By name: memory-profiler description: Diagnose memory inefficiencies, capture high-resolution memory profiles, and execute architectural refactoring across C++, Rust, TypeScript/Node.js, .NET, and Python. Use when troubleshooting memory leaks, out-of-memory (OOM) errors, or heap fragmentation.
Memory Profiler
This skill establishes an exhaustive framework for diagnosing memory inefficiencies, capturing memory profiles, and executing architectural refactoring across various programming languages.
Core Concepts
- Memory Wall: Speed and efficiency are constrained by memory spatial locality, access patterns, and allocation overhead.
- Fragmentation: Frequent allocations/deallocations cause heap memory to fragment, forcing OS to allocate additional pages.
- Data-Oriented Design (DOD): Refactoring from Array of Structures (AoS) to Structure of Arrays (SoA) guarantees homogenous data fields are packed tightly, optimizing CPU cache usage and minimizing RAM latency.
Language Specific Guides
For instructions on memory profiling, tooling workflows, and refactoring strategies for a specific language, load the corresponding reference document:
- C++: See references/cpp.md for Valgrind, Heaptrack, Memory Pools, and SSO.
- Rust: See references/rust.md for Jemallocator, Dhat, Collection Bounding, Cow, and Arc.
- TypeScript & Node.js: See references/node.md for V8 Inspector, Clinic.js, Object Pooling, and Closure scope mitigation.
- .NET (C#): See references/dotnet.md for dotnet-counters, dotnet-gcdump, ArrayPool, and struct conversions.
- Python: See references/python.md for Tracemalloc,
Memray,
__slots__, Generators, and String Interning.
Use these files to perform high-level memory analysis, locate leaks, and apply the appropriate refactoring pattern.