CC 2022 – Proceedings

Frontmatter

Keynote

Writing and Verifying a Quantum Optimizing Compiler (Keynote)
Robert Rand
(University of Chicago, USA)

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Quantum Computing and Hardware Design

QSSA: An SSA-Based IR for Quantum Computing
Anurudh Peduri, Siddharth Bhat, and Tobias Grosser
(IIIT Hyderabad, India; University of Edinburgh, UK)

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QRANE: Lifting QASM Programs to an Affine IR
Blake Gerard, Tobias Grosser, and Martin Kong
(University of Oklahoma, USA; University of Edinburgh, UK)

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A Polynomial Time Exact Solution to the Bit-Aware Register Binding Problem
Michael Canesche, Ricardo Ferreira, José Augusto Nacif, and Fernando Magno Quintão Pereira
(Federal University of Minas Gerais, Brazil; Federal University of Viçosa, Brazil)

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Compiler Theory

Graph Transformations for Register-Pressure-Aware Instruction Scheduling
Ghassan Shobaki, Justin Bassett, Mark Heffernan, and Austin Kerbow
(California State University at Sacramento, USA; Google, USA)

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Caviar: An E-Graph Based TRS for Automatic Code Optimization
Smail Kourta, Adel Abderahmane Namani, Fatima Benbouzid-Si Tayeb, Kim Hazelwood, Chris Cummins, Hugh Leather, and Riyadh Baghdadi
(NYU Abu Dhabi, United Arab Emirates; École nationale supérieure d'informatique, Algeria; Meta AI, USA)

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On the Computation of Interprocedural Weak Control Closure
Abu Naser Masud and Björn Lisper
(Mälardalen University, Sweden)

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Seamless Deductive Inference via Macros
Arash Sahebolamri, Thomas Gilray, and Kristopher Micinski
(Syracuse University, USA; University of Alabama at Birmingham, USA)

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Compilers and Machine Learning

One-Shot Tuner for Deep Learning Compilers
Jaehun Ryu, Eunhyeok Park, and Hyojin Sung
(POSTECH, South Korea)

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Training of Deep Learning Pipelines on Memory-Constrained GPUs via Segmented Fused-Tiled Execution
Yufan Xu, Saurabh Raje, Atanas Rountev, Gerald Sabin, Aravind Sukumaran-Rajam, and P. Sadayappan
(University of Utah, USA; Ohio State University, USA; RNET Technologies, USA; Washington State University, USA)

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MLIR-Based Code Generation for GPU Tensor Cores
Navdeep Katel, Vivek Khandelwal, and Uday Bondhugula
(Indian Institute of Science, India; PolyMage Labs, India)

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Automating Reinforcement Learning Architecture Design for Code Optimization
Huanting Wang, Zhanyong Tang, Cheng Zhang, Jiaqi Zhao, Chris Cummins, Hugh Leather, and Zheng Wang
(NorthWest University, China; University of Leeds, UK; Meta AI, USA)

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Parallelism

Memory Access Scheduling to Reduce Thread Migrations
Sana Damani, Prithayan Barua, and Vivek Sarkar
(Georgia Institute of Technology, USA)

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Performant Portable OpenMP
Guray Ozen and Michael Wolfe
(NVIDIA, Germany; NVIDIA, USA)

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Safety and Correctness

BinPointer: Towards Precise, Sound, and Scalable Binary-Level Pointer Analysis
Sun Hyoung Kim, Dongrui Zeng, Cong Sun, and Gang Tan
(Pennsylvania State University, USA; Xidian University, China)

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Cape: Compiler-Aided Program Transformation for HTM-Based Cache Side-Channel Defense
Rui Zhang, Michael D. Bond, and Yinqian Zhang
(Ohio State University, USA; Southern University of Science and Technology, China)

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Making No-Fuss Compiler Fuzzing Effective
Alex Groce, Rijnard van Tonder, Goutamkumar Tulajappa Kalburgi, and Claire Le Goues
(Northern Arizona University, USA; Sourcegraph, USA; Carnegie Mellon University, USA)

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Performance Optimizations

Loner: Utilizing the CPU Vector Datapath to Process Scalar Integer Data
Armand Behroozi, Sunghyun Park, and Scott Mahlke
(University of Michigan, USA; NVIDIA Research, USA)

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Mapping Parallelism in a Functional IR through Constraint Satisfaction: A Case Study on Convolution for Mobile GPUs
Naums Mogers, Lu Li, Valentin Radu, and Christophe Dubach
(University of Edinburgh, UK; University of Sheffield, UK; McGill University, Canada)

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Software Pre-execution for Irregular Memory Accesses in the HBM Era
Sanyam Mehta, Gary Elsesser, and Terry Greyzck
(HPE, USA)

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Efficient Profile-Guided Size Optimization for Native Mobile Applications
Kyungwoo Lee, Ellis Hoag, and Nikolai Tillmann
(Meta, USA)

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