>X<-data[["10x10"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells3208969171.45709258305.05709258305.0Vcells638784048.822345847170.556666022432.4>colStats<- microbenchmark(colTabulates= colTabulates(X, na.rm=FALSE), unit="ms")
>X<- t(X)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells3207288171.35709258305.05709258305.0Vcells638277748.722345847170.556666022432.4>rowStats<- microbenchmark(rowTabulates= rowTabulates(X, na.rm=FALSE), unit="ms")
Table: Benchmarking of colTabulates() on 10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
expr
min
lq
mean
median
uq
max
1
colTabulates
0.097305
0.0985175
0.100785
0.099041
0.0997155
0.230715
expr
min
lq
mean
median
uq
max
1
colTabulates
1
1
1
1
1
1
Table: Benchmarking of rowTabulates() on 10x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
expr
min
lq
mean
median
uq
max
1
rowTabulates
0.0947
0.095504
0.0980804
0.096104
0.096741
0.227335
expr
min
lq
mean
median
uq
max
1
rowTabulates
1
1
1
1
1
1
Figure: Benchmarking of colTabulates() on 10x10 data as well as rowTabulates() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colTabulates() and rowTabulates() on 10x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
expr
min
lq
mean
median
uq
max
2
rowTabulates
94.700
95.5040
98.08044
96.104
96.7410
227.335
1
colTabulates
97.305
98.5175
100.78495
99.041
99.7155
230.715
expr
min
lq
mean
median
uq
max
2
rowTabulates
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1
colTabulates
1.027508
1.031554
1.027574
1.030561
1.030747
1.014868
Figure: Benchmarking of colTabulates() and rowTabulates() on 10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
100x100 matrix
>X<-data[["100x100"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells3205796171.35709258305.05709258305.0Vcells618833347.322345847170.556666022432.4>colStats<- microbenchmark(colTabulates= colTabulates(X, na.rm=FALSE), unit="ms")
>X<- t(X)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells3205790171.35709258305.05709258305.0Vcells619337647.322345847170.556666022432.4>rowStats<- microbenchmark(rowTabulates= rowTabulates(X, na.rm=FALSE), unit="ms")
Table: Benchmarking of colTabulates() on 100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
expr
min
lq
mean
median
uq
max
1
colTabulates
0.398019
0.4012145
0.4066294
0.403242
0.4084285
0.535625
expr
min
lq
mean
median
uq
max
1
colTabulates
1
1
1
1
1
1
Table: Benchmarking of rowTabulates() on 100x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
expr
min
lq
mean
median
uq
max
1
rowTabulates
0.456053
0.4607885
0.4757639
0.4671985
0.472886
0.690054
expr
min
lq
mean
median
uq
max
1
rowTabulates
1
1
1
1
1
1
Figure: Benchmarking of colTabulates() on 100x100 data as well as rowTabulates() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colTabulates() and rowTabulates() on 100x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
expr
min
lq
mean
median
uq
max
1
colTabulates
398.019
401.2145
406.6294
403.2420
408.4285
535.625
2
rowTabulates
456.053
460.7885
475.7639
467.1985
472.8860
690.054
expr
min
lq
mean
median
uq
max
1
colTabulates
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
2
rowTabulates
1.145807
1.148484
1.170018
1.158606
1.157818
1.288315
Figure: Benchmarking of colTabulates() and rowTabulates() on 100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
1000x10 matrix
>X<-data[["1000x10"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells3206509171.35709258305.05709258305.0Vcells619162747.322345847170.556666022432.4>colStats<- microbenchmark(colTabulates= colTabulates(X, na.rm=FALSE), unit="ms")
>X<- t(X)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells3206500171.35709258305.05709258305.0Vcells619666547.322345847170.556666022432.4>rowStats<- microbenchmark(rowTabulates= rowTabulates(X, na.rm=FALSE), unit="ms")
Table: Benchmarking of colTabulates() on 1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
expr
min
lq
mean
median
uq
max
1
colTabulates
0.37314
0.3751665
0.3802066
0.377041
0.381153
0.50368
expr
min
lq
mean
median
uq
max
1
colTabulates
1
1
1
1
1
1
Table: Benchmarking of rowTabulates() on 1000x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
expr
min
lq
mean
median
uq
max
1
rowTabulates
0.461854
0.469589
0.4768716
0.4736355
0.4802825
0.639372
expr
min
lq
mean
median
uq
max
1
rowTabulates
1
1
1
1
1
1
Figure: Benchmarking of colTabulates() on 1000x10 data as well as rowTabulates() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colTabulates() and rowTabulates() on 1000x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
expr
min
lq
mean
median
uq
max
1
colTabulates
373.140
375.1665
380.2066
377.0410
381.1530
503.680
2
rowTabulates
461.854
469.5890
476.8716
473.6355
480.2825
639.372
expr
min
lq
mean
median
uq
max
1
colTabulates
1.00000
1.000000
1.000000
1.000000
1.000000
1.000000
2
rowTabulates
1.23775
1.251682
1.254244
1.256191
1.260078
1.269401
Figure: Benchmarking of colTabulates() and rowTabulates() on 1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
10x1000 matrix
>X<-data[["10x1000"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells3206677171.35709258305.05709258305.0Vcells619219647.322345847170.556666022432.4>colStats<- microbenchmark(colTabulates= colTabulates(X, na.rm=FALSE), unit="ms")
>X<- t(X)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells3206671171.35709258305.05709258305.0Vcells619723947.322345847170.556666022432.4>rowStats<- microbenchmark(rowTabulates= rowTabulates(X, na.rm=FALSE), unit="ms")
Table: Benchmarking of colTabulates() on 10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
expr
min
lq
mean
median
uq
max
1
colTabulates
0.480075
0.486541
0.5151467
0.500231
0.5298665
0.825429
expr
min
lq
mean
median
uq
max
1
colTabulates
1
1
1
1
1
1
Table: Benchmarking of rowTabulates() on 10x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
expr
min
lq
mean
median
uq
max
1
rowTabulates
0.488855
0.499118
0.5067292
0.5035765
0.508941
0.628956
expr
min
lq
mean
median
uq
max
1
rowTabulates
1
1
1
1
1
1
Figure: Benchmarking of colTabulates() on 10x1000 data as well as rowTabulates() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colTabulates() and rowTabulates() on 10x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
expr
min
lq
mean
median
uq
max
1
colTabulates
480.075
486.541
515.1467
500.2310
529.8665
825.429
2
rowTabulates
488.855
499.118
506.7292
503.5765
508.9410
628.956
expr
min
lq
mean
median
uq
max
1
colTabulates
1.000000
1.00000
1.00000
1.000000
1.000000
1.0000000
2
rowTabulates
1.018289
1.02585
0.98366
1.006688
0.960508
0.7619747
Figure: Benchmarking of colTabulates() and rowTabulates() on 10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
100x1000 matrix
>X<-data[["100x1000"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells3206848171.35709258305.05709258305.0Vcells619261047.322345847170.556666022432.4>colStats<- microbenchmark(colTabulates= colTabulates(X, na.rm=FALSE), unit="ms")
>X<- t(X)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells3206842171.35709258305.05709258305.0Vcells624265347.722345847170.556666022432.4>rowStats<- microbenchmark(rowTabulates= rowTabulates(X, na.rm=FALSE), unit="ms")
Table: Benchmarking of colTabulates() on 100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
expr
min
lq
mean
median
uq
max
1
colTabulates
2.951937
3.346852
3.654082
3.549232
3.695155
12.4183
expr
min
lq
mean
median
uq
max
1
colTabulates
1
1
1
1
1
1
Table: Benchmarking of rowTabulates() on 100x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
expr
min
lq
mean
median
uq
max
1
rowTabulates
3.421477
3.654142
3.837853
3.700607
3.787078
10.39184
expr
min
lq
mean
median
uq
max
1
rowTabulates
1
1
1
1
1
1
Figure: Benchmarking of colTabulates() on 100x1000 data as well as rowTabulates() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colTabulates() and rowTabulates() on 100x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
expr
min
lq
mean
median
uq
max
1
colTabulates
2.951937
3.346852
3.654082
3.549232
3.695155
12.41830
2
rowTabulates
3.421477
3.654142
3.837853
3.700607
3.787078
10.39184
expr
min
lq
mean
median
uq
max
1
colTabulates
1.000000
1.000000
1.000000
1.00000
1.000000
1.0000000
2
rowTabulates
1.159062
1.091815
1.050292
1.04265
1.024877
0.8368168
Figure: Benchmarking of colTabulates() and rowTabulates() on 100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.
1000x100 matrix
>X<-data[["1000x100"]]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells3207019171.35709258305.05709258305.0Vcells619308047.322345847170.556666022432.4>colStats<- microbenchmark(colTabulates= colTabulates(X, na.rm=FALSE), unit="ms")
>X<- t(X)
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells3207013171.35709258305.05709258305.0Vcells624312347.722345847170.556666022432.4>rowStats<- microbenchmark(rowTabulates= rowTabulates(X, na.rm=FALSE), unit="ms")
Table: Benchmarking of colTabulates() on 1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
expr
min
lq
mean
median
uq
max
1
colTabulates
2.744159
3.067887
3.254978
3.185613
3.323376
13.0348
expr
min
lq
mean
median
uq
max
1
colTabulates
1
1
1
1
1
1
Table: Benchmarking of rowTabulates() on 1000x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
expr
min
lq
mean
median
uq
max
1
rowTabulates
3.512745
3.794685
4.023299
3.829916
4.242972
10.3594
expr
min
lq
mean
median
uq
max
1
rowTabulates
1
1
1
1
1
1
Figure: Benchmarking of colTabulates() on 1000x100 data as well as rowTabulates() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.
Table: Benchmarking of colTabulates() and rowTabulates() on 1000x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.
expr
min
lq
mean
median
uq
max
1
colTabulates
2.744159
3.067887
3.254978
3.185613
3.323376
13.0348
2
rowTabulates
3.512745
3.794685
4.023299
3.829916
4.242972
10.3594
expr
min
lq
mean
median
uq
max
1
colTabulates
1.000000
1.000000
1.000000
1.000000
1.000000
1.0000000
2
rowTabulates
1.280081
1.236905
1.236045
1.202254
1.276706
0.7947496
Figure: Benchmarking of colTabulates() and rowTabulates() on 1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.