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Cleave compiler optimizes tensor programs for large models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Cleave, a new machine learning compiler designed to optimize tensor programs for large models. Cleave employs a strategy of symbolic decoupling, first performing superoptimization on a graph with symbolic shapes and then scheduling the transformed graph on concrete shapes. This approach allows for efficient handling of computations with multiple reductions and has demonstrated significant speedups, generating kernels up to 2.8x faster than existing baselines. AI

IMPACT Cleave's optimization techniques could lead to faster and more efficient execution of large AI models.

RANK_REASON Academic paper detailing a new compiler technique for optimizing ML models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Cleave compiler optimizes tensor programs for large models

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Academic paper detailing a new compiler technique for optimizing ML models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · David Pissarra, Jinkun Lin, Haitian Jiang, Aurojit Panda, Jinyang Li ·

    Cleave: Scaling Tensor Program Optimization via Decoupled Algebraic Search and Operator Scheduling

    arXiv:2610.07742v1 Announce Type: cross Abstract: Optimized kernels such as FlashAttention and FlashDecoding are crucial for accelerating today's large models. Most of them are handwritten by experts because existing ML compilers cannot match their efficiency. Producing such kern…

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