
Worked on high-performance system programming projects, focusing on low-level optimization and resource management. For google/XNNPACK, developed and integrated a 10x8 F32 GEMM/IGEMM microkernel using C and assembly intrinsics, updating build systems and configuration to enable end-to-end usage and kernel selection based on output channel dimensions. In tschneidereit/wasm-micro-runtime, implemented a configurable shared WASM heap size feature, adding a command-line interface option and logic to align memory allocation with system page size. Demonstrated expertise in C, build systems, and WASM, delivering targeted features that improve performance, resource control, and maintainability in complex, performance-sensitive environments.
July 2025 – tschneidereit/wasm-micro-runtime: Delivered Configurable Shared WASM Heap Size feature enabling tunable memory budgets for WASM workloads. The change adds a CLI option to specify the shared heap size, includes parsing logic, creates/attaches the shared heap when provided, and aligns the size to the system page size for reliable memory management. Impact: improved resource control, better performance tuning, and easier capacity planning for multi-tenant deployments. No major bugs fixed this month.
July 2025 – tschneidereit/wasm-micro-runtime: Delivered Configurable Shared WASM Heap Size feature enabling tunable memory budgets for WASM workloads. The change adds a CLI option to specify the shared heap size, includes parsing logic, creates/attaches the shared heap when provided, and aligns the size to the system page size for reliable memory management. Impact: improved resource control, better performance tuning, and easier capacity planning for multi-tenant deployments. No major bugs fixed this month.
March 2025 for google/XNNPACK focused on delivering a high-performance GEMM/IGEMM microkernel, with end-to-end build/config integration and kernel selection optimization. The effort also included FC-layer dispatch integration to route workloads to the new kernel, enabling efficient use of the new microkernel in real workloads. This work lays a foundation for sustained performance improvements on targeted matrix dimensions and streaming workloads.
March 2025 for google/XNNPACK focused on delivering a high-performance GEMM/IGEMM microkernel, with end-to-end build/config integration and kernel selection optimization. The effort also included FC-layer dispatch integration to route workloads to the new kernel, enabling efficient use of the new microkernel in real workloads. This work lays a foundation for sustained performance improvements on targeted matrix dimensions and streaming workloads.

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