March 2026 monthly summary for golang/go focused on delivering precise RISC-V memory ordering in the assembler. Key work delivered includes FENCE support and FENCE.TSO in the RISC-V assembler, enabling fine-grained memory barriers and reducing unnecessary full barriers. Changes were validated through code review and upstream testing, with LUCI verification.

Month 2025-12 — golang/go: focused on performance optimization and stability for RISC-V, delivering a critical bug fix and validating performance gains.

September 2025 monthly summary focused on enabling end-to-end Zibi experimental extension support for the RISC-V backend across LLVM projects. Delivered MC-level support and CodeGen integration to facilitate experimental evaluations and performance investigations of the Zibi ISA. Key outcomes: - Implemented MC support for Zibi experimental extension on RISC-V (branch-with-immediate) in llvm-project, including necessary definitions and tests to ensure correct assembly/disassembly handling. Commits: a7521a81c4b7aa135086488a566eab2dbc6b1326. - Implemented CodeGen support for the Zibi extension in RISC-V in swiftlang/llvm-project, introducing new instruction patterns and selection logic for conditional branches and selects to enable efficient code emission. Commits: 27f8f9e1f1dcf00df8c338df29193833e6d807f8. Business value: Provides first-class end-to-end support for experimental Zibi workflows, enabling faster iteration, improved code quality for Zibi-enabled toolchains, and reduced integration risk as the feature moves toward broader adoption. Overall impact: Cross-repo alignment between MC and CodeGen ensures consistent handling of Zibi instructions from assembly/disassembly through to code generation, accelerating experimentation and potential performance gains. Technologies/skills demonstrated: LLVM MC, LLVM CodeGen, RISC-V backend, Zibi ISA, branch-with-immediate handling, instruction patterns and select lowering, test instrumentation.

July 2025: Deliveries focus on memory lowering precision and RISC-V vector capabilities across LLVM-based codegen and the Go toolchain. In llvm/clangir, refined memory operation type determination by introducing LLVM Context usage and EVT vector typing, enabling more precise lowering as part of broader memory-lowering improvements. In golang/go, expanded vector support with assembler support for vector unit-stride fault-only-first loads (vle8ff, vle16ff, vle32ff, vle64ff) and implemented vector segment load/store instructions, aligning Go tooling with RVV capabilities and boosting performance for vector workloads.