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Pathways: asynchronous distributed accelerator dataflow

Contribution grade

Large-scale AI orchestration systems · Human-designed research method or resource

Reference score130,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Used sharded asynchronous operators over futures and gang scheduling to decouple control-plane execution from dependent accelerator work under a single-controller programming model.

Scientific scope

Used sharded asynchronous operators over futures and gang scheduling to decouple control-plane execution from dependent accelerator work under a single-controller programming model. The orchestration-system contribution is distinct from PaLM's language-model training. Inherits dataflow/SPMD/MPMD systems; measured utilization on selected TPU workloads is not universal100percent utilization, failure freedom or proof of optimal resource scheduling.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. The orchestration-system contribution is distinct from PaLM's language-model training. Inherits dataflow/SPMD/MPMD systems; measured utilization on selected TPU workloads is not universal100percent utilization, failure freedom or proof of optimal resource scheduling.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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