
Developed a memory-optimized particle filtering feature for the eic/EICrecon repository, focusing on performance and scalability in C++. The work introduced a configurable filter based on generator status, allowing users to limit the number of processed particles and thereby reduce memory consumption by up to five times in merged-background event tests. This was achieved by adding a new parameter to the UndoAfterBurner algorithm and exposing it for runtime configuration, enabling flexible usage without requiring code changes. The implementation maintained backward compatibility and provided clear usage examples, demonstrating strong skills in C++ programming, algorithm optimization, and memory management within collaborative workflows.
Monthly summary for 2025-11: Performance-focused update in eic/EICrecon delivering a memory-optimized particle filtering capability. Introduced a configurable filter by generator status that substantially reduces memory usage during event processing. The change adds a new m_max_gen_status parameter to the UndoAfterBurner algorithm (default 1000, -1 to disable limit) and exposes it through the factory for runtime configuration. In tests with merged background events, memory consumption was reduced by approximately 5x, enabling processing of larger datasets with improved throughput. The feature is designed to be non-breaking and user-configurable, with a usage example provided for operators. This work demonstrates solid C++ development, runtime configurability, and alignment with performance and scalability goals.
Monthly summary for 2025-11: Performance-focused update in eic/EICrecon delivering a memory-optimized particle filtering capability. Introduced a configurable filter by generator status that substantially reduces memory usage during event processing. The change adds a new m_max_gen_status parameter to the UndoAfterBurner algorithm (default 1000, -1 to disable limit) and exposes it through the factory for runtime configuration. In tests with merged background events, memory consumption was reduced by approximately 5x, enabling processing of larger datasets with improved throughput. The feature is designed to be non-breaking and user-configurable, with a usage example provided for operators. This work demonstrates solid C++ development, runtime configurability, and alignment with performance and scalability goals.

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