DISCLAIMER
The present benchmark was conducted on 2025-06-17 on a Intel® Core™
i7-10510U (8-core CPU, Intel® UHD Graphics (CML GT2) GPU, 16GB RAM,
Fedora 42). Results will vary depending on the hardware. These
benchmarks aim to provide a general idea of the performance differences
between the fio package and other implementations but
should not be considered definitive. The performance of the functions
may also vary depending on the specific data used and the context in
which they are applied.
Introduction
This vignette presents a benchmarking analysis comparing the
performance of functions from the fio package with
equivalent base R functions and functions from other packages. The
fio package provides a set of functions for input-output
analysis, a method used in economics to analyze the interdependencies
between different sectors of an economy.
Our benchmarking tests show that fio package functions
are generally faster than other implementations. This improved
performance can make a substantial difference in larger analyses, making
the fio package a valuable tool for input-output analysis
in R.
The tests were run on simulated square matrices, with dimensions ranging from 100x100 up to 2000x2000, and each test was repeated at least 20 times to account for variability. Please note that the results of this benchmarking analysis depend on the specific test datasets used and the hardware on which the algorithms were run. Therefore, the results should be interpreted in the context of these specific conditions.
Technical coefficients matrix
The technical coefficients matrix calculation, a key and initial step
in input-output analysis, was tested using the
compute_tech_coeff() function from the fio
package, equivalent functions from the leontief package,
and a base R implementation. It consists of dividing each
element of the intermediate transactions matrix by the corresponding
element of the total production vector1.
Results shows that {fio} is generally faster than the other two implementations, especially for larger matrices (≥500x500).
# set seed
set.seed(100)
# Base R function
tech_coeff_r <- function(intermediate_transactions, total_production) {
tech_coeff_matrix <- intermediate_transactions %*% diag(1 / as.vector(total_production))
return(tech_coeff_matrix)
}
# benchmark
benchmark_a <- bench::press(
matrix_dim = c(100, 500, 1000, 2000),
{
intermediate_transactions <- matrix(
as.double(sample(1:1000, matrix_dim^2, replace = TRUE)),
nrow = matrix_dim,
ncol = matrix_dim
)
total_production <- matrix(
as.double(sample(4000000:6000000, matrix_dim, replace = TRUE)),
nrow = 1,
ncol = matrix_dim
)
iom_fio <- fio::iom$new("iom", intermediate_transactions, total_production)
bench::mark(
fio = fio:::compute_tech_coeff(intermediate_transactions, total_production),
`Base R` = tech_coeff_r(intermediate_transactions, total_production),
leontief = leontief::input_requirement(intermediate_transactions, total_production),
iterations = 100
)
}
)
#> Running with:
#> matrix_dim
#> 1 100
#> 2 500
#> 3 1000
#> 4 2000
#> Warning: Some expressions had a GC in every iteration; so filtering is disabled.
print(benchmark_a)
#> # A tibble: 12 × 9
#> expression matrix_dim min median mem_alloc `gc/sec` n_itr n_gc total_time
#> <bch:expr> <dbl> <bch:tm> <bch:tm> <bch:byt> <dbl> <int> <dbl> <bch:tm>
#> 1 fio 100 112.56µs 118.83µs 861.82KB 0 100 0 12.73ms
#> 2 Base R 100 91.71µs 98µs 190.67KB 0 100 0 11.96ms
#> 3 leontief 100 320.35µs 353.71µs 724.27KB 23.5 99 1 42.61ms
#> 4 fio 500 985.86µs 1.4ms 1.91MB 40.6 95 5 123.16ms
#> 5 Base R 500 2.05ms 2.7ms 3.82MB 35.9 90 10 278.89ms
#> 6 leontief 500 5.26ms 6.13ms 16.29MB 241. 39 66 274.29ms
#> 7 fio 1000 5.59ms 6.31ms 7.63MB 23.2 87 13 560.16ms
#> 8 Base R 1000 13.75ms 17.73ms 15.27MB 28.4 66 34 1.2s
#> 9 leontief 1000 32.03ms 32.21ms 65MB 2359. 2 152 64.43ms
#> 10 fio 2000 16.91ms 23.11ms 30.52MB 6.83 100 16 2.34s
#> 11 Base R 2000 132.31ms 168.71ms 61.05MB 2.17 100 37 17.05s
#> 12 leontief 2000 91.15ms 132.83ms 259.7MB 20.8 100 272 13.11s
# plot
ggplot2::autoplot(benchmark_a)
For larger matrices (≥500x500), {fio} is generally faster.
Leontief inverse matrix
The Leontief matrix () is obtained by subtracting the technical coefficients matrix () from the identity matrix (); therefore, it has no null rows or columns. This allows for solving the linear system through LU decomposition, which is a more efficient method than direct inverse matrix calculation.
Results shows that for larger matrices, fio is slightly slower than the alternatives.
# base R function
leontief_inverse_r <- function(technical_coefficients_matrix) {
dim <- nrow(technical_coefficients_matrix)
leontief_inverse_matrix <- solve(diag(dim) - technical_coefficients_matrix)
return(leontief_inverse_matrix)
}
# benchmark
benchmark_b <- bench::press(
matrix_dim = c(100, 500, 1000, 2000),
{
intermediate_transactions <- matrix(
as.double(sample(1:1000, matrix_dim^2, replace = TRUE)),
nrow = matrix_dim,
ncol = matrix_dim
)
total_production <- matrix(
as.double(sample(4000000:6000000, matrix_dim, replace = TRUE)),
nrow = 1,
ncol = matrix_dim
)
iom_fio <- fio::iom$new("iom", intermediate_transactions, total_production)
iom_fio$compute_tech_coeff()
technical_coefficients_matrix <- iom_fio$technical_coefficients_matrix
bench::mark(
fio = fio:::compute_leontief_inverse(technical_coefficients_matrix),
`Base R` = leontief_inverse_r(technical_coefficients_matrix),
leontief = leontief::leontief_inverse(technical_coefficients_matrix),
iterations = 100,
check = FALSE
)
}
)
#> Running with:
#> matrix_dim
#> 1 100
#> 2 500
#> 3 1000
#> 4 2000
#> Warning: Some expressions had a GC in every iteration; so filtering is disabled.
print(benchmark_b)
#> # A tibble: 12 × 9
#> expression matrix_dim min median mem_alloc `gc/sec` n_itr n_gc total_time
#> <bch:expr> <dbl> <bch:tm> <bch:tm> <bch:byt> <dbl> <int> <dbl> <bch:tm>
#> 1 fio 100 191.88µs 226.28µs 158.49KB 0 100 0 27.26ms
#> 2 Base R 100 203.29µs 207.36µs 413.29KB 0 100 0 23.69ms
#> 3 leontief 100 207.99µs 216.64µs 402.57KB 45.6 99 1 21.94ms
#> 4 fio 500 6.61ms 7.67ms 3.81MB 1.31 99 1 765.07ms
#> 5 Base R 500 6.75ms 7.27ms 9.55MB 9.89 92 8 808.97ms
#> 6 leontief 500 7.16ms 7.2ms 9.55MB 13.5 91 9 664.83ms
#> 7 fio 1000 39.35ms 40.58ms 15.26MB 2.12 92 8 3.77s
#> 8 Base R 1000 40.98ms 43.47ms 38.18MB 5.24 81 19 3.63s
#> 9 leontief 1000 42.15ms 43.5ms 38.18MB 5.89 79 21 3.56s
#> 10 fio 2000 272.45ms 297.67ms 61.03MB 0.633 100 19 30.01s
#> 11 Base R 2000 268.28ms 274.25ms 152.66MB 3.64 100 105 28.86s
#> 12 leontief 2000 265.02ms 273.16ms 152.66MB 2.57 100 74 28.76s
# plot
ggplot2::autoplot(benchmark_b)
For larger matrices, fio is slightly slower than the alternatives.
Sensitivity of dispersion coefficients of variation
To evaluate the performance of linkage-based functions, we benchmarked the sensitivity of dispersion coefficients of variation.
Results shows that {fio} is substantially faster than {leontief} across all tested dimensions. Compared to Base R, {fio} is faster for matrices 1000x1000 and larger.
# base R function
sensitivity_r <- function(B) {
n <- nrow(B)
SL = rowSums(B)
ML = SL / n
(((1 / (n - 1)) * (colSums((B - ML) ** 2))) ** 0.5) / ML
}
# benchmark
benchmark_c <- bench::press(
matrix_dim = c(100, 500, 1000, 2000),
{
intermediate_transactions <- matrix(
as.double(sample(1:1000, matrix_dim^2, replace = TRUE)),
nrow = matrix_dim,
ncol = matrix_dim
)
total_production <- matrix(
as.double(sample(4000000:6000000, matrix_dim, replace = TRUE)),
nrow = 1,
ncol = matrix_dim
)
iom_fio <- fio::iom$new("iom", intermediate_transactions, total_production)
iom_fio$compute_tech_coeff()$compute_leontief_inverse()
leontief_inverse_matrix <- iom_fio$leontief_inverse_matrix
bench::mark(
fio = fio:::compute_sensitivity_dispersion_cv(leontief_inverse_matrix),
`Base R` = sensitivity_r(leontief_inverse_matrix),
leontief = leontief::sensitivity_dispersion_cv(leontief_inverse_matrix),
iterations = 100,
check = FALSE
)
}
)
#> Running with:
#> matrix_dim
#> 1 100
#> 2 500
#> 3 1000
#> 4 2000
print(benchmark_c)
#> # A tibble: 12 × 9
#> expression matrix_dim min median mem_alloc `gc/sec` n_itr n_gc total_time
#> <bch:expr> <dbl> <bch:tm> <bch:tm> <bch:byt> <dbl> <int> <dbl> <bch:tm>
#> 1 fio 100 98.81µs 111.06µs 81.21KB 0 100 0 11.48ms
#> 2 Base R 100 52.02µs 53.11µs 81.48KB 0 100 0 12.66ms
#> 3 leontief 100 1.46ms 1.49ms 745.38KB 6.69 99 1 149.52ms
#> 4 fio 500 692.57µs 745.83µs 1.91MB 0 100 0 76.71ms
#> 5 Base R 500 1ms 1.47ms 1.92MB 0 100 0 140.15ms
#> 6 leontief 500 31.64ms 31.98ms 17.31MB 1.28 96 4 3.13s
#> 7 fio 1000 3.15ms 3.27ms 7.64MB 0 100 0 331.91ms
#> 8 Base R 1000 4.09ms 4.26ms 7.66MB 4.75 98 2 421.33ms
#> 9 leontief 1000 173.77ms 175.64ms 68.94MB 1.17 83 17 14.59s
#> 10 fio 2000 12.66ms 14.83ms 30.53MB 0 100 0 1.45s
#> 11 Base R 2000 17.17ms 23.86ms 30.58MB 7.53 86 14 1.86s
#> 12 leontief 2000 861.23ms 865ms 275.22MB 19.5 9 152 7.79s
# plot
ggplot2::autoplot(benchmark_c)
{fio} is substantially faster than {leontief} across all tested dimensions. Compared to Base R, {fio} is faster for matrices 1000x1000 and larger.
Field of influence
Since computing the field of influence involves calculating the Leontief inverse matrix for each element of the technical coefficients matrix after an increment, it can be demanding for high-dimensional matrices. Here, we benchmark the base R function and fio, as there is no similar function in leontief. For brevity, we limited the matrix dimensions to 100x100 and the number of repetitions to 10.
Results shows that {fio} is faster than the base R implementation.
# base R function
field_influence_r <- function(A, B, ee = 0.001) {
n = nrow(A)
I = diag(n)
E = matrix(0, ncol = n, nrow = n)
SI = matrix(0, ncol = n, nrow = n)
for (i in 1:n) {
for (j in 1:n) {
E[i, j] = ee
AE = A + E
BE = solve(I - AE)
FE = (BE - B) / ee
FEq = FE * FE
S = sum(FEq)
SI[i, j] = S
E[i, j] = 0
}
}
return(SI) # Added return statement
}
# benchmark
benchmark_d <- bench::press(
matrix_dim = c(30, 60, 100, 200),
{
intermediate_transactions <- matrix(
as.double(sample(1:1000, matrix_dim^2, replace = TRUE)),
nrow = matrix_dim,
ncol = matrix_dim
)
total_production <- matrix(
as.double(sample(4000000:6000000, matrix_dim, replace = TRUE)),
nrow = 1,
ncol = matrix_dim
)
iom_fio_reduced <- fio::iom$new(
"iom_reduced",
intermediate_transactions,
total_production
)$compute_tech_coeff()$compute_leontief_inverse()
bench::mark(
fio = fio:::compute_field_influence(
iom_fio_reduced$technical_coefficients_matrix,
iom_fio_reduced$leontief_inverse_matrix,
0.001
),
`Base R` = field_influence_r(
iom_fio_reduced$technical_coefficients_matrix,
iom_fio_reduced$leontief_inverse_matrix
),
iterations = 20,
check = FALSE
)
}
)
#> Running with:
#> matrix_dim
#> 1 30
#> 2 60
#> Warning: Some expressions had a GC in every iteration; so filtering is disabled.
#> 3 100
#> Warning: Some expressions had a GC in every iteration; so filtering is disabled.
#> 4 200
#> Warning: Some expressions had a GC in every iteration; so filtering is disabled.
print(benchmark_d)
#> # A tibble: 8 × 9
#> expression matrix_dim min median mem_alloc `gc/sec` n_itr n_gc total_time
#> <bch:expr> <dbl> <bch:tm> <bch:tm> <bch:byt> <dbl> <int> <dbl> <bch:tm>
#> 1 fio 30 11.32ms 11.55ms 16.66KB 0 20 0 231.88ms
#> 2 Base R 30 40.07ms 40.5ms 44.52MB 4.34 17 3 690.66ms
#> 3 fio 60 189.83ms 190.81ms 56.34KB 0 20 0 3.82s
#> 4 Base R 60 403.25ms 404.8ms 701.2MB 5.04 20 41 8.13s
#> 5 fio 100 2.24s 2.31s 156.34KB 0 20 0 46.6s
#> 6 Base R 100 3.04s 3.08s 5.25GB 5.62 20 348 1.03m
#> 7 fio 200 39.98s 40.43s 625.09KB 0 20 0 13.49m
#> 8 Base R 200 46.14s 46.6s 83.73GB 9.23 20 9050 16.35m
ggplot2::autoplot(benchmark_d)
{fio} is faster than the Base R implementation.
Or equivalently, multiplying the intermediate transactions matrix by a diagonal matrix constructed from the total production vector.↩︎