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Programming Language Theory: Compiler design#

Meta Tracing#

Idea#

Write an interpreter and get a JIT for free by trace the execution of an interpreter and JIT the trace

  • Insight: interpreter is simply a large loop: load the next instruction, perform the associated actions, go back to the beginning of the loop.
  • In Practice: bimodal performance leads people to prefer method based JITs instead

Details#

VM consists of two interpreters: language interpreter and tracing interpreter

  • language interpreter
  • normal interpreter of language
  • tracing interpreter
  • second representation of the interpreter that creates traces
  • when a hot loop is detected, insert marker
  • on next loop execution, tracing interpreter used to record each action
  • the execution trace is then JIT'ed into machine code that subsequent loop executions will use
  • tracing interpreter automatically inserts guard checks to verify trace preconditions/code-paths taken match the JIT'ed code
  • if guard fails, execution falls back to the tracing interpreter for the rest of that bytecode, and then back to the language interpreter
  • only need two meta functions for the JIT
    • can_enter_jit: mark hot loop entry and generate machine code
    • jit_merge_point: mark when it can switch to an existing machine code version of a loop.

Issues#

  • unpredictable performance (very bimodal, either super fast or super slow)
  • great for tight, non-branchy code terrible at branchy code
  • optimizing only single code-path at a time
  • if a trace guard fails
    • execution must use the very slow tracing interpreter for remainder of bytecode (might be very long)
    • then switch back to the language interpreter for subsequent bytecode
  • fundamental issue is that no way of knowing which code is likely to branch unpredictably until it actually does so
  • Example: AST type checker, calling a function _preorder(x) which, using reflection, dispatches to a function which can handle that AST type

References#

Lua#

Query Based Compilers#

Overview#

Instead of linear batch pass, implement compiler as a database that can be queried

  • queries e.g. type_of are implemented as deterministic pure functions that explicitly declare their dependencies/outputs
  • compilation becomes just a big pure function
  • aim is to allow for incremental computation
  • internally,
  • just a big lazily constructed data dependency DAG
  • queries are memoized for performance
  • simple idea but lots of complex nuance implementation around memoization
    • dependency/partial change tracking
    • caching
    • efficient result cloning e.g. structural sharing
    • cycle detection
    • enforcing determinism
    • etc

References#

Symbolic Assembly#

Symbolic Assembly: Using Clojure to Meta-program Bytecode - Ramsey Nasser: https://www.youtube.com/watch?v=eDad1pvwX34 The Language of the Language: Comparing compiler construction in Clojure and F# - Ramsey Nasser https://www.youtube.com/watch?v=t8usj1fN9rs

Example Repos#


Last update: 2022-09-07