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      "statement": "# GSPMD: compiler propagation of tensor sharding\n\nInferred operator partitioning from limited tensor annotations and generated SPMD computation, supporting uneven/nested sharding, padding and halo exchange in XLA.\n\n## Scientific scope\n\nExtends GShard and inherits XLA/SPMD. Pipeline support requires wrappers or existing pipelining in some cases; annotations are not removed entirely. Tested TPU utilization and compiler scalability are not general hardware/workload optimality.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nYuanzhong Xu; HyoukJoong Lee; Dehao Chen; Blake Hechtman; Yanping Huang; Rahul Joshi; Maxim Krikun; Dmitry Lepikhin; Andy Ly; Marcello Maggioni; Ruoming Pang; Noam Shazeer; Shibo Wang; Tao Wang; Yonghui Wu; Zhifeng Chen\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [GSPMD: General and Scalable Parallelization for ML Computation Graphs](https://arxiv.org/abs/2105.04663v1)\n",
      "summary": "Inferred operator partitioning from limited tensor annotations and generated SPMD computation, supporting uneven/nested sharding, padding and halo exchange in XLA.",
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