
Rasmus Larsen contributed to core math and compiler infrastructure in the google/filament and google/xls repositories, focusing on performance and reliability. He optimized quaternion power and slerp calculations in C++ for 3D transformations, leveraging mathematical identities to improve both speed and accuracy while addressing edge-case stability. In google/xls, he enhanced JIT and AOT compilation by reducing memory allocations, introducing cross-compilation flags, and refactoring interpreter result handling using efficient data structures and move semantics. Larsen also inlined key math utilities with compiler intrinsics, reducing function call overhead. His work demonstrated depth in low-level programming, mathematics, and build system optimization.

July 2025 monthly summary for google/xls. The primary focus this month was performance optimization of core math utilities, delivering inline implementations to reduce overhead and improve runtime efficiency in arithmetic-heavy XLS workloads.
July 2025 monthly summary for google/xls. The primary focus this month was performance optimization of core math utilities, delivering inline implementations to reduce overhead and improve runtime efficiency in arithmetic-heavy XLS workloads.
June 2025 monthly performance summary for google/xls highlighting business value and technical achievements. Key features delivered: JIT Compiler Performance Optimization and AOT Target Flag with Cross-Compilation. Major bugs fixed: eliminated unnecessary allocations in XLS JIT. Overall impact: improved runtime performance, reduced memory pressure, and streamlined cross-platform build process, enabling faster releases and broader platform support. Technologies/skills demonstrated: JIT/AOT optimization, move semantics, efficient data structures, interpreter result handling, Bazel build improvements, and cross-compilation workflows.
June 2025 monthly performance summary for google/xls highlighting business value and technical achievements. Key features delivered: JIT Compiler Performance Optimization and AOT Target Flag with Cross-Compilation. Major bugs fixed: eliminated unnecessary allocations in XLS JIT. Overall impact: improved runtime performance, reduced memory pressure, and streamlined cross-platform build process, enabling faster releases and broader platform support. Technologies/skills demonstrated: JIT/AOT optimization, move semantics, efficient data structures, interpreter result handling, Bazel build improvements, and cross-compilation workflows.
April 2025: Key strides in quaternion math for the google/filament repo. Delivered a feature: optimized quaternion pow and slerp calculations using mathematical identities, improving performance and accuracy for 3D transformations. Also fixed quaternion helper issues (typos and pow implementation) to ensure correct quaternion construction and reliable sine/cosine based on norm and angle. Impact: faster, more stable 3D rendering paths with reduced risk of artifacts; improved code quality and maintainability in core math utilities. Technologies: C++ quaternion algebra, numeric optimization, identity-based computation, code hygiene.
April 2025: Key strides in quaternion math for the google/filament repo. Delivered a feature: optimized quaternion pow and slerp calculations using mathematical identities, improving performance and accuracy for 3D transformations. Also fixed quaternion helper issues (typos and pow implementation) to ensure correct quaternion construction and reliable sine/cosine based on norm and angle. Impact: faster, more stable 3D rendering paths with reduced risk of artifacts; improved code quality and maintainability in core math utilities. Technologies: C++ quaternion algebra, numeric optimization, identity-based computation, code hygiene.
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