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)

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