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 5351096 285.8 7916910 422.9 7916910 422.9
Vcells 17018782 129.9 35130986 268.1 94934136 724.3
> 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 | 0.004058 | 0.004164 | 0.0043724 | 0.0042300 | 0.0043345 | 0.007633 |
| 2 | sum2(x, idxs) | 0.005715 | 0.005797 | 0.0062388 | 0.0058745 | 0.0060165 | 0.015764 |
| 3 | sum2(x[idxs]) | 0.006886 | 0.007081 | 0.0093681 | 0.0071920 | 0.0072985 | 0.212567 |
| 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) | 1.408329 | 1.392171 | 1.426875 | 1.388771 | 1.388049 | 2.065243 |
| 3 | sum2(x[idxs]) | 1.696895 | 1.700528 | 2.142571 | 1.700236 | 1.683816 | 27.848421 |
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 5349070 285.7 7916910 422.9 7916910 422.9
Vcells 15889485 121.3 35130986 268.1 94934136 724.3
> 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 | 0.014937 | 0.0150565 | 0.0157990 | 0.015211 | 0.0155210 | 0.036959 |
| 2 | sum2(x, idxs) | 0.029887 | 0.0301085 | 0.0312808 | 0.030231 | 0.0307245 | 0.052853 |
| 3 | sum2(x[idxs]) | 0.037243 | 0.0377700 | 0.0395601 | 0.038266 | 0.0392410 | 0.073865 |
| 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) | 2.000870 | 1.999701 | 1.979917 | 1.987443 | 1.979544 | 1.430044 |
| 3 | sum2(x[idxs]) | 2.493339 | 2.508551 | 2.503953 | 2.515679 | 2.528252 | 1.998566 |
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 5349142 285.7 7916910 422.9 7916910 422.9
Vcells 15953045 121.8 35130986 268.1 94934136 724.3
> 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 | 0.074582 | 0.0770210 | 0.0935544 | 0.0942520 | 0.1041050 | 0.153356 |
| 2 | sum2(x, idxs) | 0.173005 | 0.1877705 | 0.2107951 | 0.2054650 | 0.2262850 | 0.305723 |
| 3 | sum2(x[idxs]) | 0.225503 | 0.2491455 | 0.2874428 | 0.2861915 | 0.3112985 | 0.453296 |
| 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) | 2.319662 | 2.437913 | 2.253183 | 2.179954 | 2.173623 | 1.993551 |
| 3 | sum2(x[idxs]) | 3.023558 | 3.234774 | 3.072468 | 3.036450 | 2.990236 | 2.955841 |
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 5349214 285.7 7916910 422.9 7916910 422.9
Vcells 16583094 126.6 35130986 268.1 94934136 724.3
> 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 | 0.899186 | 0.955913 | 0.9781089 | 0.9745195 | 0.9910775 | 1.247664 |
| 2 | sum2(x, idxs) | 2.484263 | 2.729147 | 2.9763069 | 2.9132910 | 3.0965315 | 5.170635 |
| 3 | sum2(x[idxs]) | 3.485286 | 4.653993 | 5.0484895 | 5.0643155 | 5.2687700 | 18.574634 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | sum2(x, idxs) | 2.762791 | 2.855016 | 3.04292 | 2.989464 | 3.124409 | 4.144253 |
| 3 | sum2(x[idxs]) | 3.876046 | 4.868637 | 5.16148 | 5.196731 | 5.316204 | 14.887529 |
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 5349286 285.7 7916910 422.9 7916910 422.9
Vcells 22883142 174.6 35130986 268.1 94934136 724.3
> 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 | 10.12059 | 12.52840 | 14.53045 | 13.35103 | 15.23759 | 21.53914 |
| 2 | sum2(x, idxs) | 84.54040 | 99.31616 | 112.45785 | 113.54498 | 123.88016 | 146.49164 |
| 3 | sum2(x[idxs]) | 121.28929 | 133.40567 | 149.59686 | 148.72843 | 164.35145 | 190.94653 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 1.00000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | sum2(x, idxs) | 8.35331 | 7.927284 | 7.73946 | 8.504586 | 8.129906 | 6.801184 |
| 3 | sum2(x[idxs]) | 11.98441 | 10.648263 | 10.29540 | 11.139848 | 10.785922 | 8.865096 |
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 5349358 285.7 7916910 422.9 7916910 422.9
Vcells 21440297 163.6 42237183 322.3 94934136 724.3
> 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 | 0.004073 | 0.0041675 | 0.0043044 | 0.0042630 | 0.0043295 | 0.005104 |
| 2 | sum2(x, idxs) | 0.005691 | 0.0058320 | 0.0060248 | 0.0059345 | 0.0060655 | 0.011282 |
| 3 | sum2(x[idxs]) | 0.006600 | 0.0067975 | 0.0072208 | 0.0069115 | 0.0070985 | 0.030047 |
| 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) | 1.397250 | 1.399400 | 1.399687 | 1.392095 | 1.400970 | 2.210423 |
| 3 | sum2(x[idxs]) | 1.620427 | 1.631074 | 1.677545 | 1.621276 | 1.639566 | 5.886951 |
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 5349430 285.7 7916910 422.9 7916910 422.9
Vcells 21449794 163.7 42237183 322.3 94934136 724.3
> 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 | 0.014450 | 0.0151475 | 0.0155525 | 0.0153985 | 0.0156285 | 0.030081 |
| 2 | sum2(x, idxs) | 0.028984 | 0.0294010 | 0.0301643 | 0.0304145 | 0.0305615 | 0.032902 |
| 3 | sum2(x[idxs]) | 0.033606 | 0.0353325 | 0.0364819 | 0.0360850 | 0.0366540 | 0.074489 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 |
| 2 | sum2(x, idxs) | 2.005813 | 1.940980 | 1.939518 | 1.97516 | 1.955498 | 1.093780 |
| 3 | sum2(x[idxs]) | 2.325675 | 2.332563 | 2.345728 | 2.34341 | 2.345331 | 2.476281 |
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 5349502 285.7 7916910 422.9 7916910 422.9
Vcells 21544662 164.4 42237183 322.3 94934136 724.3
> 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 | 0.074623 | 0.0772380 | 0.0908662 | 0.0887490 | 0.0977030 | 0.165804 |
| 2 | sum2(x, idxs) | 0.183186 | 0.1884210 | 0.2149429 | 0.2049695 | 0.2333605 | 0.293395 |
| 3 | sum2(x[idxs]) | 0.262342 | 0.2864695 | 0.3175127 | 0.3174385 | 0.3360615 | 0.568722 |
| 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) | 2.454820 | 2.439486 | 2.365488 | 2.309541 | 2.388468 | 1.769529 |
| 3 | sum2(x[idxs]) | 3.515565 | 3.708919 | 3.494289 | 3.576812 | 3.439623 | 3.430086 |
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 5349574 285.7 7916910 422.9 7916910 422.9
Vcells 22490092 171.6 42237183 322.3 94934136 724.3
> 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 | 1.237533 | 1.342108 | 1.439008 | 1.437491 | 1.507708 | 1.864939 |
| 2 | sum2(x, idxs) | 5.983188 | 6.391203 | 6.724696 | 6.551378 | 6.876295 | 9.803509 |
| 3 | sum2(x[idxs]) | 7.418274 | 8.139433 | 9.955356 | 10.303071 | 10.981101 | 20.978886 |
| 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) | 4.834771 | 4.762065 | 4.673146 | 4.557510 | 4.560760 | 5.256745 |
| 3 | sum2(x[idxs]) | 5.994405 | 6.064666 | 6.918207 | 7.167402 | 7.283308 | 11.249100 |
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 5349646 285.8 7916910 422.9 7916910 422.9
Vcells 31940140 243.7 50764619 387.4 94934136 724.3
> 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 | 10.43411 | 13.53736 | 16.58253 | 14.54233 | 20.96135 | 26.28596 |
| 2 | sum2(x, idxs) | 103.56397 | 145.32464 | 161.57033 | 164.40443 | 180.04523 | 221.33732 |
| 3 | sum2(x[idxs]) | 147.64456 | 184.61754 | 200.38570 | 194.71552 | 207.89210 | 586.47072 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | sum2_x_S | 1.00000 | 1.00000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 |
| 2 | sum2(x, idxs) | 9.92552 | 10.73508 | 9.743407 | 11.30523 | 8.589392 | 8.420363 |
| 3 | sum2(x[idxs]) | 14.15018 | 13.63763 | 12.084146 | 13.38957 | 9.917879 | 22.311177 |
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_0.60.0 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] rstudioapi_0.13 rappdirs_0.3.3 startup_0.15.0-9000
[67] labeling_0.4.2 bitops_1.0-7 base64enc_0.1-3
[70] boot_1.3-28 gtable_0.3.0 DBI_1.1.1
[73] markdown_1.1 R6_2.5.1 lpSolveAPI_5.5.2.0-17.7
[76] rle_0.9.2 dplyr_1.0.7 fastmap_1.1.0
[79] bit_4.0.4 utf8_1.2.2 parallel_4.1.1
[82] Rcpp_1.0.7 vctrs_0.3.8 png_0.1-7
[85] DEoptimR_1.0-9 tidyselect_1.1.1 xfun_0.25
[88] coda_0.19-4
Total processing time was 1.42 mins.
Reproducibility
To reproduce this report, do:
html <- matrixStats:::benchmark('sum2_subset')
Copyright Dongcan Jiang. Last updated on 2021-08-25 22:48:06 (+0200 UTC). Powered by RSP.