matrixStats.benchmarks

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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	0.001299	0.0015820	0.0018379	0.0017665	0.0018650	0.009142
3	indexByRow_R2	0.005909	0.0065965	0.0068262	0.0067405	0.0069570	0.011900
2	indexByRow_R1	0.006170	0.0066940	0.0071205	0.0068535	0.0070345	0.030130

	expr	min	lq	mean	median	uq	max
1	indexByRow	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	indexByRow_R2	4.548884	4.169722	3.714200	3.815737	3.730295	1.301684
2	indexByRow_R1	4.749808	4.231353	3.874354	3.879706	3.771850	3.295778

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	0.002651	0.0028410	0.0030917	0.003025	0.0031490	0.010353
2	indexByRow_R1	0.006865	0.0074755	0.0081541	0.007747	0.0081120	0.022752
3	indexByRow_R2	0.008505	0.0091020	0.0095135	0.009320	0.0095155	0.026926

	expr	min	lq	mean	median	uq	max
1	indexByRow	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	indexByRow_R1	2.589589	2.631292	2.637413	2.560992	2.576056	2.197624
3	indexByRow_R2	3.208223	3.203801	3.077106	3.080992	3.021753	2.600792

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	0.002170	0.0026120	0.0028964	0.0027420	0.0029275	0.015684
3	indexByRow_R2	0.005774	0.0066035	0.0069382	0.0068375	0.0069935	0.015883
2	indexByRow_R1	0.006599	0.0075625	0.0080337	0.0077960	0.0080375	0.026498

	expr	min	lq	mean	median	uq	max
1	indexByRow	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	indexByRow_R2	2.660830	2.528139	2.395483	2.493618	2.388898	1.012688
2	indexByRow_R1	3.041014	2.895291	2.773709	2.843180	2.745517	1.689493

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	0.014707	0.0157655	0.0169439	0.0166400	0.0177275	0.029435
3	indexByRow_R2	0.071444	0.0762680	0.0818437	0.0799215	0.0864430	0.125276
2	indexByRow_R1	0.072069	0.0779185	0.0825318	0.0814940	0.0860730	0.109465

	expr	min	lq	mean	median	uq	max
1	indexByRow	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	indexByRow_R2	4.857823	4.837652	4.830278	4.802975	4.876209	4.256022
2	indexByRow_R1	4.900320	4.942342	4.870888	4.897476	4.855338	3.718872

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	0.072910	0.080718	0.0902224	0.0901955	0.096851	0.142181
1	indexByRow	0.078984	0.086229	0.0990125	0.0966650	0.106715	0.137972
3	indexByRow_R2	0.298727	0.318073	0.3661331	0.3549020	0.400063	0.504130

	expr	min	lq	mean	median	uq	max
2	indexByRow_R1	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	indexByRow	1.083308	1.068275	1.097427	1.071727	1.101847	0.9703969
3	indexByRow_R2	4.097202	3.940546	4.058118	3.934808	4.130706	3.5456918

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	0.052460	0.0580035	0.0624434	0.0613560	0.0660955	0.083137
2	indexByRow_R1	0.084546	0.0917165	0.0991287	0.0963445	0.1056215	0.137692
3	indexByRow_R2	0.167735	0.1826280	0.1959618	0.1942570	0.2084645	0.255780

	expr	min	lq	mean	median	uq	max
1	indexByRow	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	indexByRow_R1	1.611628	1.581223	1.587497	1.570254	1.598013	1.656206
3	indexByRow_R2	3.197389	3.148569	3.138229	3.166064	3.153989	3.076608

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	0.013854	0.0152330	0.0164959	0.0160885	0.0170400	0.028068
3	indexByRow_R2	0.069970	0.0760695	0.0806432	0.0794470	0.0847020	0.100642
2	indexByRow_R1	0.070949	0.0755335	0.0830653	0.0817080	0.0853975	0.229437

	expr	min	lq	mean	median	uq	max
1	indexByRow	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	indexByRow_R2	5.050527	4.993731	4.888670	4.938123	4.970775	3.585649
2	indexByRow_R1	5.121192	4.958544	5.035497	5.078659	5.011590	8.174327

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	0.072431	0.0765610	0.0887787	0.0887680	0.0961470	0.126798
1	indexByRow	0.079205	0.0860535	0.0995028	0.0989155	0.1069795	0.141722
3	indexByRow_R2	0.298908	0.3254525	0.3678825	0.3571470	0.3919725	0.513092

	expr	min	lq	mean	median	uq	max
2	indexByRow_R1	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	indexByRow	1.093524	1.123986	1.120796	1.114315	1.112666	1.117699
3	indexByRow_R2	4.126797	4.250891	4.143814	4.023376	4.076804	4.046531

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	0.054830	0.0582375	0.0635264	0.0615535	0.0664515	0.087094
2	indexByRow_R1	0.085742	0.0927930	0.1000789	0.0967470	0.1055590	0.148024
3	indexByRow_R2	0.171513	0.1877405	0.2000782	0.1948435	0.2152745	0.249066

	expr	min	lq	mean	median	uq	max
1	indexByRow	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	indexByRow_R1	1.563779	1.593355	1.575390	1.571755	1.588512	1.699589
3	indexByRow_R2	3.128087	3.223705	3.149527	3.165433	3.239573	2.859738

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	0.013077	0.0141160	0.0154194	0.0152745	0.0161520	0.031682
3	indexByRow_R2	0.068060	0.0716075	0.0772792	0.0761455	0.0804155	0.118814
2	indexByRow_R1	0.067850	0.0719840	0.0779652	0.0763925	0.0810255	0.113477

	expr	min	lq	mean	median	uq	max
1	indexByRow	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	indexByRow_R2	5.204558	5.072790	5.011807	4.985139	4.978671	3.750205
2	indexByRow_R1	5.188499	5.099462	5.056302	5.001309	5.016438	3.581750

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	0.071935	0.0802495	0.0911010	0.0915240	0.1001050	0.134227
1	indexByRow	0.079312	0.0863960	0.1006102	0.1005545	0.1104035	0.147812
3	indexByRow_R2	0.292392	0.3170705	0.3595154	0.3465860	0.3822290	0.569033

	expr	min	lq	mean	median	uq	max
2	indexByRow_R1	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	indexByRow	1.102551	1.076592	1.104382	1.098668	1.102877	1.101209
3	indexByRow_R2	4.064669	3.951059	3.946340	3.786832	3.818281	4.239333

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	0.055208	0.0599835	0.0649007	0.0622565	0.067882	0.089031
2	indexByRow_R1	0.084403	0.0930065	0.1009876	0.0979790	0.108639	0.154516
3	indexByRow_R2	0.168124	0.1821755	0.1951964	0.1903740	0.210525	0.250651

	expr	min	lq	mean	median	uq	max
1	indexByRow	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	indexByRow_R1	1.528818	1.550535	1.556033	1.573795	1.600410	1.735530
3	indexByRow_R2	3.045283	3.037094	3.007618	3.057898	3.101338	2.815323

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	0.096349	0.1104665	0.1422542	0.1195365	0.2001225	0.217076
3	indexByRow_R2	0.525708	0.5882020	0.6891502	0.6551905	0.8073100	0.856582
2	indexByRow_R1	0.526196	0.6100990	0.8132235	0.7463895	0.8094195	10.693230

	expr	min	lq	mean	median	uq	max
1	indexByRow	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000
3	indexByRow_R2	5.456289	5.324709	4.844498	5.481091	4.034079	3.94600
2	indexByRow_R1	5.461354	5.522932	5.716692	6.244030	4.044620	49.26031

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	0.668629	0.6991650	0.7722982	0.717976	0.763533	1.226463
1	indexByRow	0.779617	0.8038425	0.8569607	0.832211	0.891887	1.189537
3	indexByRow_R2	3.013338	3.0963505	3.5061742	3.212157	3.689051	11.740948

	expr	min	lq	mean	median	uq	max
2	indexByRow_R1	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
1	indexByRow	1.165993	1.149718	1.109624	1.159107	1.168105	0.9698923
3	indexByRow_R2	4.506741	4.428641	4.539923	4.473907	4.831554	9.5730144

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	0.459125	0.4680545	0.6008779	0.4868045	0.574379	9.133765
2	indexByRow_R1	0.707026	0.7339155	0.8397930	0.7643920	0.990676	1.102537
3	indexByRow_R2	1.483072	1.5023825	1.7314764	1.6263660	1.905382	3.071705

	expr	min	lq	mean	median	uq	max
1	indexByRow	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	indexByRow_R1	1.539942	1.568013	1.397610	1.570224	1.724778	0.1207100
3	indexByRow_R2	3.230214	3.209845	2.881578	3.340902	3.317290	0.3363022

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	0.099831	0.1119940	0.1373374	0.122643	0.1476510	0.215146
2	indexByRow_R1	0.482236	0.5211715	0.6253584	0.596648	0.7752655	0.846253
3	indexByRow_R2	0.481438	0.5233860	0.6955313	0.602666	0.7354995	6.996129

	expr	min	lq	mean	median	uq	max
1	indexByRow	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	indexByRow_R1	4.830524	4.653566	4.553445	4.864917	5.250662	3.933389
3	indexByRow_R2	4.822530	4.673340	5.064398	4.913986	4.981338	32.518053

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	0.672735	0.6933185	0.8037763	0.7172435	0.9851695	1.056198
1	indexByRow	0.779524	0.7961490	0.8712172	0.8563715	0.9341550	1.203426
3	indexByRow_R2	3.029157	3.0778145	3.5656160	3.2164275	3.9160485	11.938071

	expr	min	lq	mean	median	uq	max
2	indexByRow_R1	1.000000	1.000000	1.000000	1.000000	1.0000000	1.000000
1	indexByRow	1.158739	1.148316	1.083905	1.193976	0.9482175	1.139394
3	indexByRow_R2	4.502749	4.439250	4.436080	4.484429	3.9749997	11.302872

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	0.458298	0.4623595	0.5152177	0.4865565	0.5682305	0.734950
2	indexByRow_R1	0.704467	0.7249640	0.8760234	0.7468985	0.8710335	7.363990
3	indexByRow_R2	1.487527	1.5049120	1.6739440	1.5726410	1.8617750	2.334786

	expr	min	lq	mean	median	uq	max
1	indexByRow	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	indexByRow_R1	1.537137	1.567966	1.700298	1.535070	1.532888	10.019716
3	indexByRow_R2	3.245764	3.254853	3.249003	3.232186	3.276443	3.176796

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 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 18.34 secs.

Reproducibility

To reproduce this report, do:

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

---

Copyright Henrik Bengtsson. Last updated on 2021-08-25 19:14:31 (+0200 UTC). Powered by RSP.

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
+ }