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PLDI 2021
Sun 20 - Sat 26 June 2021 PLDI

Widely used Deep Learning (DL) frameworks, such as TensorFlow, PyTorch, and MXNet, heavily rely on the NVIDIA cuDNN for performance. However, using cuDNN does not always give the best performance. One reason is that it is hard to handle every case of versatile DNN models and GPU architectures with a library that has a fixed implementation. Another reason is that cuDNN lacks kernel fusion functionality that gives a lot of chances to improve performance. In this paper, we propose a DL optimization framework for versatile GPU workloads, called DeepCuts. It considers both kernel implementation parameters and GPU architectures. It analyzes the DL workload, groups multiple DL operations into a single GPU kernel, and generates optimized GPU kernels considering kernel implementation parameters and GPU architecture parameters. The evaluation result with various DL workloads for inference and training indicates that DeepCuts outperforms cuDNN/cuBLAS-based implementations and the state-of-the-art DL optimization frameworks, such as TVM, TensorFlow XLA, and TensorRT.

Fri 25 Jun

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09:00 - 09:40
Talks 5A: Machine Learning and Probabilistic ProgrammingPLDI at PLDI-A +12h
09:00
5m
Talk
DeepCuts: A Deep Learning Optimization Framework for Versatile GPU Workloads
PLDI
Wookeun Jung Seoul National University, Thanh Tuan Dao Seoul National University, Jaejin Lee Seoul National University
DOI
09:05
5m
Talk
Provable Repair of Deep Neural Networks
PLDI
Matthew Sotoudeh University of California at Davis, Aditya V. Thakur University of California at Davis
DOI Pre-print Media Attached
09:10
5m
Talk
DreamCoder: Bootstrapping Inductive Program Synthesis with Wake-Sleep Library Learning
PLDI
Kevin Ellis Cornell University, Lionel Wong Massachusetts Institute of Technology, Maxwell Nye Massachusetts Institute of Technology, Mathias Sablé-Meyer PSL University; Collège de France; NeuroSpin, Lucas Morales Massachusetts Institute of Technology, Luke Hewitt Massachusetts Institute of Technology, Luc Cary Massachusetts Institute of Technology, Armando Solar-Lezama Massachusetts Institute of Technology, Joshua B. Tenenbaum Massachusetts Institute of Technology
DOI
09:15
5m
Talk
Specification Synthesis with Constrained Horn Clauses
PLDI
Sumanth Prabhu TCS Research, Grigory Fedyukovich Florida State University, Kumar Madhukar TCS Research, Deepak D'Souza IISc Bangalore
DOI
09:20
5m
Talk
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming
PLDI
Guillaume Baudart Inria, Javier Burroni University of Massachusetts Amherst, Martin Hirzel IBM Research, Louis Mandel IBM Research, USA, Avraham Shinnar IBM Research
DOI
09:25
5m
Talk
Sound Probabilistic Inference via Guide Types
PLDI
Di Wang Carnegie Mellon University, Jan Hoffmann Carnegie Mellon University, Thomas Reps University of Wisconsin
DOI
09:30
5m
Talk
SPPL: Probabilistic Programming with Fast Exact Symbolic Inference
PLDI
Feras Saad Massachusetts Institute of Technology, Martin C. Rinard Massachusetts Institute of Technology, Vikash K. Mansinghka Massachusetts Institute of Technology
DOI
09:35
5m
Talk
Quantitative Analysis of Assertion Violations in Probabilistic Programs
PLDI
Jinyi Wang Shanghai Jiao Tong University, Yican Sun Peking University, Hongfei Fu Shanghai Jiao Tong University, Krishnendu Chatterjee IST Austria, Amir Kafshdar Goharshady Hong Kong University of Science and Technology
DOI
21:00 - 21:40
Talks 5A: Machine Learning and Probabilistic ProgrammingPLDI at PLDI-A
21:00
5m
Talk
DeepCuts: A Deep Learning Optimization Framework for Versatile GPU Workloads
PLDI
Wookeun Jung Seoul National University, Thanh Tuan Dao Seoul National University, Jaejin Lee Seoul National University
DOI
21:05
5m
Talk
Provable Repair of Deep Neural Networks
PLDI
Matthew Sotoudeh University of California at Davis, Aditya V. Thakur University of California at Davis
DOI Pre-print Media Attached
21:10
5m
Talk
DreamCoder: Bootstrapping Inductive Program Synthesis with Wake-Sleep Library Learning
PLDI
Kevin Ellis Cornell University, Lionel Wong Massachusetts Institute of Technology, Maxwell Nye Massachusetts Institute of Technology, Mathias Sablé-Meyer PSL University; Collège de France; NeuroSpin, Lucas Morales Massachusetts Institute of Technology, Luke Hewitt Massachusetts Institute of Technology, Luc Cary Massachusetts Institute of Technology, Armando Solar-Lezama Massachusetts Institute of Technology, Joshua B. Tenenbaum Massachusetts Institute of Technology
DOI
21:15
5m
Talk
Specification Synthesis with Constrained Horn Clauses
PLDI
Sumanth Prabhu TCS Research, Grigory Fedyukovich Florida State University, Kumar Madhukar TCS Research, Deepak D'Souza IISc Bangalore
DOI
21:20
5m
Talk
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming
PLDI
Guillaume Baudart Inria, Javier Burroni University of Massachusetts Amherst, Martin Hirzel IBM Research, Louis Mandel IBM Research, USA, Avraham Shinnar IBM Research
DOI
21:25
5m
Talk
Sound Probabilistic Inference via Guide Types
PLDI
Di Wang Carnegie Mellon University, Jan Hoffmann Carnegie Mellon University, Thomas Reps University of Wisconsin
DOI
21:30
5m
Talk
SPPL: Probabilistic Programming with Fast Exact Symbolic Inference
PLDI
Feras Saad Massachusetts Institute of Technology, Martin C. Rinard Massachusetts Institute of Technology, Vikash K. Mansinghka Massachusetts Institute of Technology
DOI
21:35
5m
Talk
Quantitative Analysis of Assertion Violations in Probabilistic Programs
PLDI
Jinyi Wang Shanghai Jiao Tong University, Yican Sun Peking University, Hongfei Fu Shanghai Jiao Tong University, Krishnendu Chatterjee IST Austria, Amir Kafshdar Goharshady Hong Kong University of Science and Technology
DOI