CGO 2019
2019 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)
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2019 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), February 16–20, 2019, Washington, DC, USA

CGO 2019 – Advance Table of Contents

Contents - Abstracts - Authors


Title Page

Message from the Chairs




Transforming Query Sequences for High-Throughput B+ Tree Processing on Many-Core Processors
Ruiqin Tian, Junqiao Qiu, Zhijia Zhao, Xu Liu, and Bin Ren
(College of William and Mary, USA; University of California at Riverside, USA)

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Tiramisu: A Polyhedral Compiler with a Scheduling Language for Targeting High Performance Systems
Riyadh Baghdadi, Jessica Ray, Malek Ben Romdhane, Emanuele Del Sozzo, Abdurrahman Akkas, Yunming Zhang, Patricia Suriana, Shoaib Kamil, and Saman Amarasinghe
(Massachusetts Institute of Technology, USA; Politecnico di Milano, Italy; Google, n.n.; Adobe, n.n.)

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Reasoning about the Node.js Event Loop using Async Graphs
Haiyang Sun, Daniele Bonetta, Filippo Schiavio, and Walter Binder
(USI Lugano, Switzerland; Oracle Labs, n.n.)

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Janus: Statically-Driven and Profile-Guided Automatic Dynamic Binary Parallelization
Ruoyu Zhou and Timothy M. Jones
(University of Cambridge, UK)

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Decoding CUDA Binary
Ari B. Hayes, Fei Hua, Jin Huang, Yanhao Chen, and Eddy Z. Zhang
(Rutgers University, USA)

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Safe Performance Test in Memory-Unsafe Languages
Marcus Rodrigues, Breno Guimarães, and Fernando Quintao
(Federal University of Minas Gerais, Brazil)
This paper presents a technique to generate in-bounds inputs for arrays used in memory-unsafe programming languages, such as C and C++. We show that most memory indexation found in actual C programs follows patterns that are easy to analyze statically. Based on this observation, we show how symbolic range analysis can be used to establish contracts between the arguments of a function and the arrays used within that function. To demonstrate the effectiveness of our ideas, we use them to implement Griffin-TG, a tool to stress-test C programs whose source code might be partially available. We show how Griffin-TG improves Aprof, a well-known algorithmic profiling tool, and we show how it lets us enrich Polybench with a large set of new inputs.
Preprint Info
White-Box Program Tuning
Wen-Chuan Lee, Yingqi Liu, Peng Liu, Shiqing Ma, Hongjun Choi, Xiangyu Zhang, and Rajiv Gupta
(Purdue University, USA; University of California at Riverside, USA)
Many programs or algorithms are largely parameterized, especially those based on heuristics. The quality of the results depends on the parameter setting. Different inputs often have different optimal settings. Program tuning is hence of great importance. Existing tuning techniques treat the the program as a black-box and hence cannot leverage the internal program states to achieve better tuning. We propose a white-box tuning technique that is implemented as a library. The user can compose complex program tuning tasks by adding a small number of library calls to the original program and providing a few callback functions. Our experiments on 13 widely-used real-world programs show that our technique substantially improves data processing results and outperforms OpenTuner, the state-of-the-art black-box tuning technique.
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From Loop Fusion to Kernel Fusion: A Domain-Specific Approach to Locality Optimization
Bo Qiao, Oliver Reiche, Frank Hannig, and Jürgen Teich
(University of Erlangen-Nuremberg, Germany)

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Function Merging by Sequence Alignment
Rodrigo C. O. Rocha, Pavlos Petoumenos, Zheng Wang, Murray Cole, and Hugh Leather
(University of Edinburgh, UK; Lancaster University, UK)

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Smokestack: Thwarting DOP Attacks with Runtime Stack Layout Randomization
Misiker Tadesse Aga and Todd Austin
(University of Michigan, USA)

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BOLT: A Practical Binary Optimizer for Data Centers and Beyond
Maksim Panchenko, Rafael Auler, Guilherme Ottoni, and Bill Nell
(Facebook, n.n.)

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Sparse Tensor Algebra Optimization with Workspaces
Fredrik Kjolstad, Peter Ahrens, Shoaib Kamil, and Saman Amarasinghe
(Massachusetts Institute of Technology, USA)

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A Code Generator for High-Performance Tensor Contractions on GPUs
Jinsung Kim, Aravind Sukumaran-Rajam, Vineeth Thumma, Sriram Krishnamoorthy, Ajay Panyala, Louis-Noël Pouchet, Atanas Rountev, and P. Sadayappan
(Ohio State University, USA; Pacific Northwest National Laboratory, USA; Colorado State University, USA)

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An Optimization-Driven Incremental Inline Substitution Algorithm for Just-in-Time Compilers
Aleksandar Prokopec, Gilles Duboscq, David Leopoldseder, and Thomas Würthinger
(Oracle Labs, n.n.; JKU Linz, Austria)

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Locus: A System and a Language for Program Optimization
Thiago S. F. X. Teixeira, Corinne Ancourt, David Padua, and William Gropp
(University of Illinois at Urbana-Champaign, USA; MINES ParisTech, France)

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IGC: The Open Source Intel Graphics Compiler
Weiyu Chen, Po-yu Chen, Guei-Yuan Lueh, Peng Guo, Wei Pan, Thomas F. Raoux, Pankaj Mistry, Gang Y. Chen, Shruthi Hebbur Prasanna Kumar, Junjie Gu, Konrad Trifunovic, and Anupama Chandrasekhar
(Intel, n.n.)

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Automatic Generation of Warp-Level Primitives and Atomic Operations for Fast-Portable GPU Reductions
Simon Garcia De Gonzalo, Sitao Huang, Juan Gomez-Luna, Simon Hammond, Onur Mutlu, and Wen-mei Hwu
(University of Illinois at Urbana-Champaign, USA; ETH Zurich, Switzerland; Sandia National Laboratories, n.n.)

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Automatic Equivalence Checking for Assembly Implementations of Cryptography Libraries
Jay P. Lim and Santosh Nagarakatte
(Rutgers University, USA)

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Super-Node SLP: Optimized Vectorization for Code Sequences Containing Operators and Their Inverse Elements
Vasileios Porpodas, Rodrigo C. O. Rocha, Evgueni Brevnov, Luís F. W. Góes, and Timothy Mattson
(Intel, n.n.; University of Edinburgh, UK; PUC Minas, Brazil)

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Causer: Context-Sensitive Overflow Detection
Hongyu Liu, Sam Silvestro, Xiaoyin Wang, Lide Duan, and Tongping Liu
(University of Texas at San Antonio, USA)

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Quantifying and Reducing Execution Variance in STM via Model Driven Commit Optimization
Girish Mururu, Ada Gavrilovska, and Santosh Pande
(Georgia Institute of Technology, USA)

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