
During a two-month period, Andastrike refined the X86 Znver4 scheduling model in the swiftlang/llvm-project repository, improving the accuracy of latency and micro-op estimates for various instruction classes by applying data-driven adjustments based on updated uops.info data. This work enhanced the reliability of performance estimations, supporting better scheduling and optimization in LLVM backends. In the CachyOS-PKGBUILDS repository, Andastrike enabled vectorized XXH64 hashing for zstd on Znver4 with AVX-512, consolidating related changes and fixing autovectorization flags. The work demonstrated depth in C++, build system configuration, and performance optimization, resulting in measurable improvements for targeted CPU architectures.

December 2025 monthly summary for CachyOS-PKGBUILDS: Delivered architecture-aware performance enhancement by enabling vectorized XXH64 hashing for zstd on Znver4 with AVX-512, consolidating related commits into a single user-facing improvement. Fixed autovectorization flags to ensure stable, correct vectorization across builds, reducing risk of regressions. These changes position CachyOS to deliver higher hashing throughput and more efficient compression workloads on supported CPUs, with clear traceability from commits to release notes. Demonstrated proficiency in low-level optimization, build flag management, and cross-architecture performance tuning.
December 2025 monthly summary for CachyOS-PKGBUILDS: Delivered architecture-aware performance enhancement by enabling vectorized XXH64 hashing for zstd on Znver4 with AVX-512, consolidating related commits into a single user-facing improvement. Fixed autovectorization flags to ensure stable, correct vectorization across builds, reducing risk of regressions. These changes position CachyOS to deliver higher hashing throughput and more efficient compression workloads on supported CPUs, with clear traceability from commits to release notes. Demonstrated proficiency in low-level optimization, build flag management, and cross-architecture performance tuning.
October 2025 focused on refining the X86 Znver4 scheduling model within swiftlang/llvm-project to improve the accuracy of latency and uOP (micro-op) estimates. Implemented data-driven adjustments to latency and micro-op values for instruction classes including division, bit manipulation, string operations, and vector operations, based on updated data from uops.info. The work enhances the reliability of performance estimations, enabling better scheduling decisions, resource planning, and optimization work across LLVM backends.
October 2025 focused on refining the X86 Znver4 scheduling model within swiftlang/llvm-project to improve the accuracy of latency and uOP (micro-op) estimates. Implemented data-driven adjustments to latency and micro-op values for instruction classes including division, bit manipulation, string operations, and vector operations, based on updated data from uops.info. The work enhances the reliability of performance estimations, enabling better scheduling decisions, resource planning, and optimization work across LLVM backends.
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