rapidsai/cuvs
May 2025 – May 2025
1 Month active
Languages Used
AssemblyC++
Technical Skills
ARM NEON IntrinsicsAlgorithm OptimizationC++Performance OptimizationVectorization
Anna Verner
PROFILE
Anna Verner
Worked on the rapidsai/cuvs repository to deliver a performance-focused optimization for the host refine phase, implementing a generic vectorized Euclidean distance calculation tailored for ARM NEON architectures. The approach replaced serial assembly and strictly-ordered fadda operations with NEON intrinsics and partial sums, enabling improved throughput and reduced CPU contention on NEON-enabled devices. This C++ and assembly-based solution enhanced both compiler and architecture efficiency for distance-based workloads. The work demonstrated strong skills in algorithm optimization, vectorization, and performance tuning, addressing the need for scalable, efficient computation in high-performance environments while aligning with the repository’s performance-first development goals.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
314
Activity Months1
Your Network
59 peopleWork History
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly performance overview for rapidsai/cuvs. Delivered a major optimization in the host refine phase by introducing a generic vectorized Euclidean distance calculation for ARM NEON. The new implementation uses partial sums and NEON intrinsics, replacing the previous serial assembly and strictly-ordered fadda usage. This work enhances throughput on NEON-enabled devices, reduces CPU contention, and improves compiler/architecture efficiency across supported ARM platforms. The change aligns with performance-first goals in distance-based workloads and is tracked under the commit 7affbc034e36cb66288391302ba6f910b54cd517 (Optimize euclidean distance in host refine phase (#689)).
1 Commits • 1 Features
May 1, 2025May 2025 monthly performance overview for rapidsai/cuvs. Delivered a major optimization in the host refine phase by introducing a generic vectorized Euclidean distance calculation for ARM NEON. The new implementation uses partial sums and NEON intrinsics, replacing the previous serial assembly and strictly-ordered fadda usage. This work enhances throughput on NEON-enabled devices, reduces CPU contention, and improves compiler/architecture efficiency across supported ARM platforms. The change aligns with performance-first goals in distance-based workloads and is tracked under the commit 7affbc034e36cb66288391302ba6f910b54cd517 (Optimize euclidean distance in host refine phase (#689)).
May 2025
Activity
Loading activity data...
Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
AssemblyC++
Technical Skills
ARM NEON IntrinsicsAlgorithm OptimizationC++Performance OptimizationVectorization
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline