
During November 2025, Sakib Rahman developed a memory-optimized particle filtering feature for the eic/EICrecon repository, focusing on performance and scalability. He introduced a configurable filter based on generator status within the UndoAfterBurner algorithm, allowing users to limit processed particles and reduce memory usage by approximately fivefold in merged-background event tests. The solution leveraged C++ programming, algorithm optimization, and memory management, exposing the new parameter for runtime configuration without requiring code changes from users. Rahman ensured backward compatibility and provided clear usage examples, demonstrating a thoughtful approach to extensibility and operational efficiency in high-throughput event processing environments.
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.

Overview of all repositories you've contributed to across your timeline