MATLAB vs Julia: which is better for performance?

Julia is generally faster for custom, compute-heavy numerical code. In 2017, Celeste.jl, a program written entirely in Julia, reached 1.54 petaflops on 9,300 nodes of the Cori supercomputer, the first Julia program to hit petaflop-scale performance. MATLAB's own JIT-compiled execution engine, introduced in R2015a, keeps built-in matrix and vectorized operations close in speed, but custom loops and large-scale simulations tend to run faster in Julia.

When does Julia perform better?

Julia tends to win once a workload steps outside MATLAB's built-in, pre-optimized functions. Because Julia compiles user-written functions through LLVM rather than relying on a library match, hand-written loops, recursive algorithms, and custom differential equation solvers don't lose speed the way older interpreted code could. Multiple dispatch lets the same function specialize automatically for different data types without manual vectorization tricks, which matters for large agent-based models or numerical methods research where no ready-made toolbox function exists.

When does MATLAB hold up fine?

MATLAB stays competitive whenever a task maps onto its built-in linear algebra, signal processing, or control system functions, since those are already compiled and optimized at the library level. Simulink users get additional speed from parsim, which distributes multiple simulation runs across CPU cores, and gpuArray, which offloads array operations to NVIDIA GPUs without writing CUDA code. For teams already standardized on Simulink-based control or simulation workflows, the performance gap with Julia rarely justifies a rewrite.

Published at: August 2, 2026