README.md

In quickr: Compiler for R

quickr

The goal of quickr is to make your R code run quicker.

Overview

R is an extremely flexible and dynamic language, but that flexibility and dynamicism can come at the expense of speed. This package lets you trade back some of that flexibility for some speed, for the context of a single function.

The main exported function is quick(), here is how you use it.

library(quickr)

convolve <- quick(function(a, b) {
  declare(type(a = double(NA)),
          type(b = double(NA)))
  ab <- double(length(a) + length(b) - 1)
  for (i in seq_along(a)) {
    for (j in seq_along(b)) {
      ab[i+j-1] <- ab[i+j-1] + a[i] * b[j]
    }
  }
  ab
})

quick() returns a quicker R function. How much quicker? Let’s benchmark it! For reference, we’ll also compare it to a pure-C implementation.

slow_convolve <- function(a, b) {
  declare(type(a = double(NA)),
          type(b = double(NA)))
  ab <- double(length(a) + length(b) - 1)
  for (i in seq_along(a)) {
    for (j in seq_along(b)) {
      ab[i+j-1] <- ab[i+j-1] + a[i] * b[j]
    }
  }
  ab
}

library(quickr)
quick_convolve <- quick(slow_convolve)

convolve_c <- inline::cfunction(
  sig = c(a = "SEXP", b = "SEXP"), body = r"({
    int na, nb, nab;
    double *xa, *xb, *xab;
    SEXP ab;

    a = PROTECT(Rf_coerceVector(a, REALSXP));
    b = PROTECT(Rf_coerceVector(b, REALSXP));
    na = Rf_length(a); nb = Rf_length(b); nab = na + nb - 1;
    ab = PROTECT(Rf_allocVector(REALSXP, nab));
    xa = REAL(a); xb = REAL(b); xab = REAL(ab);
    for(int i = 0; i < nab; i++) xab[i] = 0.0;
    for(int i = 0; i < na; i++)
        for(int j = 0; j < nb; j++)
            xab[i + j] += xa[i] * xb[j];
    UNPROTECT(3);
    return ab;
})")



a <- runif (100000); b <- runif (100)

timings <- bench::mark(
  r = slow_convolve(a, b),
  quickr = quick_convolve(a, b),
  c = convolve_c(a, b),
  min_time = 2
)
timings
#> # A tibble: 3 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 r             1.04s    1.04s     0.957     782KB    0.957
#> 2 quickr       4.85ms   5.03ms   198.        782KB    3.61 
#> 3 c            4.88ms   5.06ms   197.        782KB    4.13
plot(timings) + bench::scale_x_bench_time(base = NULL)

In the case of convolve(), quick() returns a function approximately 200 times quicker, giving similar performance to the C function.

quick() can accelerate any R function, with some restrictions:

• Function arguments must have their types and shapes declared using declare().
• Only atomic vectors, matrices, and array are currently supported: integer, double, logical, and complex.
• The return value must be an atomic array (e.g., not a list)
• Named variables must have consistent shapes throughout their lifetimes.
• NA values are not supported.
• Only a subset of R’s vocabulary is currently supported.

#>  [1] -         :         !=        (         [         [<-       {        
#>  [8] *         /         &         &&        %/%       %%        ^        
#> [15] +         <         <-        <=        =         ==        >        
#> [22] >=        |         ||        Arg       Conj      Fortran   Im       
#> [29] Mod       Re        abs       acos      as.double asin      atan     
#> [36] break     c         cat       cbind     ceiling   character cos      
#> [43] declare   dim       double    exp       floor     for       if       
#> [50] ifelse    integer   length    log       log10     logical   matrix   
#> [57] max       min       ncol      next      nrow      numeric   print    
#> [64] prod      raw       repeat    runif     seq       sin       sqrt     
#> [71] sum       tan       which.max which.min while

Many of these restrictions are expected to be relaxed as the project matures. However, quickr is intended primarily for high-performance numerical computing, so features like polymorphic dispatch or support for complex or dynamic types are out of scope.

declare(type()) syntax:

The shape and mode of all function arguments must be declared. Local and return variables may optionally also be declared.

declare(type()) also has support for declaring size constraints, or size relationships between variables. Here are some examples of declare calls:

declare(type(x = double(NA))) # x is a 1-d double vector of any length
declare(type(x = double(10))) # x is a 1-d double vector of length 10
declare(type(x = double(1)))  # x is a scalar double

declare(type(x = integer(2, 3)))  # x is a 2-d integer matrix with dim (2, 3)
declare(type(x = integer(NA, 3))) # x is a 2-d integer matrix with dim (<any>, 3)

# x is a 4-d logical matrix with dim (<any>, 24, 24, 3)
declare(type(x = logical(NA, 24, 24, 3)))

# x and y are 1-d double vectors of any length
declare(type(x = double(NA)),
        type(y = double(NA)))

# x and y are 1-d double vectors of the same length
declare(
  type(x = double(n)),
  type(y = double(n)),
)

# x and y are 1-d double vectors, where length(y) == length(x) + 2
declare(type(x = double(n)),
        type(y = double(n+2)))

More examples:

viterbi

The Viterbi algorithm is an example of a dynamic programming algorithm within the family of Hidden Markov Models (https://en.wikipedia.org/wiki/Viterbi_algorithm). Here, quick() makes the viterbi() approximately 50 times faster.

slow_viterbi <- function(observations, states, initial_probs, transition_probs, emission_probs) {
    declare(
      type(observations = integer(num_steps)),
      type(states = integer(num_states)),
      type(initial_probs = double(num_states)),
      type(transition_probs = double(num_states, num_states)),
      type(emission_probs = double(num_states, num_obs)),
    )

    trellis <- matrix(0, nrow = length(states), ncol = length(observations))
    backpointer <- matrix(0L, nrow = length(states), ncol = length(observations))
    trellis[, 1] <- initial_probs * emission_probs[, observations[1]]

    for (step in 2:length(observations)) {
      for (current_state in 1:length(states)) {
        probabilities <- trellis[, step - 1] * transition_probs[, current_state]
        trellis[current_state, step] <- max(probabilities) * emission_probs[current_state, observations[step]]
        backpointer[current_state, step] <- which.max(probabilities)
      }
    }

    path <- integer(length(observations))
    path[length(observations)] <- which.max(trellis[, length(observations)])
    for (step in seq(length(observations) - 1, 1)) {
      path[step] <- backpointer[path[step + 1], step + 1]
    }

    out <- states[path]
    out
}

quick_viterbi <- quick(slow_viterbi)

set.seed(1234)
num_steps <- 16
num_states <- 8
num_obs <- 16

observations <- sample(1:num_obs, num_steps, replace = TRUE)
states <- 1:num_states
initial_probs <- runif (num_states)
initial_probs <- initial_probs / sum(initial_probs)  # normalize to sum to 1
transition_probs <- matrix(runif (num_states * num_states), nrow = num_states)
transition_probs <- transition_probs / rowSums(transition_probs)  # normalize rows
emission_probs <- matrix(runif (num_states * num_obs), nrow = num_states)
emission_probs <- emission_probs / rowSums(emission_probs)  # normalize rows

timings <- bench::mark(
  slow_viterbi = slow_viterbi(observations, states, initial_probs,
                              transition_probs, emission_probs),
  quick_viterbi = quick_viterbi(observations, states, initial_probs,
                                transition_probs, emission_probs)
)
timings
#> # A tibble: 2 × 6
#>   expression         min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>    <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 slow_viterbi  147.12µs 162.33µs     5957.    1.59KB     16.8
#> 2 quick_viterbi   2.48µs   2.62µs   367468.        0B      0
plot(timings)

Diffusion simulation

Simulate how heat spreads over time across a 2D grid, using the finite difference method applied to the Heat Equation.

Here, quick() returns a function over 100 times faster.

diffuse_heat <- function(nx, ny, dx, dy, dt, k, steps) {
  declare(
    type(nx = integer(1)),
    type(ny = integer(1)),
    type(dx = integer(1)),
    type(dy = integer(1)),
    type(dt = double(1)),
    type(k = double(1)),
    type(steps = integer(1))
  )

  # Initialize temperature grid
  temp <- matrix(0, nx, ny)
  temp[nx / 2, ny / 2] <- 100  # Initial heat source in the center

  # Time stepping
  for (step in seq_len(steps)) {
    # Apply boundary conditions
    temp[1, ] <- 0
    temp[nx, ] <- 0
    temp[, 1] <- 0
    temp[, ny] <- 0

    # Update using finite differences
    temp_new <- temp
    for (i in 2:(nx - 1)) {
      for (j in 2:(ny - 1)) {
        temp_new[i, j] <- temp[i, j] + k * dt *
          ((temp[i + 1, j] - 2 * temp[i, j] + temp[i - 1, j]) /
             dx ^ 2 + (temp[i, j + 1] - 2 * temp[i, j] + temp[i, j - 1]) / dy ^ 2)
      }
    }
    temp <- temp_new

  }

  temp
}

quick_diffuse_heat <- quick(diffuse_heat)

# Parameters
nx <- 100L      # Grid size in x
ny <- 100L      # Grid size in y
dx <- 1L        # Grid spacing
dy <- 1L        # Grid spacing
dt <- 0.01      # Time step
k <- 0.1        # Thermal diffusivity
steps <- 500L   # Number of time steps

timings <- bench::mark(
  diffuse_heat = diffuse_heat(nx, ny, dx, dy, dt, k, steps),
  quick_diffuse_heat = quick_diffuse_heat(nx, ny, dx, dy, dt, k, steps)
)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
summary(timings, relative = TRUE)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 2 × 6
#>   expression           min median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>         <dbl>  <dbl>     <dbl>     <dbl>    <dbl>
#> 1 diffuse_heat        131.   127.        1      1011.      Inf
#> 2 quick_diffuse_heat    1      1       127.        1       NaN
plot(timings)

Rolling Mean

Here is quickr used to calculate a rolling mean. Note that the CRAN package RcppRoll already provides a highly optimized rolling mean, which we include in the benchmarks for comparison.

slow_roll_mean <- function(x, weights, normalize = TRUE) {
  declare(
    type(x = double(NA)),
    type(weights = double(NA)),
    type(normalize = logical(1))
  )
  out <- double(length(x) - length(weights) + 1)
  n <- length(weights)
  if (normalize)
    weights <- weights/sum(weights)*length(weights)

  for(i in seq_along(out)) {
    out[i] <- sum(x[i:(i+n-1)] * weights) / length(weights)
  }
  out
}

quick_roll_mean <- quick(slow_roll_mean)

x <- dnorm(seq(-3, 3, len = 100000))
weights <- dnorm(seq(-1, 1, len = 100))

timings <- bench::mark(
  r = slow_roll_mean(x, weights),
  rcpp = RcppRoll::roll_mean(x, weights = weights),
  quickr = quick_roll_mean(x, weights = weights)
)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
timings
#> # A tibble: 3 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 r           114.2ms 132.75ms      5.66  124.24MB     9.44
#> 2 rcpp        19.35ms  19.58ms     50.9     4.44MB     0   
#> 3 quickr       6.73ms   6.87ms    142.    781.35KB     1.98

timings$expression <- factor(names(timings$expression), rev(names(timings$expression)))
plot(timings) + bench::scale_x_bench_time(base = NULL)

Using quickr in an R package

When called in a package, quick() will pre-compile the quick functions and place them in the ./src directory. Run devtools::load_all() or quickr::compile_package() to ensure that the generated files in ./src and ./R are in sync with each other.

In a package, you must provide a function name to quick(). For example:

my_fun <- quick(name = "my_fun", function(x) ....)

Installation

You can install quickr from CRAN with:

install.packages("quickr")

You can install the development version of quickr from GitHub with:

# install.packages("pak")
pak::pak("t-kalinowski/quickr")

You will also need a C and Fortran compiler, preferably the same ones used to build R itself.

On macOS:

• Make sure xcode tools and gfortran are installed, as described in https://mac.r-project.org/tools/. In Terminal, run:

zsh sudo xcode-select --install # curl -LO https://mac.r-project.org/tools/gfortran-12.2-universal.pkg # R 4.4 curl -LO https://mac.r-project.org/tools/gfortran-14.2-universal.pkg # R 4.5 sudo installer -pkg gfortran-12.2-universal.pkg -target /

On Windows:

• Install the latest version of Rtools

On Linux:

• The “Install Required Dependencies” section here provides detailed instructions for installing R build tools on various Linux flavors.

quickr documentation built on Aug. 26, 2025, 1:07 a.m.