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Space Filling Curves simplify communication-avoiding matrix multiplication

By PulseAugur Editorial · [1 sources] ·

A new research paper introduces Space Filling Curves (SFC) as a method to simplify and optimize Communication-Avoiding General Matrix Multiplication (CA-GEMM) for high-performance computing and deep learning. This approach achieves platform-oblivious and shape-oblivious matrix multiplication schemes with improved data locality. The SFC-CA GEMM algorithm demonstrates provable asymptotic communication optimality and outperforms vendor libraries by up to 5.5x on x86 and Arm platforms. Its application in LLM inference prefill shows speedups of up to 1.85x, and in distributed-memory matrix multiplication, it achieves speedups of up to 2.3x. AI

IMPACT Optimizes LLM inference prefill, potentially accelerating deployment and reducing compute costs for large language models.

RANK_REASON Research paper detailing a novel algorithmic approach to matrix multiplication. [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 →

Space Filling Curves simplify communication-avoiding matrix multiplication

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Research paper detailing a novel algorithmic approach to matrix multiplication. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Evangelos Georganas, Alexander Heinecke, Pradeep Dubey ·

    Space Filling Curves is All You Need: Communication-Avoiding Matrix Multiplication Made Simple

    arXiv:2601.16294v3 Announce Type: replace-cross Abstract: General Matrix Multiplication (GEMM) is the cornerstone of HPC workloads and Deep Learning. State-of-the-art (SOTA) vendor libraries tune tensor layouts, parallelization schemes and cache blocking to minimize data movement…

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