Julia is really designed to feel dynamic while minimizing the run-time dynamicity the compiler must account for. Type inference and method specialization play a big role, and you can read the Julia papers (on julialang.org) for a discussion of many of the design choices.
Many modern JIT compilers use multiple techniques for aggressive specialization of code sections that meet optimization heuristics (whether tracing-based or otherwise), thus carefully written JavaScript (under V8 and others), Lua (under LuaJIT) and Python (under PyPy and others) can be quite fast. On these platforms there are usually implicit or explicit language subsets and coding styles required to get maximum performance out of the compiler (as with Julia: it is possible to write slow code in any language). For example, ASM.js is an optimization-friendly, explicit subset of JavaScript.
Many modern JIT compilers use multiple techniques for aggressive specialization of code sections that meet optimization heuristics (whether tracing-based or otherwise), thus carefully written JavaScript (under V8 and others), Lua (under LuaJIT) and Python (under PyPy and others) can be quite fast. On these platforms there are usually implicit or explicit language subsets and coding styles required to get maximum performance out of the compiler (as with Julia: it is possible to write slow code in any language). For example, ASM.js is an optimization-friendly, explicit subset of JavaScript.