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ARXIV:2604.05066 · AI/HPC OPTIMIZATION · SUBMITTED 08 APR · 05:54 UTC · FRESHNESS UNKNOWN
ARXIV:2604.05066AI/HPC OPTIMIZATIONSUBMITTED 08 APR · 05:54 UTCFRESHNESS UNKNOWNYifan Zhu · Yekai Pan · Yanghui Wu · Chen Ding · arXiv
An open-source tool that automatically analyzes data locality and movement complexity in AI and HPC kernels to optimize performance.
Opportunity summary
Pain An open-source tool that automatically analyzes data locality and movement complexity in AI and HPC kernels to optimize performance.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
An open-source tool that automatically analyzes data locality and movement complexity in AI and HPC kernels to optimize performance. For loop-based programs common in high-performance computing (HPC) and AI workloads, including matrix multiplication, tensor…
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…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. The tool handles arbitrary affine loop nests, covering workloads such as tensor contractions, einsum expressions, stencil computations, and general polyhedral programs.
AI/HPC Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.
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An open-source tool that automatically analyzes data locality and movement complexity in AI and HPC kernels to optimize performance.
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10.48550/arXiv.2604.05066An open-source tool that automatically analyzes data locality and movement complexity in AI and HPC kernels to optimize performance.
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, avoiding both stack simulation and Denning's recursive working-set formulation. We describe the DSL syntax and its formal semantics, the polyhedral lowering pipeline that constructs timestamp spaces and access maps via affine transformations, and the sequence of Barvinok counting operations used to derive symbolic reuse-interval and reuse-distance distributions. The system is implemented in Rust as a modular library spanning three crates, with safe bindings to the Barvinok library. We provide both a command-line interface and an interactive web playground with LaTeX rendering of the output formulas. The tool handles arbitrary affine loop nests, covering workloads such as tensor contractions, einsum expressions, stencil computations, and general polyhedral programs.
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Dimensions overall score 3.0
PROBLEM
An open-source tool that automatically analyzes data locality and movement complexity in AI and HPC kernels to optimize performance. For loop-based programs common in high-performance computing (HPC) and AI workloads, including matrix multiplication, tensor contraction, stencil...
METHOD
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 thro...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. The tool handles arbitrary affine loop nests, covering workloads such as tensor contractions, einsum expressions, stencil computations, and general polyhedral programs.
WHY NOW
AI/HPC Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
An open-source tool that automatically analyzes data locality and movement complexity in AI and HPC kernels to optimize performance. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. The tool handles arbitrary affine loop nests, covering workloads such as tensor contractions, einsum expressions, stencil computations, and general polyhedral programs.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI/HPC Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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An open-source tool that automatically analyzes data locality and movement complexity in AI and HPC kernels to optimize performance.
Segment
AI/HPC Optimization
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Commercial read
3.0/10 public viability
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