R2() is a modified version of indexByRow_R1()

where indexByRow_R1() and indexByRow_R2() are defined as in the Appendix.

Data

> data <- rmatrices(mode = "index")

where rmatrices() is defined in the Appendix.

Results

10x10 matrix

> X <- data[["10x10"]]
> dim <- dim(X)
> idxsList <- list(`all-by-NULL` = NULL, all = seq_len(prod(dim)), odd = seq(from = 1, to = prod(dim), 
+     by = 2L))
> str(idxsList)
List of 3
 $ all-by-NULL: NULL
 $ all        : int [1:100] 1 2 3 4 5 6 7 8 9 10 ...
 $ odd        : num [1:50] 1 3 5 7 9 11 13 15 17 19 ...

Index set 'all-by-NULL'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x10+all-by-NULL data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 indexByRow 0.000709 0.0010065 0.0012170 0.0011370 0.0012295 0.007657
3 indexByRow_R2 0.003279 0.0040355 0.0042458 0.0042135 0.0043475 0.008286
2 indexByRow_R1 0.003485 0.0041495 0.0045250 0.0042590 0.0044180 0.029376
expr min lq mean median uq max
1 indexByRow 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 indexByRow_R2 4.624824 4.009439 3.488792 3.705805 3.535990 1.082147
2 indexByRow_R1 4.915374 4.122702 3.718196 3.745822 3.593331 3.836489

Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x10+all-by-NULL data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'all'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x10+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 indexByRow 0.001452 0.0017125 0.0018830 0.0018285 0.0018880 0.007985
2 indexByRow_R1 0.003937 0.0044535 0.0048252 0.0046515 0.0049195 0.018117
3 indexByRow_R2 0.004492 0.0051310 0.0054952 0.0052820 0.0054460 0.024536
expr min lq mean median uq max
1 indexByRow 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 indexByRow_R1 2.711432 2.600584 2.562457 2.543888 2.605667 2.268879
3 indexByRow_R2 3.093664 2.996204 2.918276 2.888707 2.884534 3.072761

Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x10+all data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'odd'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x10+odd data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 indexByRow 0.001163 0.0014535 0.0016794 0.0015955 0.0017375 0.008264
3 indexByRow_R2 0.003142 0.0038215 0.0040446 0.0039855 0.0041055 0.011395
2 indexByRow_R1 0.004041 0.0044965 0.0049489 0.0047125 0.0048525 0.024988
expr min lq mean median uq max
1 indexByRow 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 indexByRow_R2 2.701634 2.629171 2.408373 2.497963 2.362878 1.378872
2 indexByRow_R1 3.474635 3.093567 2.946891 2.953620 2.792806 3.023717

Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x10+odd data. Outliers are displayed as crosses. Times are in milliseconds.

100x100 matrix

> X <- data[["100x100"]]
> dim <- dim(X)
> idxsList <- list(`all-by-NULL` = NULL, all = seq_len(prod(dim)), odd = seq(from = 1, to = prod(dim), 
+     by = 2L))
> str(idxsList)
List of 3
 $ all-by-NULL: NULL
 $ all        : int [1:10000] 1 2 3 4 5 6 7 8 9 10 ...
 $ odd        : num [1:5000] 1 3 5 7 9 11 13 15 17 19 ...

Index set 'all-by-NULL'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x100+all-by-NULL data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 indexByRow 0.010584 0.0110635 0.0116042 0.0112615 0.0113900 0.027467
3 indexByRow_R2 0.047659 0.0488760 0.0501652 0.0496225 0.0502945 0.066203
2 indexByRow_R1 0.047369 0.0489060 0.0500027 0.0498425 0.0504045 0.056429
expr min lq mean median uq max
1 indexByRow 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 indexByRow_R2 4.502929 4.417770 4.323043 4.406385 4.415672 2.410274
2 indexByRow_R1 4.475529 4.420482 4.309035 4.425920 4.425329 2.054429

Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x100+all-by-NULL data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'all'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x100+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 indexByRow_R1 0.061580 0.062828 0.0645130 0.0632865 0.0647365 0.105998
1 indexByRow 0.071489 0.071980 0.0729375 0.0722700 0.0725520 0.119306
3 indexByRow_R2 0.234603 0.235628 0.2374760 0.2365255 0.2379765 0.254684
expr min lq mean median uq max
2 indexByRow_R1 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 indexByRow 1.160913 1.145668 1.130586 1.141950 1.120728 1.125549
3 indexByRow_R2 3.809727 3.750366 3.681054 3.737377 3.676079 2.402725

Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x100+all data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'odd'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x100+odd data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 indexByRow 0.042664 0.043018 0.0440147 0.0433035 0.0442365 0.063995
2 indexByRow_R1 0.064634 0.065214 0.0665457 0.0660045 0.0667575 0.091848
3 indexByRow_R2 0.115175 0.115985 0.1187264 0.1175920 0.1185565 0.156242
expr min lq mean median uq max
1 indexByRow 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 indexByRow_R1 1.514954 1.515970 1.511895 1.524230 1.509105 1.435237
3 indexByRow_R2 2.699583 2.696197 2.697424 2.715531 2.680061 2.441472

Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x100+odd data. Outliers are displayed as crosses. Times are in milliseconds.

1000x10 matrix

> X <- data[["1000x10"]]
> dim <- dim(X)
> idxsList <- list(`all-by-NULL` = NULL, all = seq_len(prod(dim)), odd = seq(from = 1, to = prod(dim), 
+     by = 2L))
> str(idxsList)
List of 3
 $ all-by-NULL: NULL
 $ all        : int [1:10000] 1 2 3 4 5 6 7 8 9 10 ...
 $ odd        : num [1:5000] 1 3 5 7 9 11 13 15 17 19 ...

Index set 'all-by-NULL'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x10+all-by-NULL data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 indexByRow 0.009846 0.0102395 0.0105808 0.0104415 0.0105890 0.018708
3 indexByRow_R2 0.047214 0.0484210 0.0501052 0.0491350 0.0499515 0.089051
2 indexByRow_R1 0.047224 0.0484810 0.0497814 0.0491665 0.0499275 0.064060
expr min lq mean median uq max
1 indexByRow 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 indexByRow_R2 4.795247 4.728844 4.735507 4.705742 4.717301 4.760049
2 indexByRow_R1 4.796262 4.734704 4.704903 4.708758 4.715034 3.424203

Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x10+all-by-NULL data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'all'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x10+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 indexByRow_R1 0.061297 0.0621330 0.0633183 0.0627155 0.0637360 0.076457
1 indexByRow 0.071533 0.0720700 0.0726449 0.0722510 0.0725785 0.094779
3 indexByRow_R2 0.234260 0.2354485 0.2375439 0.2362900 0.2386405 0.257215
expr min lq mean median uq max
2 indexByRow_R1 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 indexByRow 1.166990 1.159931 1.147297 1.152044 1.138736 1.239638
3 indexByRow_R2 3.821721 3.789427 3.751585 3.767649 3.744203 3.364179

Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x10+all data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'odd'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x10+odd data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 indexByRow 0.043567 0.0440405 0.0444613 0.0442165 0.0444835 0.050173
2 indexByRow_R1 0.065186 0.0659265 0.0670409 0.0663105 0.0670705 0.089506
3 indexByRow_R2 0.117182 0.1182080 0.1191417 0.1185680 0.1192195 0.129929
expr min lq mean median uq max
1 indexByRow 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 indexByRow_R1 1.496224 1.496952 1.507849 1.499678 1.507761 1.783948
3 indexByRow_R2 2.689696 2.684075 2.679674 2.681533 2.680084 2.589620

Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x10+odd data. Outliers are displayed as crosses. Times are in milliseconds.

10x1000 matrix

> X <- data[["10x1000"]]
> dim <- dim(X)
> idxsList <- list(`all-by-NULL` = NULL, all = seq_len(prod(dim)), odd = seq(from = 1, to = prod(dim), 
+     by = 2L))
> str(idxsList)
List of 3
 $ all-by-NULL: NULL
 $ all        : int [1:10000] 1 2 3 4 5 6 7 8 9 10 ...
 $ odd        : num [1:5000] 1 3 5 7 9 11 13 15 17 19 ...

Index set 'all-by-NULL'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x1000+all-by-NULL data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 indexByRow 0.009583 0.0103150 0.0108005 0.0105130 0.0107850 0.018775
3 indexByRow_R2 0.045863 0.0475715 0.0483974 0.0482065 0.0488985 0.056372
2 indexByRow_R1 0.046520 0.0475940 0.0489322 0.0482775 0.0496545 0.066893
expr min lq mean median uq max
1 indexByRow 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
3 indexByRow_R2 4.785871 4.611876 4.481030 4.585418 4.533936 3.002503
2 indexByRow_R1 4.854430 4.614057 4.530548 4.592172 4.604033 3.562876

Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x1000+all-by-NULL data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'all'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x1000+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 indexByRow_R1 0.060350 0.0613125 0.0625899 0.0618195 0.0629455 0.075802
1 indexByRow 0.071480 0.0719345 0.0723251 0.0721445 0.0724540 0.078394
3 indexByRow_R2 0.234345 0.2356565 0.2372685 0.2362250 0.2374815 0.261667
expr min lq mean median uq max
2 indexByRow_R1 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
1 indexByRow 1.184424 1.173244 1.155539 1.167018 1.151059 1.034194
3 indexByRow_R2 3.883099 3.843531 3.790842 3.821205 3.772811 3.451980

Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x1000+all data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'odd'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x1000+odd data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 indexByRow 0.043739 0.0440865 0.0446986 0.0442500 0.0445840 0.058771
2 indexByRow_R1 0.064322 0.0653020 0.0666407 0.0656860 0.0665260 0.087385
3 indexByRow_R2 0.117426 0.1183710 0.1192118 0.1187655 0.1192815 0.128526
expr min lq mean median uq max
1 indexByRow 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 indexByRow_R1 1.470587 1.481224 1.490892 1.484429 1.492150 1.486873
3 indexByRow_R2 2.684698 2.684972 2.667016 2.683966 2.675433 2.186895

Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 10x1000+odd data. Outliers are displayed as crosses. Times are in milliseconds.

100x1000 matrix

> X <- data[["100x1000"]]
> dim <- dim(X)
> idxsList <- list(`all-by-NULL` = NULL, all = seq_len(prod(dim)), odd = seq(from = 1, to = prod(dim), 
+     by = 2L))
> str(idxsList)
List of 3
 $ all-by-NULL: NULL
 $ all        : int [1:100000] 1 2 3 4 5 6 7 8 9 10 ...
 $ odd        : num [1:50000] 1 3 5 7 9 11 13 15 17 19 ...

Index set 'all-by-NULL'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x1000+all-by-NULL data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 indexByRow 0.091331 0.1919825 0.2819083 0.1982920 0.2027125 10.455428
3 indexByRow_R2 0.450214 0.5536825 0.6677908 0.7247140 0.7388300 0.838227
2 indexByRow_R1 0.442711 0.5391645 0.6701900 0.7270285 0.7429530 0.838459
expr min lq mean median uq max
1 indexByRow 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
3 indexByRow_R2 4.929476 2.884026 2.368822 3.654782 3.644719 0.0801715
2 indexByRow_R1 4.847325 2.808404 2.377333 3.666454 3.665058 0.0801937

Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x1000+all-by-NULL data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'all'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x1000+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 indexByRow_R1 0.557787 0.5839215 0.6610249 0.5886285 0.6039455 6.304085
1 indexByRow 0.691175 0.7073965 0.7255854 0.7106190 0.7264970 0.867763
3 indexByRow_R2 2.297614 2.3664470 2.5600072 2.3759905 2.4476710 8.512826
expr min lq mean median uq max
2 indexByRow_R1 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
1 indexByRow 1.239138 1.211458 1.097667 1.207245 1.202918 0.1376509
3 indexByRow_R2 4.119160 4.052680 3.872785 4.036486 4.052801 1.3503666

Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x1000+all data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'odd'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x1000+odd data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 indexByRow 0.423800 0.4359650 0.4523315 0.447579 0.4616150 0.538598
2 indexByRow_R1 0.619296 0.6462575 0.7250991 0.660268 0.6745105 6.653620
3 indexByRow_R2 1.158735 1.1997540 1.2934724 1.230587 1.2622150 6.706267
expr min lq mean median uq max
1 indexByRow 1.000000 1.000000 1.000000 1.000000 1.000000 1.00000
2 indexByRow_R1 1.461293 1.482361 1.603026 1.475199 1.461197 12.35359
3 indexByRow_R2 2.734155 2.751950 2.859567 2.749430 2.734346 12.45134

Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 100x1000+odd data. Outliers are displayed as crosses. Times are in milliseconds.

1000x100 matrix

> X <- data[["1000x100"]]
> dim <- dim(X)
> idxsList <- list(`all-by-NULL` = NULL, all = seq_len(prod(dim)), odd = seq(from = 1, to = prod(dim), 
+     by = 2L))
> str(idxsList)
List of 3
 $ all-by-NULL: NULL
 $ all        : int [1:100000] 1 2 3 4 5 6 7 8 9 10 ...
 $ odd        : num [1:50000] 1 3 5 7 9 11 13 15 17 19 ...

Index set 'all-by-NULL'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x100+all-by-NULL data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 indexByRow 0.092339 0.1024225 0.1498238 0.117675 0.2091405 0.240272
2 indexByRow_R1 0.449351 0.4937740 0.7193290 0.559641 0.7779595 11.355862
3 indexByRow_R2 0.448114 0.4922105 0.6318389 0.586922 0.7845540 0.827782
expr min lq mean median uq max
1 indexByRow 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 indexByRow_R1 4.866319 4.820952 4.801167 4.755819 3.719794 47.262527
3 indexByRow_R2 4.852922 4.805687 4.217214 4.987652 3.751325 3.445187

Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x100+all-by-NULL data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'all'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x100+all data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
2 indexByRow_R1 0.597619 0.6173045 0.7003441 0.6327730 0.652469 6.435247
1 indexByRow 0.706675 0.7189410 0.7483689 0.7444395 0.755921 0.901611
3 indexByRow_R2 2.336351 2.4240690 2.6136927 2.4901240 2.536624 8.559633
expr min lq mean median uq max
2 indexByRow_R1 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
1 indexByRow 1.182484 1.164646 1.068573 1.176472 1.158555 0.1401051
3 indexByRow_R2 3.909432 3.926861 3.732012 3.935256 3.887730 1.3301172

Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x100+all data. Outliers are displayed as crosses. Times are in milliseconds.

Index set 'odd'

> stats <- microbenchmark(indexByRow = indexByRow(dim, idxs = idxs), indexByRow_R1 = indexByRow_R1(dim, 
+     idxs = idxs), indexByRow_R2 = indexByRow_R2(dim, idxs = idxs), unit = "ms")

Table: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x100+odd data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 indexByRow 0.415424 0.4355645 0.5153917 0.457482 0.4709800 6.292663
2 indexByRow_R1 0.630225 0.6697285 0.6923113 0.686587 0.7110825 0.801584
3 indexByRow_R2 1.161292 1.2035270 1.3017618 1.241457 1.2711150 7.175120
expr min lq mean median uq max
1 indexByRow 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 indexByRow_R1 1.517065 1.537610 1.343272 1.500796 1.509793 0.1273839
3 indexByRow_R2 2.795438 2.763143 2.525772 2.713674 2.698873 1.1402359

Figure: Benchmarking of indexByRow(), indexByRow_R1() and indexByRow_R2() on 1000x100+odd data. Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

R version 3.6.1 Patched (2019-08-27 r77078)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS

Matrix products: default
BLAS:   /home/hb/software/R-devel/R-3-6-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-3-6-branch/lib/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] microbenchmark_1.4-6    matrixStats_0.55.0-9000 ggplot2_3.2.1          
[4] knitr_1.24              R.devices_2.16.0        R.utils_2.9.0          
[7] R.oo_1.22.0             R.methodsS3_1.7.1       history_0.0.0-9002     

loaded via a namespace (and not attached):
 [1] Biobase_2.45.0       bit64_0.9-7          splines_3.6.1       
 [4] network_1.15         assertthat_0.2.1     highr_0.8           
 [7] stats4_3.6.1         blob_1.2.0           robustbase_0.93-5   
[10] pillar_1.4.2         RSQLite_2.1.2        backports_1.1.4     
[13] lattice_0.20-38      glue_1.3.1           digest_0.6.20       
[16] colorspace_1.4-1     sandwich_2.5-1       Matrix_1.2-17       
[19] XML_3.98-1.20        lpSolve_5.6.13.3     pkgconfig_2.0.2     
[22] genefilter_1.66.0    purrr_0.3.2          ergm_3.10.4         
[25] xtable_1.8-4         mvtnorm_1.0-11       scales_1.0.0        
[28] tibble_2.1.3         annotate_1.62.0      IRanges_2.18.2      
[31] TH.data_1.0-10       withr_2.1.2          BiocGenerics_0.30.0 
[34] lazyeval_0.2.2       mime_0.7             survival_2.44-1.1   
[37] magrittr_1.5         crayon_1.3.4         statnet.common_4.3.0
[40] memoise_1.1.0        laeken_0.5.0         R.cache_0.13.0      
[43] MASS_7.3-51.4        R.rsp_0.43.1         tools_3.6.1         
[46] multcomp_1.4-10      S4Vectors_0.22.1     trust_0.1-7         
[49] munsell_0.5.0        AnnotationDbi_1.46.1 compiler_3.6.1      
[52] rlang_0.4.0          grid_3.6.1           RCurl_1.95-4.12     
[55] cwhmisc_6.6          rappdirs_0.3.1       labeling_0.3        
[58] bitops_1.0-6         base64enc_0.1-3      boot_1.3-23         
[61] gtable_0.3.0         codetools_0.2-16     DBI_1.0.0           
[64] markdown_1.1         R6_2.4.0             zoo_1.8-6           
[67] dplyr_0.8.3          bit_1.1-14           zeallot_0.1.0       
[70] parallel_3.6.1       Rcpp_1.0.2           vctrs_0.2.0         
[73] DEoptimR_1.0-8       tidyselect_0.2.5     xfun_0.9            
[76] coda_0.19-3

Total processing time was 17.71 secs.

Reproducibility

To reproduce this report, do:

html <- matrixStats:::benchmark('indexByRow')

Copyright Henrik Bengtsson. Last updated on 2019-09-10 20:58:15 (-0700 UTC). Powered by RSP.

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Local functions

> indexByRow_R1 <- function(dim, idxs = NULL, ...) {
+     n <- prod(dim)
+     x <- matrix(seq_len(n), nrow = dim[2L], ncol = dim[1L], byrow = TRUE)
+     if (!is.null(idxs)) 
+         x <- x[idxs]
+     as.vector(x)
+ }
> indexByRow_R2 <- function(dim, idxs = NULL, ...) {
+     n <- prod(dim)
+     if (is.null(idxs)) {
+         x <- matrix(seq_len(n), nrow = dim[2L], ncol = dim[1L], byrow = TRUE)
+         as.vector(x)
+     }     else {
+         idxs <- idxs - 1
+         cols <- idxs%/%dim[2L]
+         rows <- idxs%%dim[2L]
+         cols + dim[1L] * rows + 1L
+     }
+ }
> rmatrix <- function(nrow, ncol, mode = c("logical", "double", "integer", "index"), range = c(-100, 
+     +100), na_prob = 0) {
+     mode <- match.arg(mode)
+     n <- nrow * ncol
+     if (mode == "logical") {
+         x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+     }     else if (mode == "index") {
+         x <- seq_len(n)
+         mode <- "integer"
+     }     else {
+         x <- runif(n, min = range[1], max = range[2])
+     }
+     storage.mode(x) <- mode
+     if (na_prob > 0) 
+         x[sample(n, size = na_prob * n)] <- NA
+     dim(x) <- c(nrow, ncol)
+     x
+ }
> rmatrices <- function(scale = 10, seed = 1, ...) {
+     set.seed(seed)
+     data <- list()
+     data[[1]] <- rmatrix(nrow = scale * 1, ncol = scale * 1, ...)
+     data[[2]] <- rmatrix(nrow = scale * 10, ncol = scale * 10, ...)
+     data[[3]] <- rmatrix(nrow = scale * 100, ncol = scale * 1, ...)
+     data[[4]] <- t(data[[3]])
+     data[[5]] <- rmatrix(nrow = scale * 10, ncol = scale * 100, ...)
+     data[[6]] <- t(data[[5]])
+     names(data) <- sapply(data, FUN = function(x) paste(dim(x), collapse = "x"))
+     data
+ }
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