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Unity: joint algebraic and parallelization search for DNN training

Contribution grade

Distributed machine-learning compilers · Human-designed research method or resource

Reference score120,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Represented computation and communication in parallel computation graphs, generated small algebraic/parallel substitutions from operator specifications, checked them with an automated prover and searched their compositions hierarchically.

Scientific scope

Represented computation and communication in parallel computation graphs, generated small algebraic/parallel substitutions from operator specifications, checked them with an automated prover and searched their compositions hierarchically. Verification is relative to supplied operator axioms, not an independently replayed proof of arbitrary floating-point execution. Fingerprints generate candidates but do not prove equivalence. The finite generated basis and hierarchical search do not establish globally optimal training runtime. Extends TASO/FlexFlow rather than inventing all graph rewriting or parallelization.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Verification is relative to supplied operator axioms, not an independently replayed proof of arbitrary floating-point execution. Fingerprints generate candidates but do not prove equivalence. The finite generated basis and hierarchical search do not establish globally optimal training runtime. Extends TASO/FlexFlow rather than inventing all graph rewriting or parallelization.

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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