The Sparse Tensor Algebra Compiler Information Guide

  1. Overview on The Sparse Tensor Algebra Compiler
  2. Core Information
  3. Recent Updates
  4. Expert Insights
  5. Summary

Overview on The Sparse Tensor Algebra Compiler

The Sparse Tensor Algebra Compiler Guide
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Core Information

The Tensor Algebra Compiler News
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Recent Updates

Full MLIR Open Meeting 2022-02-10: PyTACO - An End-to-End Use Case for the Sparse Tensor Compiler News
Stay updated on The Sparse Tensor Algebra Compiler's latest milestones.

Format Abstraction for Sparse Tensor Algebra Compilers
Format Abstraction for Sparse Tensor Algebra Compilers
DISTAL: The Distributed Tensor Algebra Compiler
DISTAL: The Distributed Tensor Algebra Compiler
2021 LLVM Dev Mtg “Sparse Tensor Algebra Optimizations in MLIR”
2021 LLVM Dev Mtg “Sparse Tensor Algebra Optimizations in MLIR”
Rohan Yadav: DISTAL, The Distributed Tensor Algebra Compiler
Rohan Yadav: DISTAL, The Distributed Tensor Algebra Compiler
[OOPSLA] Compilation of Dynamic Sparse Tensor Algebra
[OOPSLA] Compilation of Dynamic Sparse Tensor Algebra
ICPP Keynote: Making Sparse Fast with the TACO Domain Specific Language for Sparse Tensor Algebra
ICPP Keynote: Making Sparse Fast with the TACO Domain Specific Language for Sparse Tensor Algebra
A Sparse Iteration Space Transformation Framework for Sparse Tensor Algebra
A Sparse Iteration Space Transformation Framework for Sparse Tensor Algebra
taco: A Tool to Generate Tensor Algebra Kernels
taco: A Tool to Generate Tensor Algebra Kernels
The Tensor Algebra Compiler
The Tensor Algebra Compiler
Brief Announcement: Sparse Tensor Transpositions
Brief Announcement: Sparse Tensor Transpositions
[OOPSLA24] Compiler Support for Sparse Tensor Convolutions
[OOPSLA24] Compiler Support for Sparse Tensor Convolutions

Expert Insights

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Last Updated: September 27, 2026

Summary

Information The TACO Sparse Tensor Algebra Compiler Guide
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Summary

Fredrik Kjolstad, Shoaib Kamil, Stephen Chou, David Lugato, Saman Amarasinghe Bixia Zheng‎ will talk about the MLIR-PyTACO bridge, which connects the PyTACO DSL to the MLIR Presented at GPCE 2018 Achieving high performance is no easy task. When it comes to program operating on Paper: dl.acm.org/citation.cfm?id=3276493 This paper shows how to build pldi22.sigplan.org/details/pldi-2022-pldi/12/DISTAL-The-Distributed- 2021 LLVM Developers' Meeting llvm.org/devmtg/2021-11/ — Many applications, from social network graph analytics to control flow analysis, compute on Automatic generation of high-performance

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