One of Julia's secrets to high-performance lies in its ability to seamlessly call high-level languages like C and Fortran. This is made possible through its foreign function interface (FFI). By wrapping external code in Julia functions, developers can leverage the speed and numerical libraries of these languages while still enjoying the high-level features and expressiveness of Julia.

Julia's just-in-time (JIT) compilation is the main reason behind its high-performance nature. Unlike languages that have a separate compilation and execution phase, Julia's approach allows it to dynamically compile code as it is encountered, allowing for efficient optimization based on runtime behavior. This enables Julia to blend the best of both interpreted and statically compiled languages, offering high performance without sacrificing development speed.

Julia achieves high-performance by using just-in-time (JIT) compilation which allows it to dynamically compile code at runtime, optimizing it for the specific data types used. This eliminates overhead and improves execution speed. For example, when performing mathematical computations, Julia can generate highly optimized machine code that rivals the performance of statically typed languages like C or Fortran.

To achieve its high-performance, Julia uses multiple dispatch and a sophisticated type system. Julia allows multiple functions with the same name but different argument types, and the appropriate implementation is chosen based on the input types. This allows highly efficient and specialized code to be generated, tailored to the specific types encountered at runtime. This powerful feature minimizes the need for explicit type annotations and empowers developers to write concise and expressive code without sacrificing performance.

Julia's performance stems from its type system and multiple dispatch. By allowing flexible and expressive type annotations, Julia can infer types and specialize functions accordingly. This enables efficient code generation, reducing the need for runtime checks and promoting efficient memory access patterns. The performance gains are particularly noticeable in scientific computing and numerical analysis tasks, where Julia outperforms many other languages.