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 | 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 |
- Function:
snix_eval::vm::run_lambda - This is the top-level function for executing Nix lambda expressions
- Represents the overall cost of function evaluation
- 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
- Function:
snix_eval::value::thunk::Thunk::force_ - Evaluating lazy values (thunks) when their value is needed
- Critical for Nix's lazy evaluation semantics
- Functions:
Thunk::new_suspended(115M) andThunk::new_suspended_call(64M) - Creating deferred computations
- High overhead suggests many values are created but potentially not used
- Functions:
__memmove_avx512_unaligned_erms(56M+12M samples) - Low-level memory copying operations
- Indicates significant data movement 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
- mimalloc operations account for 7% of samples
- Additional Rust allocations add 2%
- Frequent allocation/deallocation cycles indicate high memory churn
genawaiter::core::advance(55M samples)genawaiter::rc::generator::Gen::new(28M samples)- The async/generator infrastructure adds ~3% overhead
- HashMap operations (5%) suggest heavy use of attribute sets
- AST operations (5%) indicate significant tree traversal
- String operations are relatively efficient at 1%
CallFrame::inc_ip(28M samples) andCallFrame::read_uvarint(26M samples)- Variable-length integer decoding and instruction pointer advancement
- Suggests potential for instruction encoding optimization
-
Thunk Optimization
- Implement strictness analysis to reduce unnecessary thunks
- Consider alternative lazy evaluation strategies
- Optimize thunk memory layout
-
Memory Allocation
- Implement object pooling for frequently allocated types
- Reduce temporary allocations
- Consider arena allocation for short-lived objects
-
VM Instruction Dispatch
- Optimize instruction encoding (fixed-size vs variable)
- Consider threaded code or JIT compilation
- Reduce instruction pointer management overhead
-
HashMap Performance
- Profile and optimize hash functions
- Consider specialized maps for small attribute sets
- Implement inline storage for small maps
-
Generator Overhead
- Evaluate if async/generator pattern is necessary everywhere
- Consider alternative control flow mechanisms
- Optimize generator state machines
-
String Operations
- Already relatively efficient at 1%
- Consider string interning for common strings
- Optimize string comparison operations
-
Parser Optimization
- Only 3% of total time
- May benefit from caching parsed expressions
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.