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Created July 3, 2025 19:18
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Snix Performance Analysis Report

Executive Summary

This performance analysis is based on profiling data from the Snix Nix evaluator. The profile contains approximately 2.6 billion samples across 769 unique stack traces. The analysis reveals that the majority of CPU time is spent in VM execution and thunk evaluation, with significant overhead from memory operations and data structure management.

Component Breakdown

Component Samples Percentage Description
VM Execution 647,033,086 24% Core bytecode interpreter and execution engine
Thunks 442,757,823 16% Lazy evaluation mechanism
Memory Allocation 192,295,474 7% mimalloc memory allocator operations
HashMap Operations 154,529,651 5% Hash table operations (hashbrown)
AST Operations 147,036,317 5% Abstract syntax tree manipulation (rowan)
Parser 94,888,965 3% Nix expression parsing (rnix)
Memory Operations 91,767,890 3% Low-level memory operations (memmove, memcmp)
Rust Allocations 72,699,648 2% Rust standard library allocations
String Operations 51,938,798 1% NixString operations
Compiler 46,016,462 1% Expression compilation to bytecode
Nix Compatibility 43,709,407 1% Nix compatibility layer
Attributes 29,304,295 1% Attribute set operations
Other ~31% Remaining operations

Top Performance Hotspots

1. Lambda Execution (262M samples, 10%)

  • Function: snix_eval::vm::run_lambda
  • This is the top-level function for executing Nix lambda expressions
  • Represents the overall cost of function evaluation

2. Bytecode Execution (196M samples, 7.5%)

  • Function: snix_eval::vm::VM<IO>::execute_bytecode
  • The core interpreter loop executing compiled bytecode
  • High sample count indicates most time is spent in the VM

3. Thunk Forcing (151M samples, 5.8%)

  • Function: snix_eval::value::thunk::Thunk::force_
  • Evaluating lazy values (thunks) when their value is needed
  • Critical for Nix's lazy evaluation semantics

4. Thunk Creation (180M samples combined, 6.9%)

  • Functions: Thunk::new_suspended (115M) and Thunk::new_suspended_call (64M)
  • Creating deferred computations
  • High overhead suggests many values are created but potentially not used

5. Memory Operations (90M+ samples, 3.5%)

  • Functions: __memmove_avx512_unaligned_erms (56M+12M samples)
  • Low-level memory copying operations
  • Indicates significant data movement overhead

Key Findings

1. Thunk Overhead

The combined cost of thunk creation, forcing, and management accounts for approximately 22% of total CPU time. This suggests:

  • Heavy use of lazy evaluation
  • Potential for optimization through strictness analysis
  • Possible memory pressure from thunk allocation

2. Memory Allocation Pressure

  • mimalloc operations account for 7% of samples
  • Additional Rust allocations add 2%
  • Frequent allocation/deallocation cycles indicate high memory churn

3. Generator/Coroutine Overhead

  • genawaiter::core::advance (55M samples)
  • genawaiter::rc::generator::Gen::new (28M samples)
  • The async/generator infrastructure adds ~3% overhead

4. Data Structure Operations

  • HashMap operations (5%) suggest heavy use of attribute sets
  • AST operations (5%) indicate significant tree traversal
  • String operations are relatively efficient at 1%

5. Instruction Dispatch

  • CallFrame::inc_ip (28M samples) and CallFrame::read_uvarint (26M samples)
  • Variable-length integer decoding and instruction pointer advancement
  • Suggests potential for instruction encoding optimization

Optimization Opportunities

High Priority

  1. Thunk Optimization

    • Implement strictness analysis to reduce unnecessary thunks
    • Consider alternative lazy evaluation strategies
    • Optimize thunk memory layout
  2. Memory Allocation

    • Implement object pooling for frequently allocated types
    • Reduce temporary allocations
    • Consider arena allocation for short-lived objects
  3. VM Instruction Dispatch

    • Optimize instruction encoding (fixed-size vs variable)
    • Consider threaded code or JIT compilation
    • Reduce instruction pointer management overhead

Medium Priority

  1. HashMap Performance

    • Profile and optimize hash functions
    • Consider specialized maps for small attribute sets
    • Implement inline storage for small maps
  2. Generator Overhead

    • Evaluate if async/generator pattern is necessary everywhere
    • Consider alternative control flow mechanisms
    • Optimize generator state machines

Low Priority

  1. String Operations

    • Already relatively efficient at 1%
    • Consider string interning for common strings
    • Optimize string comparison operations
  2. Parser Optimization

    • Only 3% of total time
    • May benefit from caching parsed expressions

Conclusion

The Snix evaluator's performance is dominated by the core evaluation loop and lazy evaluation overhead. The most significant optimization opportunities lie in reducing thunk creation through strictness analysis, optimizing memory allocation patterns, and improving VM instruction dispatch. These optimizations could potentially reduce execution time by 20-30%.

The relatively low percentage of time in parsing (3%) and compilation (1%) suggests that the evaluator is efficiently reusing compiled code, and optimization efforts should focus on runtime execution rather than the compilation pipeline.

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