Developed hardware acceleration for the all_all_out operation in PyTorch by integrating an MTIA kernel, focusing on backend development and performance optimization. The work involved registering the MTIA kernel and aligning kernel dispatch within the pytorch/pytorch repository, enabling faster tensor operations on MTIA-enabled devices. Using YAML for configuration and collaborating directly in the main repository, the developer improved hardware utilization and execution speed for high-throughput workloads. This feature contributed to PyTorch’s broader performance goals by enhancing support for specialized hardware, with no major bug fixes during the period. The approach demonstrated expertise in hardware acceleration and repository integration workflows.