Datarray

Data oriented design made easy.

Datarray is a struct of arrays data structure that can be used just like an object oriented-style array of structs.

import datarray

type
  Ant = object
    name: string
    color: string
    isWarrior: bool
    age: int32

var antColony: Datarray[1000, Ant]

# multiple styles of data access:
var numberOfWarriors = 0

# a) use the fields directly
for i in 0..<antColony.len:
  antColony.ith(i, name) = "joe"
  if antColony.ith(i, isWarrior):
    inc numberOfWarriors

# b.1) use Element[T] as a proxy for easier access
reset numberOfWarriors
for ant in antColony:
  ant{name} = "joe"
  if ant{isWarrior}:
    inc numberOfWarriors

# b.2) use Element[T] with dot operators
reset numberOfWarriors
for ant in antColony:
  ant.name = "joe"
  if ant.isWarrior:
    inc numberOfWarriors


for ant in antColony:
  ant.name = sample ["joe", "josh", "dave"]
  ant.color = sample ["red", "green", "blue"]
  ant.isWarrior = rand(1.0) < 0.5
  ant.age = int32 rand(1 .. 3)


# it is also possible to select specific fields from the datarray.
# select() is essentially just syntax sugar over ith()

var numChosen = 0
for i, (age, color) in select antColony:
  if age > 1 and color == "red":
    inc numChosen
echo numChosen

In all of these examples, datarray does some memory magic to turn Ant into the following:

type
  AntColony = object
    names: array[1000, string]
    colors: array[1000, string]
    isWarrior: array[1000, bool]
    age: array[1000, int32]

while still retaining easy, OOP-like field access using the dot operator.

Benchmarks

nim r --passC:-march=native --passC:-flto -d:danger --exceptions:goto tests/benchmark.nim

Running on an AMD Ryzen 5 1600.

name ............................... min time      avg time    std dv   runs
array of ref Ant ................... 2.450 ms      2.530 ms    ±0.044  x1000
array of Ant ....................... 1.292 ms      1.312 ms    ±0.009  x1000
AntColony .......................... 0.594 ms      0.596 ms    ±0.002  x1000
Datarray ith() ..................... 0.362 ms      0.396 ms    ±0.004  x1000
Datarray Element[T] ................ 0.594 ms      0.595 ms    ±0.001  x1000

For some reason, not using LTO and goto-based exceptions tanks the performance of Element[T], but I'm yet to discover the reason behind this (or potentially a better solution that doesn't have such high performance overhead). Thus, try to avoid Element[T] in performance-critical code, and use ith and select instead of it.

liquidev/datarray

Datarray

Benchmarks