May 2026: Delivered an environment-driven protocol selection enhancement for the Mooncake Transfer Engine to improve compatibility with EFA hardware. The feature enables selecting the transport protocol via MOONCAKE_PROTOCOL, reducing manual configuration and enabling seamless operation across diverse environments. Major bug fix implemented to honor MOONCAKE_PROTOCOL so EFA hardware can select the appropriate transport (commit referenced). This work was implemented in yhyang201/sglang with a focused change set and cross-team collaboration (Co-authored-by whn09), contributing to reliability and hardware readiness. Overall impact: improved interoperability, deployment flexibility, and maintainability across Mooncake deployments. Technologies demonstrated: environment-based configuration, protocol routing, code attribution and collaboration.

April 2026 Mooncake monthly summary (kvcache-ai/Mooncake): Performance-focused EFA transport work across Mooncake delivered a series of structural and safety improvements that unlock higher throughput, lower latency, and greater scalability for large memory transfers and multi-NIC configurations. The month centered on elevating EFA transport reliability, expanding memory registration capabilities, and moving to a shared-endpoint model to dramatically reduce handshake overhead and QP consumption while maintaining robust error handling and test coverage. In addition, efforts were made to prepare bindings and benchmarks for broader usage (Python bindings and warmup facilities) to improve integration with downstream workloads. Key deliverables include hardware-agnostic read/write support on the EFA transport, smarter endpoint lifecycle and eviction, NIC-striping-based data transfer optimizations, and PTE-aware memory registration with per-chunk NIC allocation. A SRD-based shared-endpoint refactor reduces per-peer endpoints and streamlines handshake/setup, achieving lower tail latency and improved drift resilience. Together, these changes enable larger, faster transfers across many NICs and GPUs with safer memory registration and more predictable performance. Overall impact: improved data-transfer throughput and stability for long-running, multi-peer workloads; reduced first-batch latency and operational burden; easier integration with modern ML workloads via Python bindings and improved benchmarking. Technologies/skills demonstrated: EFA/libfabric transports, memory registration (MR) management, per-NIC data striping, PTE budgeting and auto-splitting, idempotent warmup and shared-endpoint design, atomic/pacing improvements, multi-language bindings (C/C++, Python), extensive benchmarking and test automation.

February 2026: Delivered the AWS EFA transport backend (libfabric) for Mooncake, enabling a high-throughput, low-latency networking option on AWS EFA devices with a TCP fallback. Implemented the EfaTransport architecture (EfaContext → EfaEndPoint), per-device CQ polling, and thread-safe endpoint management, along with unit tests and benchmarking tooling. Hardened the EFA build path, added explicit TCP transport installation for non-EFA protocols, and updated documentation across build, usage, and benchmarking. Delivered extensive EFA documentation, updated toctree, and performance benchmarks to quantify gains over TCP and current Mooncake transports.