facebookresearch/faiss
Jun 2026 – Jun 2026
1 Month active
Languages Used
C++Python
Technical Skills
C++ developmentSIMD programmingbenchmarkinghigh-performance computing
Mulugeta Mammo
PROFILE
Mulugeta Mammo
Worked on the facebookresearch/faiss repository to deliver AVX512_SPR optimizations for Hamming distance computations, focusing on the HammingComputer classes. Leveraged C++ and SIMD programming to implement SPR-specific specializations using VPOPCNTDQ instructions, which accelerated Hamming-based similarity searches on Sapphire Rapids hardware. The work included removing obsolete code paths, centralizing popcount logic, and updating build configurations and benchmarking scripts in both C++ and Python. These changes enabled up to 1.91x speedups for k=1 Hamming workloads, supporting high-performance computing goals and improving throughput for large-scale vector processing tasks. The engineering demonstrated depth in performance tuning and benchmarking.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
294
Activity Months1
Your Network
142 peopleWork History
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026 delivered AVX512_SPR optimizations for Hamming distance computations in FAISS HammingComputer, achieving up to 1.91x speedups over the previous AVX512 path on Sapphire Rapids. Implemented SPR-specific specializations using VPOPCNTDQ, removed obsolete code, and integrated new benchmarking/dispatch flows. Updated builds and benchmarks to reflect the new SIMD capabilities, enabling faster Hamming-based similarity searches and improved throughput for large-scale vector workloads. Demonstrated solid performance engineering across SIMD ISA levels and aligned with broader performance and scalability goals.
1 Commits • 1 Features
Jun 1, 2026June 2026 delivered AVX512_SPR optimizations for Hamming distance computations in FAISS HammingComputer, achieving up to 1.91x speedups over the previous AVX512 path on Sapphire Rapids. Implemented SPR-specific specializations using VPOPCNTDQ, removed obsolete code, and integrated new benchmarking/dispatch flows. Updated builds and benchmarks to reflect the new SIMD capabilities, enabling faster Hamming-based similarity searches and improved throughput for large-scale vector workloads. Demonstrated solid performance engineering across SIMD ISA levels and aligned with broader performance and scalability goals.
June 2026
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++ developmentSIMD programmingbenchmarkinghigh-performance computing
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