CGO 2022
2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)
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2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), April 02–06, 2022, Seoul, South Korea

CGO 2022 – Proceedings

Contents - Abstracts - Authors

Frontmatter

Title Page
Welcome from the General Chair
Welcome from the Program Chairs
CGO 2022 Organization
Report from the Artifact Evaluation Committee
CGO 2022 Sponsors

GPU

A Compiler Framework for Optimizing Dynamic Parallelism on GPUs
Mhd Ghaith Olabi, Juan Gómez Luna, Onur Mutlu, Wen-mei Hwu, and Izzat El Hajj
(American University of Beirut, Lebanon; ETH Zurich, Switzerland; University of Illinois at Urbana-Champaign, USA; NVIDIA, USA)
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Automatic Horizontal Fusion for GPU Kernels
Ao Li, Bojian Zheng, Gennady Pekhimenko, and Fan Long
(Carnegie Mellon University, USA; University of Toronto, Canada)
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DARM: Control-Flow Melding for SIMT Thread Divergence Reduction
Charitha Saumya, Kirshanthan Sundararajah, and Milind KulkarniORCID logo
(Purdue University, USA)
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Efficient Execution of OpenMP on GPUs
Joseph Huber, Melanie Cornelius, Giorgis Georgakoudis, Shilei Tian, Jose M Monslave Diaz, Kuter Dinel, Barbara Chapman, and Johannes Doerfert
(Oak Ridge National Laboratory, USA; Illinois Institute of Technology, USA; Lawrence Livermore National Laboratory, USA; Stony Brook University, USA; Argonne National Laboratory, USA; Düzce University, Turkey)
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Domain-Specific Compilation

GraphIt to CUDA Compiler in 2021 LOC: A Case for High-Performance DSL Implementation via Staging with BuilDSL
Ajay BrahmakshatriyaORCID logo and Saman AmarasingheORCID logo
(Massachusetts Institute of Technology, USA)
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A Compiler for Sound Floating-Point Computations using Affine Arithmetic
Joao Rivera, Franz Franchetti, and Markus Püschel
(ETH Zurich, Switzerland; Carnegie Mellon University, USA)
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Aggregate Update Problem for Multi-clocked Dataflow Languages
Hannes Kallwies, Martin Leucker, Torben Scheffel, Malte Schmitz, and Daniel Thoma
(University of Lübeck, Germany)
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Performance

CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research
Chris Cummins, Bram Wasti, Jiadong Guo, Brandon Cui, Jason Ansel, Sahir Gomez, Somya Jain, Jia Liu, Olivier Teytaud, Benoit Steiner, Yuandong Tian, and Hugh Leather
(Meta, USA)
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PALMED: Throughput Characterization for Superscalar Architectures
Nicolas Derumigny, Théophile Bastian, Fabian Gruber, Guillaume Iooss, Christophe Guillon, Louis-Noël Pouchet, and Fabrice Rastello
(Grenoble Alps University, France; Inria, France; CNRS, France; Grenoble INP, France; Colorado State University, USA; STMicroelectronics, France)
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SRTuner: Effective Compiler Optimization Customization by Exposing Synergistic Relations
Sunghyun Park, Salar Latifi, Yongjun Park, Armand Behroozi, Byungsoo Jeon, and Scott Mahlke
(University of Michigan at Ann Arbor, USA; Hanyang University, South Korea; Carnegie Mellon University, USA)
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Binary Techniques

Recovering Container Class Types in C++ Binaries
Xudong Wang, Xuezheng Xu, Qingan Li, Mengting Yuan ORCID logo, and Jingling Xue
(UNSW, Australia; Wuhan University, China)
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Automatic Generation of Debug Headers through BlackBox Equivalence Checking
Vaibhav Kiran Kurhe, Pratik Karia, Shubhani Gupta, Abhishek Rose, and Sorav Bansal
(IIT Delhi, India)
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Gadgets Splicing: Dynamic Binary Transformation for Precise Rewriting
Linan Tian, Yangyang Shi, Liwei Chen, Yanqi Yang, and Gang Shi
(Institute of Information Engineering at Chinese Academy of Sciences, China; University of Chinese Academy of Sciences, China)
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IR, Encryption, and Compression

Lambda the Ultimate SSA: Optimizing Functional Programs in SSA
Siddharth Bhat and Tobias Grosser ORCID logo
(IIIT Hyderabad, India; University of Edinburgh, UK)
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NOELLE Offers Empowering LLVM Extensions
Angelo Matni, Enrico Armenio Deiana, Yian Su, Lukas Gross, Souradip Ghosh, Sotiris Apostolakis, Ziyang Xu, Zujun Tan, Ishita Chaturvedi, Brian Homerding, Tommy McMichen, David I. August, and Simone Campanoni ORCID logo
(Northwestern University, USA; Princeton University, USA)
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HECATE: Performance-Aware Scale Optimization for Homomorphic Encryption Compiler
Yongwoo Lee, Seonyeong Heo, Seonyoung Cheon, Shinnung Jeong, Changsu Kim, Eunkyung Kim, Dongyoon Lee, and Hanjun Kim
(Yonsei University, South Korea; ETH Zurich, Switzerland; Seoul National University, South Korea; Samsung SDS, South Korea; Stony Brook University, USA)
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Unified Compilation for Lossless Compression and Sparse Computing
Daniel Donenfeld, Stephen Chou, and Saman AmarasingheORCID logo
(Massachusetts Institute of Technology, USA)
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Program Analysis and Optimization

Loop Rolling for Code Size Reduction
Rodrigo C. O. Rocha, Pavlos Petoumenos ORCID logo, Björn Franke, Pramod Bhatotia, and Michael O'Boyle ORCID logo
(University of Edinburgh, UK; University of Manchester, UK; TU Munich, Germany)
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Solving PBQP-Based Register Allocation using Deep Reinforcement Learning
Minsu Kim, Jeong-Keun Park, and Soo-Mook Moon
(Seoul National University, South Korea)
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F3M: Fast Focused Function Merging
Sean Stirling, Rodrigo C. O. Rocha, Kim Hazelwood, Hugh Leather, Michael O'Boyle ORCID logo, and Pavlos Petoumenos ORCID logo
(Codeplay, UK; University of Edinburgh, UK; Facebook, USA; University of Manchester, UK)
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Sound, Precise, and Fast Abstract Interpretation with Tristate Numbers
Harishankar Vishwanathan, Matan Shachnai, Srinivas Narayana, and Santosh NagarakatteORCID logo
(Rutgers University, USA)
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Natural-Language Techniques

M3V: Multi-modal Multi-view Context Embedding for Repair Operator Prediction
Xuezheng Xu, Xudong Wang, and Jingling Xue
(UNSW, Australia)
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Enabling Near Real-Time NLU-Driven Natural Language Programming through Dynamic Grammar Graph-Based Translation
Zifan Nan, Xipeng ShenORCID logo, and Hui Guan
(North Carolina State University, USA; University of Massachusetts at Amherst, USA)
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AI Systems

SPNC: An Open-Source MLIR-Based Compiler for Fast Sum-Product Network Inference on CPUs and GPUs
Lukas SommerORCID logo, Cristian Axenie, and Andreas Koch ORCID logo
(TU Darmstadt, Germany; Huawei Research, Germany)
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Distill: Domain-Specific Compilation for Cognitive Models
Jan Vesely, Raghavendra Pradyumna Pothukuchi, Ketaki Joshi, Samyak Gupta, Jonathan D. Cohen, and Abhishek Bhattacharjee
(Yale University, USA; Princeton University, USA)
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Optimizing GPU Deep Learning Operators with Polyhedral Scheduling Constraint Injection
Cédric BastoulORCID logo, Zhen Zhang, Harenome Razanajato, Nelson Lossing, Adilla Susungi, Javier de Juan, Etienne Filhol, Baptiste Jarry, Gianpietro Consolaro, and Renwei Zhang
(Huawei Technologies, France; Huawei Technologies, China)
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Comprehensive Accelerator-Dataflow Co-design Optimization for Convolutional Neural Networks
Miheer Vaidya, Aravind Sukumaran-Rajam ORCID logo, Atanas Rountev, and P. Sadayappan ORCID logo
(University of Utah, USA; Washington State University, USA; Ohio State University, USA)
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