Hasung Soo developed an All-to-All communication backend for Decode Context Parallel in the jeejeelee/vllm repository, targeting reduced inter-node overhead and improved distributed inference throughput. By leveraging Python and applying distributed systems and parallel computing principles, Hasung designed a core backend component that enables more efficient scaling across multi-node deployments. The implementation focused on optimizing communication patterns to lower latency and increase end-to-end throughput for DCP workloads. This work involved close cross-team collaboration, as reflected in multi-author sign-offs, and established a technical foundation for scalable distributed inference. The contribution demonstrated depth in backend development and performance optimization.