Why Julia
Julia is designed for high-performance scientific computing with JIT compilation via LLVM. It claims to solve the "two-language problem" — write like Python, run like C. The linpack benchmark would be a particularly interesting comparison given Julia's strength in numerical computing.
Key considerations
- JIT-compiled — first run includes compilation time, need to account for warmup
- Strong in numerical/linear algebra workloads (linpack will be telling)
- Dynamic typing with optional type annotations
- Package manager is built-in (
Pkg)
Implementation
Implement all 7 benchmark algorithms following the reference implementations (PHP, C++, Python):
Setup:
Why Julia
Julia is designed for high-performance scientific computing with JIT compilation via LLVM. It claims to solve the "two-language problem" — write like Python, run like C. The linpack benchmark would be a particularly interesting comparison given Julia's strength in numerical computing.
Key considerations
Pkg)Implementation
Implement all 7 benchmark algorithms following the reference implementations (PHP, C++, Python):
Setup:
langs/julia/benchmark.ymldocker/julia/Dockerfile