Nechama M.

Responsibilities:

• Implemented several optimized algorithmic variants of the Poly-Streaming Maximum Weight Matching algorithm in C++, enabling scalable, high-performance graph processing based on an academic research model.

• Implemented efficient synchronization mechanisms that reduced contention and improved scalability and stability across multi-core parallel environments.

• implemented a NUMA-aware variant of the algorithm, ensuring locality-optimized memory usage, reduced remote access, and improved performance on multi-socket systems.

• Developed a comprehensive test suite with Google Test and integrated it into a CI pipeline (GitHub Actions) to automatically run unit and integration tests, ensuring correctness and code quality across the project.

• Used Sanitizers and perf to identify performance bottlenecks and achieve significant runtime improvements.

• Used CMake to build a clean and modular system, working in a Linux environment

• Research: Cloud Data Access – Investigated loading compressed graph datasets directly from cloud storage to reduce local disk usage, experiments showed that latency and decompression overhead made this approach impractical for high-performance runs.