Performance Tips

Optimizing performance in FastInterpolations.jl boils down to three key areas: vector handling, memory allocation, and grid selection.

1. Vector Queries: Avoid Loops

When querying multiple points (xq is a vector), never loop over scalar queries.

Bad:

results = zeros(length(xq))
for i in eachindex(xq)
   results[i] = linear_interp(x, y, xq[i]) # High overhead per call
end

Good (Vector API):

results = linear_interp(x, y, xq) # Optimized batch processing

The vector API handles grid search and overhead much more efficiently internally.

2. Zero-Allocation: Pre-allocate & In-place

To achieve true zero-allocation, combine the Vector API with pre-allocated output arrays using the ! (bang) versions of the functions.

Good (Standard Vector API):

# Allocates a new array for 'results' every time
results = linear_interp(x, y, xq) 

Best (Zero-Allocation):

# 1. Pre-allocate output array (once, outside loop)
out = similar(xq)

# 2. Use in-place version inside loop
linear_interp!(out, x, y, xq)

Use this pattern in hot loops to eliminate garbage collection overhead.

3. Grid Selection: Range vs Vector

The type of your grid x significantly affects lookup speed.

Recommendation: Always use Range objects for uniform grids. Avoid collect() which degrades performance by converting efficiently searchable Ranges into generic Vectors.

# ❌ Bad: Converts to Vector (O(log n) lookup)
x = collect(0.0:0.1:10.0)

# ✅ Good: Keeps as Range (O(1) lookup)
x = 0.0:0.1:10.0

For deeper technical details (grid types, cache optimization, thread safety), see Advanced Optimization.