R Package	Classic alpha/beta	Phylogenetic alpha/beta
abdiv	*Serial R*	none
adiv	*Serial R*	*Serial R*
ampvis2	vegan	*Serial R*
ecodist	*Serial C/R*	none
ecodive	*Parallel C*	*Parallel C*
entropart	*Serial R*	none
GUniFrac	none	*Serial C*
labdsv	*Serial FORTRAN*	none
parallelDist	none	*Parallel C++*
philentropy	*Serial C++*	none
phyloregion	vegan	*Serial R*
phyloseq	vegan	*Parallel R*
picante	vegan	*Serial R*
tabula	*Serial R*	none
vegan	*Serial C*	none

Setup

Two standard datasets from the rbiom R package, hmp50 and gems, were selected for this evaluation. The hmp50 dataset, which includes 50 samples and an associated phylogenetic tree, was used to benchmark the computationally intensive phylogenetic metrics, such as UniFrac and Faith’s PD. For the traditional diversity metrics, which are significantly less demanding, the larger gems dataset, comprising 1,006 samples, was employed.

To account for the heterogeneous input and output formats across the 15 R packages, necessary data transformations were performed. To ensure that the benchmarks exclusively measured the performance of the diversity calculations, these data conversion steps were executed outside of the timed code blocks whenever possible.

Click to reveal R code.

install.packages('pak')

# Tools and Datasets for Benchmarking Report
pak::pkg_install(pkg = c(
  'bench', 'dplyr', 'ggplot2', 'ggrepel', 'rbiom', 'svglite' ))

# Diversity Metric Implementations
pak::pkg_install(pkg = c(
  'abdiv', 'adiv', 'ecodist', 'ecodive', 'entropart', 'GUniFrac', 
  'kasperskytte/ampvis2', 'labdsv', 'parallelDist', 'philentropy', 
  'phyloregion', 'phyloseq', 'picante', 'tabula', 'vegan' ))


# Software Versions

version$version.string
#> [1] "R version 4.5.1 (2025-06-13 ucrt)"

data.frame(ver = sapply(FUN = packageDescription, fields = 'Version', c(
  'bench', 'dplyr', 'ggplot2', 'ggrepel', 'rbiom',
  'abdiv', 'adiv', 'ecodist', 'ecodive', 'entropart', 'GUniFrac', 
  'ampvis2', 'labdsv', 'parallelDist', 'philentropy', 
  'phyloregion', 'phyloseq', 'picante', 'tabula', 'vegan' )))
#>                     ver
#> bench             1.1.4
#> dplyr             1.1.4
#> ggplot2           3.5.2
#> ggrepel           0.9.6
#> rbiom             2.2.1
#> abdiv             0.2.0
#> adiv              2.2.1
#> ecodist           2.1.3
#> ecodive           2.0.0
#> entropart        1.6-16
#> GUniFrac            1.8
#> ampvis2           2.8.9
#> labdsv            2.1-2
#> parallelDist      0.2.6
#> philentropy       0.9.0
#> phyloregion       1.0.9
#> phyloseq         1.52.0
#> picante           1.8.2
#> tabula            3.3.1
#> vegan             2.7-1

library(bench)
library(ggplot2)
library(ggrepel)
library(dplyr)

(n_cpus <- ecodive::n_cpus())
#> [1] 6

# abdiv only accepts two samples at a time
pairwise <- function (f, data, ...) {
  pairs <- utils::combn(nrow(data), 2)
  structure(
    mapply(
      FUN = function (i, j) f(data[i,], data[j,], ...), 
      i   = pairs[1,], j = pairs[2,] ),
    class  = 'dist',
    Labels = rownames(data),
    Size   = nrow(data),
    Diag   = FALSE,
    Upper  = FALSE )
}


# Remove any extraneous attributes from dist objects,
# allowing them to be compared with `all.equal()`.
cleanup <- function (x) {
  for (i in setdiff(names(attributes(x)), c('class', 'Labels', 'Size', 'Diag', 'Upper')))
    attr(x, i) <- NULL
  return (x)
}


# HMP50 dataset has 50 Samples
hmp50      <- rbiom::hmp50
hmp50_phy  <- rbiom::convert_to_phyloseq(hmp50)
hmp50_mtx  <- t(rbiom::rescale_cols(as.matrix(hmp50)))
hmp50_tree <- hmp50$tree


# GEMS dataset has 1006 Samples
gems_mtx <- t(rbiom::rescale_cols(as.matrix(rbiom::gems)))


## Bray-Curtis Dissimilarity
bray_curtis_res <- bench::mark(
  iterations = 10,
  'abdiv'        = cleanup(pairwise(abdiv::bray_curtis, gems_mtx)),
  'ecodist'      = cleanup(ecodist::bcdist(gems_mtx)),
  'ecodive'      = cleanup(ecodive::bray(gems_mtx, rescale = FALSE)),
  'labdsv'       = cleanup(labdsv::dsvdis(gems_mtx, 'bray/curtis')),
  'parallelDist' = cleanup(parallelDist::parallelDist(gems_mtx, 'bray')),
  'philentropy'  = cleanup(philentropy::distance(gems_mtx, 'sorensen', test.na = FALSE, use.row.names = TRUE, as.dist.obj = TRUE, mute.message = TRUE)),
  'tabula'       = cleanup(1 - pairwise(tabula::index_bray, gems_mtx)),
  'vegan'        = cleanup(vegan::vegdist(gems_mtx, 'bray')) )


## Jaccard Distance
jaccard_res <- bench::mark(
  iterations = 10,
  'abdiv'        = cleanup(pairwise(abdiv::jaccard, gems_mtx)),
  'ecodist'      = cleanup(ecodist::distance(gems_mtx, 'jaccard')),
  'ecodive'      = cleanup(ecodive::jaccard(gems_mtx)),
  'parallelDist' = cleanup(parallelDist::parallelDist(gems_mtx, 'binary')),
  'philentropy'  = cleanup(philentropy::distance(gems_mtx > 0, 'jaccard', test.na = FALSE, use.row.names = TRUE, as.dist.obj = TRUE, mute.message = TRUE)),
  'phyloregion'  = cleanup(phyloregion::beta_diss(gems_mtx, 'jaccard')$beta.jac),
  'stats'        = cleanup(stats::dist(gems_mtx, 'binary')),
  'vegan'        = cleanup(vegan::vegdist(gems_mtx, 'jaccard', binary = TRUE)) )


## Manhattan Distance
manhattan_res <- bench::mark(
  iterations = 10,
  'abdiv'        = cleanup(pairwise(abdiv::manhattan, gems_mtx)),
  'ecodist'      = cleanup(ecodist::distance(gems_mtx, 'manhattan')),
  'ecodive'      = cleanup(ecodive::manhattan(gems_mtx, rescale = FALSE)),
  'parallelDist' = cleanup(parallelDist::parallelDist(gems_mtx, 'manhattan')),
  'philentropy'  = cleanup(philentropy::distance(gems_mtx, 'manhattan', test.na = FALSE, use.row.names = TRUE, as.dist.obj = TRUE, mute.message = TRUE)),
  'stats'        = cleanup(stats::dist(gems_mtx, 'manhattan')),
  'vegan'        = cleanup(vegan::vegdist(gems_mtx, 'manhattan')) )


## Euclidean Distance
euclidean_res <- bench::mark(
  iterations = 10,
  'abdiv'        = cleanup(pairwise(abdiv::euclidean, gems_mtx)),
  'ecodist'      = cleanup(ecodist::distance(gems_mtx, 'euclidean')),
  'ecodive'      = cleanup(ecodive::euclidean(gems_mtx, rescale = FALSE)),
  'parallelDist' = cleanup(parallelDist::parallelDist(gems_mtx, 'euclidean')),
  'philentropy'  = cleanup(philentropy::distance(gems_mtx, 'euclidean', test.na = FALSE, use.row.names = TRUE, as.dist.obj = TRUE, mute.message = TRUE)),
  'stats'        = cleanup(stats::dist(gems_mtx, 'euclidean')),
  'vegan'        = cleanup(vegan::vegdist(gems_mtx, 'euclidean')) )


## Shannon Diversity Index
shannon_res <- bench::mark(
  iterations  = 10,
  'abdiv'       = apply(gems_mtx, 1L, abdiv::shannon),
  'adiv'        = adiv::speciesdiv(gems_mtx, 'Shannon')[,1],
  'ecodive'     = ecodive::shannon(gems_mtx),
  'entropart'   = apply(gems_mtx, 1L, entropart::Shannon, CheckArguments = FALSE),
  'philentropy' = apply(gems_mtx, 1L, philentropy::H, unit = 'log'),
  'tabula'      = apply(gems_mtx, 1L, tabula::index_shannon),
  'vegan'       = vegan::diversity(gems_mtx, 'shannon') )


## Gini-Simpson Index
simpson_res <- bench::mark(
  iterations  = 10,
  'abdiv'     = apply(gems_mtx, 1L, abdiv::simpson),
  'adiv'      = adiv::speciesdiv(gems_mtx, 'GiniSimpson')[,1],
  'ecodive'   = ecodive::simpson(gems_mtx),
  'entropart' = apply(gems_mtx, 1L, entropart::Simpson, CheckArguments = FALSE),
  'tabula'    = 1 - apply(gems_mtx, 1L, tabula::index_simpson),
  'vegan'     = vegan::diversity(gems_mtx, 'simpson') )


## Faith's Phylogenetic Diversity
faith_res <- bench::mark(
  iterations    = 10,
  check         = FALSE, # entropart has incorrect output on non-ultrametric tree
  'abdiv'       = apply(hmp50_mtx, 1L, abdiv::faith_pd, hmp50_tree),
  'adiv'        = apply(hmp50_mtx, 1L, \(x) adiv::EH(hmp50_tree, colnames(hmp50_mtx)[x > 0])),
  'ecodive'     = ecodive::faith(hmp50_mtx, hmp50_tree),
  'entropart'   = apply(hmp50_mtx, 1L, entropart::PDFD, hmp50_tree, CheckArguments = FALSE),
  'phyloregion' = phyloregion::PD(hmp50_mtx, hmp50_tree),
  'picante'     = as.matrix(picante::pd(hmp50_mtx, hmp50_tree))[,'PD'] )
  

## Unweighted UniFrac
u_unifrac_res <- rbind(
  
  local({
    # cluster for phyloseq
    cl <- parallel::makeCluster(n_cpus)
    doParallel::registerDoParallel(cl)
    on.exit(parallel::stopCluster(cl))
    
    bench::mark(
      iterations    = 10,
      'abdiv'       = cleanup(pairwise(abdiv::unweighted_unifrac, hmp50_mtx, hmp50_tree)),
      'ecodive'     = cleanup(ecodive::unweighted_unifrac(hmp50_mtx, hmp50_tree)),
      'GUniFrac'    = cleanup(as.dist(GUniFrac::GUniFrac(hmp50_mtx, hmp50_tree, alpha=1, verbose=FALSE)[[1]][,,2])),
      'phyloregion' = cleanup(phyloregion::unifrac(hmp50_mtx, hmp50_tree)),
      'phyloseq'    = cleanup(phyloseq::UniFrac(hmp50_phy, weighted=FALSE, normalized=FALSE, parallel=TRUE)),
      'picante'     = cleanup(picante::unifrac(hmp50_mtx, hmp50_tree)) )
  }),
  
  # ampvis2 conflicts with phyloseq cluster, so run separately
  local({
    t_hmp50_mtx <- t(hmp50_mtx)
    bench::mark(
      iterations = 10,
      'ampvis2'  = {
        cleanup(ampvis2:::dist.unifrac(t_hmp50_mtx, hmp50_tree, weighted=FALSE, normalise=FALSE, num_threads=n_cpus))
        doParallel::stopImplicitCluster() } )
  })
)
  
  
## Weighted UniFrac
w_unifrac_res <- rbind(
  
  local({
    # cluster for phyloseq
    cl <- parallel::makeCluster(n_cpus)
    doParallel::registerDoParallel(cl)
    on.exit(parallel::stopCluster(cl))
    
    bench::mark(
      iterations = 10,
      'abdiv'    = cleanup(pairwise(abdiv::weighted_unifrac, hmp50_mtx, hmp50_tree)),
      'ecodive'  = cleanup(ecodive::weighted_unifrac(hmp50_mtx, hmp50_tree)),
      'phyloseq' = cleanup(phyloseq::UniFrac(hmp50_phy, weighted=TRUE, normalized=FALSE, parallel=TRUE)) )
  }),
  
  # ampvis2 conflicts with phyloseq cluster, so run separately
  local({
    t_hmp50_mtx <- t(hmp50_mtx)
    bench::mark(
      iterations = 10,
      'ampvis2'  = {
        cleanup(ampvis2:::dist.unifrac(t_hmp50_mtx, hmp50_tree, weighted=TRUE, normalise=FALSE, num_threads=n_cpus))
        doParallel::stopImplicitCluster() } )
  })
)


## Weighted Normalized UniFrac
wn_unifrac_res <- rbind(
  
  local({
    # cluster for phyloseq
    cl <- parallel::makeCluster(n_cpus)
    doParallel::registerDoParallel(cl)
    on.exit(parallel::stopCluster(cl))
    
    bench::mark(
      iterations = 10,
      'abdiv'    = cleanup(pairwise(abdiv::weighted_normalized_unifrac, hmp50_mtx, hmp50_tree)),
      'ecodive'  = cleanup(ecodive::normalized_unifrac(hmp50_mtx, hmp50_tree)),
      'GUniFrac' = cleanup(as.dist(GUniFrac::GUniFrac(hmp50_mtx, hmp50_tree, alpha=1, verbose=FALSE)[[1]][,,1])),
      'phyloseq' = cleanup(phyloseq::UniFrac(hmp50_phy, weighted=TRUE, normalized=TRUE, parallel=TRUE)) )
  }),
  
  # ampvis2 conflicts with phyloseq cluster, so run separately
  local({
    t_hmp50_mtx <- t(hmp50_mtx)
    bench::mark(
      iterations = 10,
      'ampvis2'  = {
        cleanup(ampvis2:::dist.unifrac(t_hmp50_mtx, hmp50_tree, weighted=TRUE, normalise=TRUE, num_threads=n_cpus))
        doParallel::stopImplicitCluster() } )
  })
)


## Weighted Normalized UniFrac
g_unifrac_res <- rbind(
  
  local({
    # cluster for phyloseq
    cl <- parallel::makeCluster(n_cpus)
    doParallel::registerDoParallel(cl)
    on.exit(parallel::stopCluster(cl))
    
    bench::mark(
      iterations = 10,
      'abdiv'    = cleanup(pairwise(abdiv::generalized_unifrac, hmp50_mtx, hmp50_tree, alpha=0.5)),
      'ecodive'  = cleanup(ecodive::generalized_unifrac(hmp50_mtx, hmp50_tree, alpha=0.5)),
      'GUniFrac' = cleanup(as.dist(GUniFrac::GUniFrac(hmp50_mtx, hmp50_tree, alpha=0.5, verbose=FALSE)[[1]][,,1])) )
  })
)


## Variance Adjusted UniFrac
va_unifrac_res <- rbind(
  
  local({
    # cluster for phyloseq
    cl <- parallel::makeCluster(n_cpus)
    doParallel::registerDoParallel(cl)
    on.exit(parallel::stopCluster(cl))
    
    bench::mark(
      iterations = 10,
      'abdiv'    = cleanup(pairwise(abdiv::variance_adjusted_unifrac, hmp50_mtx, hmp50_tree)),
      'ecodive'  = cleanup(ecodive::variance_adjusted_unifrac(hmp50_mtx, hmp50_tree)) )
  })
)
  

## Data frame for Figure 1A
Fig1A_data <- bind_rows(
    mutate(bray_curtis_res, Metric = 'bray'),
    mutate(shannon_res,     Metric = 'shannon'),
    mutate(faith_res,       Metric = 'faith') ) %>%
  mutate(Package = as.character(expression)) %>%
  select(Package, Metric, median, mem_alloc) %>%
  arrange(Metric, median)

Fig1A_data$Title <- factor(
  x      = Fig1A_data$Metric, 
  levels = c("shannon", "bray", "faith"), 
  labels = c(
    "**Shannon Diversity Index**<br>1006 samples", 
    "**Bray-Curtis Dissimilarity**<br>1006 samples", 
    "**Faith's Phylogenetic Diversity**<br>50 samples") )


## ggplot for Figure 1A
Fig1A <- ggplot(Fig1A_data, aes(x = median, y = mem_alloc)) +
    facet_wrap('Title', nrow = 1, scales = 'free_x') + 
    geom_point() +
    geom_label_repel(aes(
        label    = Package,
        alpha    = ifelse(Package == 'ecodive', 1, 0.6),
        fontface = ifelse(Package == 'ecodive', 2, 1),
        size     = ifelse(Package == 'ecodive', 3.2, 3) ), 
      box.padding        = .4,
      point.padding      = .5,
      min.segment.length = 0 ) + 
    scale_alpha_identity(guide = 'none') +
    scale_size_identity(guide = 'none') +
    bench::scale_x_bench_time() +
    scale_y_log10(labels = scales::label_bytes()) +
    labs(
      tag = 'A',
      x   = 'Median Calculation Time (log scale; n=10)', 
      y   = 'Memory Allocated\n(log scale; n=1)' ) +
    theme_bw(base_size = 12) +
    theme(
      strip.text   = ggtext::element_markdown(hjust = 0, lineheight = 1.2),
      axis.title.x = element_text(margin = margin(t = 10)))
      

## Data frame for Figure 1B
Fig1B_data <- bind_rows(
    mutate(u_unifrac_res,  `UniFrac Variant` = 'Unweighted'),
    mutate(w_unifrac_res,  `UniFrac Variant` = 'Weighted'),
    mutate(wn_unifrac_res, `UniFrac Variant` = 'Weighted Normalized'),
    mutate(g_unifrac_res,  `UniFrac Variant` = 'Generalized'),
    mutate(va_unifrac_res, `UniFrac Variant` = 'Variance Adjusted') ) %>%
  mutate(Package = as.character(expression)) %>%
  select(Package, `UniFrac Variant`, median, mem_alloc) %>%
  arrange(Package)

Fig1B_data$Title <- "**UniFrac Family**<br>50 samples"
Fig1B_data$`UniFrac Variant` <- factor(
  x      = Fig1B_data$`UniFrac Variant`, 
  levels = c("Unweighted", "Weighted", "Weighted Normalized", "Generalized", "Variance Adjusted") )


## ggplot for Figure 1B
Fig1B <- ggplot(Fig1B_data, aes(x = median, y = mem_alloc)) +
  facet_wrap('Title') + 
  geom_point(aes(shape = `UniFrac Variant`), size = 2) +
  geom_label_repel(aes(
      label    = Package, 
      alpha    = ifelse(Package == 'ecodive', 1, 0.6),
      fontface = ifelse(Package == 'ecodive', 2, 1),
      size     = ifelse(Package == 'ecodive', 3.2, 3) ),
    data          = ~subset(., `UniFrac Variant` == 'Unweighted'),
    direction     = 'y', 
    box.padding   = 0.4,
    point.padding = 10,
    min.segment.length = Inf ) + 
  scale_shape(solid = FALSE) + 
  scale_alpha_identity(guide = 'none') +
  scale_size_identity(guide = 'none') +
  bench::scale_x_bench_time() +
  scale_y_log10(labels = scales::label_bytes()) +
  labs(
    tag = 'B',
    x   = 'Median Calculation Time (log scale; n=10)', 
    y   = 'Memory Allocated\n(log scale; n=1)' ) +
  theme_bw(base_size = 12) +
  theme(
    strip.text   = ggtext::element_markdown(hjust = 0, lineheight = 1.2),
    axis.title.x = element_text(margin = margin(t = 10)) )


## Combined Figure 1
Fig1 <- patchwork::wrap_plots(
  Fig1A, 
  Fig1B, 
  patchwork::guide_area(), 
  design = "111\n223", 
  guides = 'collect' )

Results

print(Fig1)

How much faster and more memory efficient is ecodive?

plyr::ddply(Fig1A_data, as.name('Metric'), function (x) {
  x[['median']]    <- as.numeric(x[['median']])
  x[['mem_alloc']] <- as.numeric(x[['mem_alloc']])
  ecodive <- as.list(x[x[['Package']] == 'ecodive',])
  x       <- x[x[['Package']] != 'ecodive',]
  data.frame(
    speed  = paste0(paste(collapse=' - ', round(range(x$median    / ecodive$median))), 'x'),
    memory = paste0(paste(collapse=' - ', round(range(x$mem_alloc / ecodive$mem_alloc))), 'x') )
})
#>    Metric      speed       memory
#> 1    bray   1 - 166x    2 - 5312x
#> 2   faith 1 - 10413x 212 - 74389x
#> 3 shannon   2 - 113x      6 - 22x


plyr::ddply(Fig1B_data, as.name('UniFrac Variant'), function (x) {
  x[['median']]    <- as.numeric(x[['median']])
  x[['mem_alloc']] <- as.numeric(x[['mem_alloc']])
  ecodive <- as.list(x[x[['Package']] == 'ecodive',])
  x       <- x[x[['Package']] != 'ecodive',]
  data.frame(
    speed  = paste0(paste(collapse=' - ', round(range(x$median    / ecodive$median))), 'x'),
    memory = paste0(paste(collapse=' - ', round(range(x$mem_alloc / ecodive$mem_alloc))), 'x') )
})
#>       UniFrac Variant        speed         memory
#> 1          Unweighted    1 - 2251x    98 - 39485x
#> 2            Weighted   44 - 2094x   178 - 74013x
#> 3 Weighted Normalized   11 - 1991x   178 - 74049x
#> 4         Generalized   10 - 1814x   310 - 69563x
#> 5   Variance Adjusted 2353 - 2353x 90421 - 90421x

Raw Data for Figure 1 Panel A

print(Fig1A_data[,1:4], n = Inf)
#> # A tibble: 21 × 4
#>    Package      Metric    median mem_alloc
#>    <chr>        <chr>   <bch:tm> <bch:byt>
#>  1 ecodive      bray    149.79ms    3.94MB
#>  2 parallelDist bray    187.72ms    7.88MB
#>  3 ecodist      bray    398.33ms   29.43MB
#>  4 labdsv       bray    726.73ms   68.16MB
#>  5 vegan        bray       1.69s   16.45MB
#>  6 philentropy  bray      10.31s   14.66GB
#>  7 abdiv        bray      13.75s   20.43GB
#>  8 tabula       bray      24.89s   20.43GB
#>  9 ecodive      faith     2.62ms  337.58KB
#> 10 phyloregion  faith     3.08ms  141.47MB
#> 11 picante      faith       77ms   69.86MB
#> 12 abdiv        faith   476.24ms  651.64MB
#> 13 adiv         faith      1.72s  489.05MB
#> 14 entropart    faith     27.27s   23.95GB
#> 15 ecodive      shannon  11.16ms    6.06MB
#> 16 tabula       shannon  25.34ms    36.6MB
#> 17 entropart    shannon  40.73ms   41.77MB
#> 18 adiv         shannon   46.8ms  131.49MB
#> 19 vegan        shannon  68.58ms   68.08MB
#> 20 abdiv        shannon  85.22ms   95.41MB
#> 21 philentropy  shannon    1.26s   52.78MB

Raw Data for Figure 1 Panel B

print(Fig1B_data[,1:4], n = Inf)
#> # A tibble: 21 × 4
#>    Package     `UniFrac Variant`     median mem_alloc
#>    <chr>       <fct>               <bch:tm> <bch:byt>
#>  1 GUniFrac    Unweighted           86.15ms   113.4MB
#>  2 GUniFrac    Weighted Normalized  78.51ms    92.1MB
#>  3 GUniFrac    Generalized           75.7ms    92.1MB
#>  4 abdiv       Unweighted               15s    20.1GB
#>  5 abdiv       Weighted              13.67s      20GB
#>  6 abdiv       Weighted Normalized   13.78s      20GB
#>  7 abdiv       Generalized           13.78s    20.2GB
#>  8 abdiv       Variance Adjusted     15.81s    24.5GB
#>  9 ampvis2     Unweighted             3.64s    53.7MB
#> 10 ampvis2     Weighted                3.2s    49.2MB
#> 11 ampvis2     Weighted Normalized    3.33s    49.3MB
#> 12 ecodive     Unweighted            6.66ms   532.6KB
#> 13 ecodive     Weighted              6.53ms   283.7KB
#> 14 ecodive     Weighted Normalized   6.92ms   283.7KB
#> 15 ecodive     Generalized           7.59ms   304.3KB
#> 16 ecodive     Variance Adjusted     6.72ms   283.7KB
#> 17 phyloregion Unweighted             7.1ms   145.9MB
#> 18 phyloseq    Unweighted          325.11ms    50.9MB
#> 19 phyloseq    Weighted            288.33ms    49.4MB
#> 20 phyloseq    Weighted Normalized 296.54ms    49.5MB
#> 21 picante     Unweighted             2.56s     1.8GB

Benchmarks

Introduction

State of the Field

Methodology

Setup

Results

How much faster and more memory efficient is ecodive?

Raw Data for Figure 1 Panel A

Raw Data for Figure 1 Panel B