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rainoftimerainoftime/pl-skills/liveness-analysis/SKILL.md

liveness-analysis

Skill17 starsChanged 9 months agoApache-2.0View on GitHub

Compute which variables are live at each program point for optimization and register allocation.

The file

We split this skill into the rules it gives an agent

6 rules. About 0 are common, 6 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈6

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Run in reverse order for efficiency

    line 43
  2. Distinctive:

    Use worklist algorithm for large functions

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  3. Distinctive:

    Consider SSA form for simpler analysis

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  4. Distinctive:

    Use for dead code detection

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  5. Distinctive:

    When memory is not constrained

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  6. Distinctive:

    Does not consider aliasing

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≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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Files
SKILL.md

Liveness Analysis

Liveness analysis determines which variables may be used in the future at each program point. It is a fundamental backward dataflow analysis used for register allocation, dead code elimination, and optimization.

When to Use This Skill

  • Register allocation
  • Dead code elimination
  • Optimizing compiler passes
  • Understanding program behavior
  • Building IDE features

What This Skill Does

  1. Live Variable Detection: Find variables that may be used later
  2. Backward Dataflow: Propagate information from uses to definitions
  3. Control Flow Handling: Merge information at join points
  4. Register Allocation: Map live ranges to registers
  5. Dead Code Detection: Find dead stores

Key Concepts

ConceptDescription
Live VariableVariable that may be used before being redefined
Live RangeInstructions where a variable is live
UseInstruction that reads a variable
DefinitionInstruction that writes a variable
Backward AnalysisInformation flows from uses to definitions

Tips

  • Run in reverse order for efficiency
  • Use worklist algorithm for large functions
  • Consider SSA form for simpler analysis
  • Combine with register allocation
  • Use for dead code detection

Common Use Cases

  • Register allocation
  • Dead code elimination
  • Live variable debugging
  • Memory optimization
  • SSA construction

Related Skills

  • dataflow-analysis-framework - General framework
  • register-allocator - Uses liveness info
  • dead-code-eliminator - Uses liveness to find dead code
  • ssa-constructor - Uses liveness for phi placement

Canonical References

ReferenceWhy It Matters
Aho et al., "Compilers: Principles, Techniques, and Tools", Ch. 9 (1986)Classic treatment
Muchnick, "Advanced Compiler Design and Implementation", Ch. 9 (1997)Detailed algorithms
Cytron et al., "Efficiently Computing Static Single Assignment Form" (TOPLAS 1991)SSA liveness

Tradeoffs and Limitations

Approach Tradeoffs

ApproachProsCons
Simple backwardEasy to implementO(n²) worst case
WorklistEfficientMore complex
SSA-basedSimpler livenessRequires SSA

When NOT to Use This Skill

  • Simple stack machines (no register allocation)
  • Interpreted languages with unlimited locals
  • When memory is not constrained

Limitations

  • Conservative (may overestimate liveness)
  • Does not consider aliasing
  • Path-sensitive liveness is expensive

Assessment Criteria

A high-quality implementation should have:

CriterionWhat to Look For
CorrectnessSound over-approximation
EfficiencyReasonable runtime
HandlingDeals with loops, branches
IntegrationWorks with CFG

Quality Indicators

✅ Good: Correct liveness, efficient algorithm, handles all control flow ⚠️ Warning: Slow on large functions, doesn't handle loops ❌ Bad: Incorrect results, misses live variables

Research Tools & Artifacts

Real-world liveness analysis tools:

ToolWhy It Matters
LLVMProduction liveness in LLVM
GCCGCC dataflow framework
SootJava bytecode analysis
MIRAIRust liveness analysis

Key Systems

  • LLVM: SSA-based liveness
  • Graal: Truffle liveness

Research Frontiers

Current liveness analysis research:

DirectionKey PapersChallenge
Precise liveness"Precise Liveness"Aliasing
Incremental"Incremental Liveness"Change impact
Parallel"Parallel Liveness"Multi-core

Hot Topics

  1. WASM liveness: Binary liveness
  2. ML liveness: Learning liveness

Implementation Pitfalls

Common liveness bugs:

PitfallReal ExamplePrevention
AliasingPointer aliasingConservative
MemoryLarge programsIncremental

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