How to pass an array of objects to a function in julia? - julia

Question

I want to pass an array of objects to a function and return another array of the same size. How should I do this ? The code should be so fast. For example consider the below code:
struct triangle
height::Float64
base::Float64
end
function area(height::Float64, base::Float64)
return 0.5*base*height
end
I want to define a function which returns the area of an array of triangles.
What is the fastest way to do it?

Answer 1

You can leverage Julia's broadcasting syntax for this. Consider:
struct Triangle # Note: Julia convention is to capitalize types
height::Float64
base::Float64
end
# Define an area method for the Triangle type
area(t::Triangle) = 0.5 * t.height * t.base
# Create an area of random triangles
triangle_array = [Triangle(rand(), rand()) for _ in 1:10]
# Now we can use dot syntax to broadcast our area function over the array of triangles
area.(triangle_array)
Note that this differs from your code in that it directly uses the Triangle object for dispatch in the call to the area function. The area function then doesn't take height and base arguments but just a single Triangle object and accesses its height and base fields (t.height and t.base).

Answer 2

Here are some benchmarks for computing this with:
map: map(area, triangles)
comprehension: `[area(triangle) for triangle in triangles]
broadcasting: area.(triangles)
using interpolation $ on the non-local global variable triangles (based on #DNF comment).
Definitions
using Pkg
Pkg.add("BenchmarkTools")
using BenchmarkTools
struct Triangle
height::Float64
base::Float64
end
function area(t::Triangle)
0.5 * t.height * t.base
end
triangles = [Triangle(rand(), rand()) for _ in 1:1000000]
Results
julia> #benchmark map(area, $triangles)
BenchmarkTools.Trial:
memory estimate: 7.63 MiB
allocs estimate: 3
--------------
minimum time: 1.168 ms (0.00% GC)
median time: 2.510 ms (0.00% GC)
mean time: 2.485 ms (10.00% GC)
maximum time: 43.540 ms (91.62% GC)
--------------
samples: 2008
evals/sample: 1
julia> #benchmark [area(triangle) for triangle in $triangles]
BenchmarkTools.Trial:
memory estimate: 7.63 MiB
allocs estimate: 3
--------------
minimum time: 1.150 ms (0.00% GC)
median time: 1.921 ms (0.00% GC)
mean time: 2.327 ms (10.76% GC)
maximum time: 45.883 ms (91.42% GC)
--------------
samples: 2144
evals/sample: 1
julia> #benchmark area.($triangles)
BenchmarkTools.Trial:
memory estimate: 7.63 MiB
allocs estimate: 2
--------------
minimum time: 1.165 ms (0.00% GC)
median time: 1.224 ms (0.00% GC)
mean time: 1.961 ms (10.13% GC)
maximum time: 44.156 ms (89.33% GC)
--------------
samples: 2544
evals/sample: 1
This would indicate that for this input size, the broadcasting method seems to be the fastest.
For different input size, relative timings may be different, so it is probably a good idea to benchmark it yourself for your use case