matrixStats.benchmarks
sum2() benchmarks on subsetted computation
This report benchmark the performance of sum2() on subsetted computation.
Data type “integer”
Data
> rvector <- function(n, mode = c("logical", "double", "integer"), range = c(-100, +100), na_prob = 0) {
+ mode <- match.arg(mode)
+ if (mode == "logical") {
+ x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+ } 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
+ x
+ }
> rvectors <- function(scale = 10, seed = 1, ...) {
+ set.seed(seed)
+ data <- list()
+ data[[1]] <- rvector(n = scale * 100, ...)
+ data[[2]] <- rvector(n = scale * 1000, ...)
+ data[[3]] <- rvector(n = scale * 10000, ...)
+ data[[4]] <- rvector(n = scale * 1e+05, ...)
+ data[[5]] <- rvector(n = scale * 1e+06, ...)
+ names(data) <- sprintf("n = %d", sapply(data, FUN = length))
+ data
+ }
> data <- rvectors(mode = mode)
Results
n = 1000 vector
> x <- data[["n = 1000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5339042 285.2 8529671 455.6 8529671 455.6
Vcells 16966105 129.5 35919338 274.1 101881463 777.3
> stats <- microbenchmark(sum2_x_S = sum2(x_S), `sum2(x, idxs)` = sum2(x, idxs = idxs), `sum2(x[idxs])` = sum2(x[idxs]),
+ unit = "ms")
Table: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on integer+n = 1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 0.003888 | 0.003944 | 0.0041001 | 0.0040685 | 0.0041925 | 0.005062 |
| 3 | sum2(x[idxs]) | 0.006465 | 0.006634 | 0.0090127 | 0.0067995 | 0.0069405 | 0.211990 |
| 2 | sum2(x, idxs) | 0.006585 | 0.006819 | 0.0071780 | 0.0069955 | 0.0071600 | 0.021329 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | sum2(x[idxs]) | 1.662809 | 1.682049 | 2.198174 | 1.671255 | 1.655456 | 41.878704 |
| 2 | sum2(x, idxs) | 1.693673 | 1.728955 | 1.750705 | 1.719430 | 1.707812 | 4.213552 |
Figure: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on integer+n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 10000 vector
> x <- data[["n = 10000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5337016 285.1 8529671 455.6 8529671 455.6
Vcells 15836808 120.9 35919338 274.1 101881463 777.3
> stats <- microbenchmark(sum2_x_S = sum2(x_S), `sum2(x, idxs)` = sum2(x, idxs = idxs), `sum2(x[idxs])` = sum2(x[idxs]),
+ unit = "ms")
Table: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on integer+n = 10000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 0.016542 | 0.0171135 | 0.0180283 | 0.0175225 | 0.0181555 | 0.043571 |
| 3 | sum2(x[idxs]) | 0.037432 | 0.0386760 | 0.0411477 | 0.0397415 | 0.0410015 | 0.082208 |
| 2 | sum2(x, idxs) | 0.039996 | 0.0416600 | 0.0431274 | 0.0424055 | 0.0436945 | 0.060357 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | sum2(x[idxs]) | 2.262846 | 2.259970 | 2.282401 | 2.268027 | 2.258351 | 1.886759 |
| 2 | sum2(x, idxs) | 2.417845 | 2.434335 | 2.392212 | 2.420060 | 2.406681 | 1.385256 |
Figure: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on integer+n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 100000 vector
> x <- data[["n = 100000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5337088 285.1 8529671 455.6 8529671 455.6
Vcells 15900368 121.4 35919338 274.1 101881463 777.3
> stats <- microbenchmark(sum2_x_S = sum2(x_S), `sum2(x, idxs)` = sum2(x, idxs = idxs), `sum2(x[idxs])` = sum2(x[idxs]),
+ unit = "ms")
Table: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on integer+n = 100000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 0.103240 | 0.111763 | 0.1629306 | 0.1360855 | 0.2284565 | 0.309776 |
| 2 | sum2(x, idxs) | 0.275884 | 0.305763 | 0.4091385 | 0.3538015 | 0.5390420 | 0.732925 |
| 3 | sum2(x[idxs]) | 0.269274 | 0.291527 | 0.4129744 | 0.3563350 | 0.5721275 | 0.699163 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | sum2(x, idxs) | 2.672259 | 2.735816 | 2.511121 | 2.599847 | 2.359495 | 2.365984 |
| 3 | sum2(x[idxs]) | 2.608233 | 2.608439 | 2.534664 | 2.618464 | 2.504317 | 2.256995 |
Figure: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on integer+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 1000000 vector
> x <- data[["n = 1000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5337160 285.1 8529671 455.6 8529671 455.6
Vcells 16530417 126.2 35919338 274.1 101881463 777.3
> stats <- microbenchmark(sum2_x_S = sum2(x_S), `sum2(x, idxs)` = sum2(x, idxs = idxs), `sum2(x[idxs])` = sum2(x[idxs]),
+ unit = "ms")
Table: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on integer+n = 1000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 1.035728 | 1.144772 | 1.198305 | 1.186244 | 1.218014 | 1.530576 |
| 3 | sum2(x[idxs]) | 3.770716 | 4.764187 | 5.140660 | 4.982477 | 5.170399 | 18.618434 |
| 2 | sum2(x, idxs) | 4.045700 | 6.052726 | 10.287626 | 6.256565 | 6.515727 | 388.068841 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 |
| 3 | sum2(x[idxs]) | 3.640643 | 4.161689 | 4.289941 | 4.200215 | 4.244942 | 12.16433 |
| 2 | sum2(x, idxs) | 3.906141 | 5.287274 | 8.585145 | 5.274267 | 5.349468 | 253.54431 |
Figure: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on integer+n = 1000000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 10000000 vector
> x <- data[["n = 10000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5337232 285.1 8529671 455.6 8529671 455.6
Vcells 22830465 174.2 35919338 274.1 101881463 777.3
> stats <- microbenchmark(sum2_x_S = sum2(x_S), `sum2(x, idxs)` = sum2(x, idxs = idxs), `sum2(x[idxs])` = sum2(x[idxs]),
+ unit = "ms")
Table: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on integer+n = 10000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 11.93667 | 15.40207 | 17.26997 | 16.03664 | 20.18322 | 25.04504 |
| 2 | sum2(x, idxs) | 119.20238 | 133.66502 | 142.01954 | 143.87386 | 149.18902 | 169.49411 |
| 3 | sum2(x[idxs]) | 128.06503 | 140.13187 | 148.41911 | 145.64447 | 148.90959 | 502.87241 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | sum2(x, idxs) | 9.98623 | 8.678378 | 8.223495 | 8.971574 | 7.391736 | 6.767573 |
| 3 | sum2(x[idxs]) | 10.72870 | 9.098246 | 8.594056 | 9.081984 | 7.377891 | 20.078725 |
Figure: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on integer+n = 10000000 data. Outliers are displayed as crosses. Times are in milliseconds.
Data type “double”
Data
> rvector <- function(n, mode = c("logical", "double", "integer"), range = c(-100, +100), na_prob = 0) {
+ mode <- match.arg(mode)
+ if (mode == "logical") {
+ x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+ } 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
+ x
+ }
> rvectors <- function(scale = 10, seed = 1, ...) {
+ set.seed(seed)
+ data <- list()
+ data[[1]] <- rvector(n = scale * 100, ...)
+ data[[2]] <- rvector(n = scale * 1000, ...)
+ data[[3]] <- rvector(n = scale * 10000, ...)
+ data[[4]] <- rvector(n = scale * 1e+05, ...)
+ data[[5]] <- rvector(n = scale * 1e+06, ...)
+ names(data) <- sprintf("n = %d", sapply(data, FUN = length))
+ data
+ }
> data <- rvectors(mode = mode)
Results
n = 1000 vector
> x <- data[["n = 1000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5337304 285.1 8529671 455.6 8529671 455.6
Vcells 21387621 163.2 43183205 329.5 101881463 777.3
> stats <- microbenchmark(sum2_x_S = sum2(x_S), `sum2(x, idxs)` = sum2(x, idxs = idxs), `sum2(x[idxs])` = sum2(x[idxs]),
+ unit = "ms")
Table: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on double+n = 1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 0.003315 | 0.0068155 | 0.0065835 | 0.0070115 | 0.0071520 | 0.007755 |
| 3 | sum2(x[idxs]) | 0.005372 | 0.0105045 | 0.0107345 | 0.0108215 | 0.0110625 | 0.051268 |
| 2 | sum2(x, idxs) | 0.006120 | 0.0117700 | 0.0117878 | 0.0121080 | 0.0122820 | 0.014512 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | sum2(x[idxs]) | 1.620513 | 1.541266 | 1.630519 | 1.543393 | 1.546770 | 6.610961 |
| 2 | sum2(x, idxs) | 1.846154 | 1.726946 | 1.790510 | 1.726877 | 1.717282 | 1.871309 |
Figure: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on double+n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 10000 vector
> x <- data[["n = 10000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5337376 285.1 8529671 455.6 8529671 455.6
Vcells 21397118 163.3 43183205 329.5 101881463 777.3
> stats <- microbenchmark(sum2_x_S = sum2(x_S), `sum2(x, idxs)` = sum2(x, idxs = idxs), `sum2(x[idxs])` = sum2(x[idxs]),
+ unit = "ms")
Table: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on double+n = 10000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 0.012539 | 0.013029 | 0.0138391 | 0.0133220 | 0.0136885 | 0.025307 |
| 3 | sum2(x[idxs]) | 0.029324 | 0.030377 | 0.0344914 | 0.0326805 | 0.0351940 | 0.098803 |
| 2 | sum2(x, idxs) | 0.037780 | 0.038241 | 0.0419622 | 0.0406485 | 0.0418245 | 0.082499 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | sum2(x[idxs]) | 2.338624 | 2.331491 | 2.492310 | 2.453123 | 2.571063 | 3.904177 |
| 2 | sum2(x, idxs) | 3.012999 | 2.935068 | 3.032139 | 3.051231 | 3.055448 | 3.259928 |
Figure: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on double+n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 100000 vector
> x <- data[["n = 100000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5337448 285.1 8529671 455.6 8529671 455.6
Vcells 21491986 164.0 43183205 329.5 101881463 777.3
> stats <- microbenchmark(sum2_x_S = sum2(x_S), `sum2(x, idxs)` = sum2(x, idxs = idxs), `sum2(x[idxs])` = sum2(x[idxs]),
+ unit = "ms")
Table: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on double+n = 100000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 0.074775 | 0.0787330 | 0.0948227 | 0.0911010 | 0.1053205 | 0.176324 |
| 2 | sum2(x, idxs) | 0.267652 | 0.2787425 | 0.3251536 | 0.3077015 | 0.3561590 | 0.504595 |
| 3 | sum2(x[idxs]) | 0.255743 | 0.2721100 | 0.3210709 | 0.3160875 | 0.3490395 | 0.603275 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | sum2(x, idxs) | 3.579432 | 3.540352 | 3.429068 | 3.377586 | 3.381668 | 2.861749 |
| 3 | sum2(x[idxs]) | 3.420167 | 3.456111 | 3.386012 | 3.469638 | 3.314070 | 3.421400 |
Figure: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on double+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 1000000 vector
> x <- data[["n = 1000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5337520 285.1 8529671 455.6 8529671 455.6
Vcells 22437417 171.2 43183205 329.5 101881463 777.3
> stats <- microbenchmark(sum2_x_S = sum2(x_S), `sum2(x, idxs)` = sum2(x, idxs = idxs), `sum2(x[idxs])` = sum2(x[idxs]),
+ unit = "ms")
Table: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on double+n = 1000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 1.275939 | 1.346394 | 1.409058 | 1.372686 | 1.447218 | 1.930537 |
| 3 | sum2(x[idxs]) | 7.534066 | 9.775640 | 10.968739 | 10.861674 | 12.071755 | 22.010154 |
| 2 | sum2(x, idxs) | 7.902787 | 10.818909 | 12.123413 | 11.409294 | 12.962701 | 25.696688 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 |
| 3 | sum2(x[idxs]) | 5.904723 | 7.260612 | 7.784446 | 7.912713 | 8.341352 | 11.40105 |
| 2 | sum2(x, idxs) | 6.193703 | 8.035473 | 8.603910 | 8.311653 | 8.956979 | 13.31064 |
Figure: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on double+n = 1000000 data. Outliers are displayed as crosses. Times are in milliseconds.
n = 10000000 vector
> x <- data[["n = 10000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 5337592 285.1 8529671 455.6 8529671 455.6
Vcells 31887465 243.3 51899846 396.0 101881463 777.3
> stats <- microbenchmark(sum2_x_S = sum2(x_S), `sum2(x, idxs)` = sum2(x, idxs = idxs), `sum2(x[idxs])` = sum2(x[idxs]),
+ unit = "ms")
Table: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on double+n = 10000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 10.47633 | 13.53525 | 16.05734 | 14.62333 | 19.78404 | 21.19626 |
| 2 | sum2(x, idxs) | 129.07190 | 168.06425 | 180.12031 | 175.49009 | 187.25718 | 571.11144 |
| 3 | sum2(x[idxs]) | 138.63391 | 179.40518 | 187.78671 | 185.09707 | 196.74914 | 216.24259 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 1.00000 | 1.00000 | 1.00000 | 1.00000 | 1.000000 | 1.00000 |
| 2 | sum2(x, idxs) | 12.32034 | 12.41678 | 11.21732 | 12.00070 | 9.465061 | 26.94397 |
| 3 | sum2(x[idxs]) | 13.23306 | 13.25466 | 11.69476 | 12.65766 | 9.944840 | 10.20192 |
Figure: Benchmarking of sum2_x_S(), sum2(x, idxs)() and sum2(x[idxs])() on double+n = 10000000 data. Outliers are displayed as crosses. Times are in milliseconds.
Appendix
Session information
R version 4.1.1 Patched (2021-08-10 r80727)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS
Matrix products: default
BLAS: /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-4-1-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_1.4-7 matrixStats_0.60.1 ggplot2_3.3.5
[4] knitr_1.33 R.devices_2.17.0 R.utils_2.10.1
[7] R.oo_1.24.0 R.methodsS3_1.8.1-9001 history_0.0.1-9000
loaded via a namespace (and not attached):
[1] Biobase_2.52.0 httr_1.4.2 splines_4.1.1
[4] bit64_4.0.5 network_1.17.1 assertthat_0.2.1
[7] highr_0.9 stats4_4.1.1 blob_1.2.2
[10] GenomeInfoDbData_1.2.6 robustbase_0.93-8 pillar_1.6.2
[13] RSQLite_2.2.8 lattice_0.20-44 glue_1.4.2
[16] digest_0.6.27 XVector_0.32.0 colorspace_2.0-2
[19] Matrix_1.3-4 XML_3.99-0.7 pkgconfig_2.0.3
[22] zlibbioc_1.38.0 genefilter_1.74.0 purrr_0.3.4
[25] ergm_4.1.2 xtable_1.8-4 scales_1.1.1
[28] tibble_3.1.4 annotate_1.70.0 KEGGREST_1.32.0
[31] farver_2.1.0 generics_0.1.0 IRanges_2.26.0
[34] ellipsis_0.3.2 cachem_1.0.6 withr_2.4.2
[37] BiocGenerics_0.38.0 mime_0.11 survival_3.2-13
[40] magrittr_2.0.1 crayon_1.4.1 statnet.common_4.5.0
[43] memoise_2.0.0 laeken_0.5.1 fansi_0.5.0
[46] R.cache_0.15.0 MASS_7.3-54 R.rsp_0.44.0
[49] progressr_0.8.0 tools_4.1.1 lifecycle_1.0.0
[52] S4Vectors_0.30.0 trust_0.1-8 munsell_0.5.0
[55] tabby_0.0.1-9001 AnnotationDbi_1.54.1 Biostrings_2.60.2
[58] compiler_4.1.1 GenomeInfoDb_1.28.1 rlang_0.4.11
[61] grid_4.1.1 RCurl_1.98-1.4 cwhmisc_6.6
[64] rappdirs_0.3.3 startup_0.15.0 labeling_0.4.2
[67] bitops_1.0-7 base64enc_0.1-3 boot_1.3-28
[70] gtable_0.3.0 DBI_1.1.1 markdown_1.1
[73] R6_2.5.1 lpSolveAPI_5.5.2.0-17.7 rle_0.9.2
[76] dplyr_1.0.7 fastmap_1.1.0 bit_4.0.4
[79] utf8_1.2.2 parallel_4.1.1 Rcpp_1.0.7
[82] vctrs_0.3.8 png_0.1-7 DEoptimR_1.0-9
[85] tidyselect_1.1.1 xfun_0.25 coda_0.19-4
Total processing time was 1.51 mins.
Reproducibility
To reproduce this report, do:
html <- matrixStats:::benchmark('sum2_subset')
Copyright Dongcan Jiang. Last updated on 2021-08-25 19:27:41 (+0200 UTC). Powered by RSP.