  We present the design of a new large scale orchestration layer for
accelerators. Our system, Pathways, is explicitly designed to enable
exploration of new systems and ML research ideas, while retaining state of the
art performance for current models. Pathways uses a sharded dataflow graph of
asynchronous operators that consume and produce futures, and efficiently
gang-schedules heterogeneous parallel computations on thousands of accelerators
while coordinating data transfers over their dedicated interconnects. Pathways
makes use of a novel asynchronous distributed dataflow design that lets the
control plane execute in parallel despite dependencies in the data plane. This
design, with careful engineering, allows Pathways to adopt a single-controller
model that makes it easier to express complex new parallelism patterns. We
demonstrate that Pathways can achieve performance parity (~100% accelerator
utilization) with state-of-the-art systems when running SPMD computations over
2048 TPUs, while also delivering throughput comparable to the SPMD case for
Transformer models that are pipelined across 16 stages, or sharded across two
islands of accelerators connected over a data center network.
