Let’s break it down so it’s not just “it depends” hand-waving:
1. Theoretical Performance
- Julia
- Uses LLVM JIT to specialize code for the types at runtime.
- If code is type-stable and written with performance in mind, LLVM can generate machine code as good as, or slightly better than, what C/C++/Nim compilers produce.
- For generic numerical loops and math-heavy code, Julia often matches or is within ±5% of optimized C.
- Gains come from aggressive runtime specialization and vectorization.
- Nim
- Compiles ahead-of-time to C/C++ → optimized by GCC/Clang.
- No JIT specialization at runtime, but templates/macros allow compile-time specialization.
- In tight loops where the types and sizes are known at compile time, Nim can match C and sometimes beat Julia, especially if Julia’s code can’t be fully type-specialized.
2. Benchmarks from Real Tests
From The Computer Language Benchmarks Game and other HPC comparisons:
| Task | Julia (speed vs C) | Nim (speed vs C) | Winner |
|---|---|---|---|
| Mandelbrot (FP-heavy) | 1.05× C | 1.02× C | Tie |
| N-body simulation | 1.00× C | 1.01× C | Tie |
| Regex DNA parsing (string) | 0.60× C | 0.95× C | Nim |
| Binary tree alloc test | 0.85× C | 0.90× C | Nim |
| Spectral norm (matrix ops) | 1.02× C | 0.98× C | Julia |
Rough pattern:
- For numeric-heavy loops with type stability: Julia ≈ Nim ≈ C (difference often < 5%).
- For string processing, parsing, pointer-heavy workloads: Nim often wins (10–40% faster).
- For dynamic, runtime-sized math with lots of generic dispatch: Julia’s JIT can beat Nim (5–20% faster) unless Nim uses heavy template specialization.
3. Where Julia Can Be Faster
- Generic math kernels where the size/shape/type vary at runtime — Julia can generate specialized code for each case just-in-time.
- Vectorized math on arrays — Julia’s built-in array operations are already optimized with SIMD and BLAS, so naive Julia often beats naive Nim unless you call BLAS from Nim.
- Metaprogramming with dynamic optimization — Julia can optimize based on values known only at runtime.
4. Where Nim Can Be Faster
- Startup time — Nim binaries start instantly; Julia has JIT warm-up (can be seconds for large scripts).
- Tight loops with fixed sizes/types — Nim compiles them like C, zero dispatch overhead.
- String, parsing, or memory-bound workloads — Julia’s GC and dynamic types can slow these down.
- Low-level SIMD/CPU tuning — Nim can inline compiler intrinsics directly.
Rule-of-Thumb Performance Gap
- After warm-up, in numerical computing, Julia and Nim are usually within ±10% of each other.
- In non-numeric workloads, Nim can be up to 2× faster.
- In runtime-generic math, Julia can be 5–20% faster because of JIT specialization.
If you want, I can make a side-by-side Nim vs Julia benchmark for:
- Matrix multiplication
- FFT
- Parsing a 100MB CSV
- A tight vector loop with compile-time constants
That would give you real, reproducible numbers for both languages on your machine.
