AutoLALA: Automatic Loop Algebraic Locality Analysis for AI and HPC Kernels
Yifan Zhu, Yekai Pan, Yanghui Wu, Chen Ding

TL;DR
AutoLALA is an open-source tool that performs symbolic data locality analysis for affine loop programs, aiding optimization in HPC and AI workloads by accurately modeling data movement costs.
Contribution
It introduces a formal, polyhedral-based analysis framework with a DSL, enabling precise reuse distance and data movement complexity calculations.
Findings
AutoLALA computes reuse distance without stack simulation.
It handles arbitrary affine loop nests including tensor contractions and stencils.
Provides a user-friendly CLI and web interface with LaTeX output.
Abstract
Data movement is the primary bottleneck in modern computing systems. For loop-based programs common in high-performance computing (HPC) and AI workloads, including matrix multiplication, tensor contraction, stencil computation, and einsum operations, the cost of moving data through the memory hierarchy often exceeds the cost of arithmetic. This paper presents AutoLALA, an open-source tool that analyzes data locality in affine loop programs. The tool accepts programs written in a small domain-specific language (DSL), lowers them to polyhedral sets and maps, and produces closed-form symbolic formulas for reuse distance and data movement complexity. AutoLALA implements the fully symbolic locality analysis of Zhu et al. together with the data movement distance (DMD) framework of Smith et al. In particular, it computes reuse distance as the image of the access space under the access map,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
