| 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.
