Implement JIT-Specific Optimizations

Cheap Wins Under Time Budget

JIT optimizers run under millisecond budgets. JavaScriptCore's DFG folds constants and eliminates type checks on known shapes. LuaJIT traces specialize on observed types. Inline caching replaces polymorphic dispatch. HotSpot's C2 only runs after C1 has gathered enough profile data to justify the compile cost.

JIT-Specific Patterns

Specialize on runtime types seen at compile time. Guard instructions verify assumptions; failure triggers deopt. Strength-reduce within trace bounds. Keep the fast path branch-free where possible so the CPU predictor stays hot.

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• Type feedback from interpreter profiling
• Guards are cheap branches on fast path
• OSR (on-stack replacement) mid-loop entry
• Bailout rebuilds interpreter frame state

Connecting to real compilers

Production compilers embed this step inside a longer pipeline. GCC flows through cpp, cc1, assembly, and ld; Clang uses the driver, Sema, LLVM IR passes, and a target backend. LLVM bitcode, JVM bytecode, and WASM are other familiar IRs at the same layer. The exercise isolates one pass so you can test it alone before chaining it to the next stage.

Why for this exercise

You will implement JIT-specific optimizations guarded by runtime checks. This exercise requires specializing code on profiled types and inserting deoptimization points when assumptions fail.