Julia Performance Optimization
Techniques for writing high-performance Julia code.
Type Stability
# Bad - returns different types
function unstable(x)
if x > 0
return x
else
return "negative"
end
end
# Good - always returns same type
function stable(x)
if x > 0
return x
else
return -x
end
end
Pre-Allocation
# Bad - grows array
function slow(n)
result = []
for i in 1:n
push!(result, i^2)
end
result
end
# Good - pre-allocated
function fast(n)
result = Vector{Int}(undef, n)
for i in 1:n
result[i] = i^2
end
result
end
Benchmarking
using BenchmarkTools
@btime sum($(rand(1000))) # Measure time and memory
Loop Fusion
# Bad - multiple loops
result = sin.(x) .+ cos.(x)
# Better - fused operation
result = @. sin(x) + cos(x)
Multithreading
using Base.Threads
function threaded_sum(arr)
total = 0.0
@threads for x in arr
total += x
end
total
end
GPU Computing
using CUDA
# Move data to GPU
x_gpu = cu(rand(1000))
# GPU-accelerated operations
y_gpu = sin.(x_gpu)