Releases: CHSZLab/AgenticAlgorithmEngineering
Releases · CHSZLab/AgenticAlgorithmEngineering
v1.03
Knowledge Base: 30 Performance Optimization Entries
Seed the knowledge base with 30 expert entries covering C++ and Python performance optimization techniques.
C++ (entries 004–018)
- Cache efficiency: SoA vs AoS, hot-cold splitting, loop tiling
- Branch & instruction optimization: branchless programming, SIMD vectorization, compiler intrinsics
- Memory management: small buffer optimization, move semantics, reserve/preallocate, mmap, arena-style hash maps
- Compiler techniques: constexpr, PGO + LTO
- Concurrency: false sharing avoidance
- API design: std::string_view
Python (entries 019–033)
- Vectorization & JIT: NumPy vectorization, Numba JIT, Cython
- Memory reduction: slots, generators, array module, mmap
- Data structures: dict/set O(1) lookup, deque for queues
- Patterns: preallocation, string join, local variable caching, itertools pipelines, struct packing
- Parallelism: multiprocessing to bypass GIL
v1.02
What's New
Knowledge Base
- Community-contributed knowledge base for sharing optimization techniques, experiment results, and lessons learned from AAE sessions
- Agents can fetch and search the knowledge base from GitHub to inform hypothesis generation
- Contributors submit entries via PR (one entry per PR, with INDEX.md update)
- Three example entries included (blocked sorting, gradient accumulation, arena allocation)
AAE Program Enhancements
- User assertions: The agent now formulates correctness invariants from the code and presents them to the user for confirmation during setup. Assertions are checked after every change, before benchmarking.
- Subcomponent replacement: Explicitly allows replacing entire algorithms, data structures, or modules (not just incremental tuning) when the analysis supports it.
Housekeeping
- Moved image files to
img/folder - Added knowledge base banner image
v1.01
- Add Agent Teams section for keeping the AAE loop alive
- Encourage GNU parallel for multi-run experiments
- Use geometric mean as default aggregation statistic