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2024 IEEE/ACM International Symposium on Code Generation and Optimization (CGO),
March 02–06, 2024,
Edinburgh, United Kingdom
Frontmatter
Compilers for Machine Learning
PolyTOPS: Reconfigurable and Flexible Polyhedral Scheduler
Gianpietro Consolaro, Zhen Zhang, Harenome Razanajato, Nelson Lossing, Nassim Tchoulak, Adilla Susungi, Artur Cesar Araujo Alves, Renwei Zhang, Denis Barthou, Corinne Ancourt, and Cédric Bastoul
(Huawei Technologies, France; Mines Paris-PSL, France; Huawei Technologies, China)
Published Artifact
Artifacts Available
Artifacts Reusable
Results Reproduced
Machine-Learning Guided Optimizations
Compilers for GPUs
Custom Processors
AXI4MLIR: User-Driven Automatic Host Code Generation for Custom AXI-Based Accelerators
Nicolas Bohm Agostini,
Jude Haris,
Perry Gibson,
Malith Jayaweera, Norm Rubin,
Antonino Tumeo,
José L. Abellán,
José Cano, and
David Kaeli
(Northeastern University, USA; Pacific Northwest National Laboratory, USA; University of Glasgow, United Kingdom; University of Murcia, Spain)
Published Artifact
Artifacts Available
Artifacts Reusable
Results Reproduced
Compiler Construction
Custom Environments
Static/Dynamic Analyses
Supporting Tools
Practice and Experience
Experiences Building an MLIR-Based SYCL Compiler
Ettore Tiotto,
Víctor Pérez, Whitney Tsang,
Lukas Sommer,
Julian Oppermann,
Victor Lomüller,
Mehdi Goli, and
James Brodman
(Intel Corporation, Canada; Codeplay Software, United Kingdom; Intel Corporation, USA)
Published Artifact
Artifacts Available
Acceleration Techniques
A System-Level Dynamic Binary Translator using Automatically-Learned Translation Rules
Jinhu Jiang, Chaoyi Liang, Rongchao Dong, Zhaohui Yang, Zhongjun Zhou, Wenwen Wang, Pen-Chung Yew, and Weihua Zhang
(Fudan University, China; University of Georgia, USA; University of Minnesota at Twin Cities, USA)
Instruction Scheduling for the GPU on the GPU
Ghassan Shobaki,
Pınar Muyan-Özçelik, Josh Hutton, Bruce Linck, Vladislav Malyshenko,
Austin Kerbow, Ronaldo Ramirez-Ortega, and Vahl Scott Gordon
(California State University, Sacramento, USA; Advanced Micro Devices, USA)
Published Artifact
Artifacts Available
Artifacts Functional
oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation
Jianhui Li, Zhennan Qin, Yijie Mei, Jingze Cui, Yunfei Song, Ciyong Chen, Yifei Zhang, Longsheng Du, Xianhang Cheng, Baihui Jin, Yan Zhang, Jason Ye, Eric Lin, and Dan Lavery
(Intel, USA; Intel, China)
Published Artifact
Artifacts Available
Artifacts Reusable
Results Reproduced
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