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research-article

MLIR: scaling compiler infrastructure for domain specific computation

Authors:

Uday Bondhugula,

Jacques Pienaar,

Tatiana Shpeisman,

Nicolas Vasilache,

Oleksandr ZinenkoAuthors Info & Claims

CGO '21: Proceedings of the 2021 IEEE/ACM International Symposium on Code Generation and Optimization

Pages 2 - 14

https://doi.org/10.1109/CGO51591.2021.9370308

Published: 17 September 2021 Publication History

Abstract

This work presents MLIR, a novel approach to building reusable and extensible compiler infrastructure. MLIR addresses software fragmentation, compilation for heterogeneous hardware, significantly reducing the cost of building domain specific compilers, and connecting existing compilers together.

MLIR facilitates the design and implementation of code generators, translators and optimizers at different levels of abstraction and across application domains, hardware targets and execution environments. The contribution of this work includes (1) discussion of MLIR as a research artifact, built for extension and evolution, while identifying the challenges and opportunities posed by this novel design, semantics, optimization specification, system, and engineering. (2) evaluation of MLIR as a generalized infrastructure that reduces the cost of building compilers---describing diverse use-cases to show research and educational opportunities for future programming languages, compilers, execution environments, and computer architecture. The paper also presents the rationale for MLIR, its original design principles, structures and semantics.

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Leißa RUllrich MMeyer JHack S(2025)MimIR: An Extensible and Type-Safe Intermediate Representation for the DSL AgeProceedings of the ACM on Programming Languages10.1145/37048409:POPL(95-125)Online publication date: 9-Jan-2025
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Cheon SLee YKim DLee JJung SKim TLee DKim HBalzarotti DXu W(2024)DaCapoProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699291(6993-7010)Online publication date: 14-Aug-2024
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Archer AFahrbach MLiu KPrabhu PSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Practical performance guarantees for pipelined DNN inferenceProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692137(1655-1671)Online publication date: 21-Jul-2024
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MLIR: scaling compiler infrastructure for domain specific computation
1. Software and its engineering
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cover image ACM Conferences

CGO '21: Proceedings of the 2021 IEEE/ACM International Symposium on Code Generation and Optimization

February 2021

395 pages

ISBN:9781728186139

General Chair:
Jae W. Lee
Seoul National University, South Korea

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IEEE Press

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Published: 17 September 2021

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CGO '21: 19th ACM/IEEE International Symposium on Code Generation and Optimization

February 27 - March 3, 2021

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Leißa RUllrich MMeyer JHack S(2025)MimIR: An Extensible and Type-Safe Intermediate Representation for the DSL AgeProceedings of the ACM on Programming Languages10.1145/37048409:POPL(95-125)Online publication date: 9-Jan-2025
https://dl.acm.org/doi/10.1145/3704840
Cheon SLee YKim DLee JJung SKim TLee DKim HBalzarotti DXu W(2024)DaCapoProceedings of the 33rd USENIX Conference on Security Symposium10.5555/3698900.3699291(6993-7010)Online publication date: 14-Aug-2024
https://dl.acm.org/doi/10.5555/3698900.3699291
Archer AFahrbach MLiu KPrabhu PSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Practical performance guarantees for pipelined DNN inferenceProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692137(1655-1671)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692137
Ferikoglou AKakolyris AMasouros DSoudris DXydis S(2024)CollectiveHLS: A Collaborative Approach to High-Level Synthesis Design OptimizationACM Transactions on Reconfigurable Technology and Systems10.1145/370200518:1(1-32)Online publication date: 26-Oct-2024
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Nickel MGöhringer D(2024)A Survey on Architectures, Hardware Acceleration and Challenges for In-Network ComputingACM Transactions on Reconfigurable Technology and Systems10.1145/369951418:1(1-34)Online publication date: 10-Oct-2024
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Prasad ARajendra SRajan KGovindarajan RBondhugula UWitchel EArpaci-Dusseau ARossbach CKeeton K(2024)SilvanForge: A Schedule-Guided Retargetable Compiler for Decision Tree InferenceProceedings of the ACM SIGOPS 30th Symposium on Operating Systems Principles10.1145/3694715.3695958(488-504)Online publication date: 4-Nov-2024
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Bao GShi HCui CZhang YYao JFilkov VRay BZhou M(2024)UFront: Toward A Unified MLIR Frontend for Deep LearningProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695002(255-267)Online publication date: 27-Oct-2024
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Kim CLi PMohan AButt ASampson ANigam R(2024)Unifying Static and Dynamic Intermediate Languages for Accelerator GeneratorsProceedings of the ACM on Programming Languages10.1145/36897908:OOPSLA2(2242-2267)Online publication date: 8-Oct-2024
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Klopp DErdweg SPacak A(2024)A Typed Multi-level Datalog IR and Its Compiler FrameworkProceedings of the ACM on Programming Languages10.1145/36897678:OOPSLA2(1586-1614)Online publication date: 8-Oct-2024
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Jungmair MEngelke AGiceva J(2024)HiPy: Extracting High-Level Semantics from Python Code for Data ProcessingProceedings of the ACM on Programming Languages10.1145/36897378:OOPSLA2(736-762)Online publication date: 8-Oct-2024
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