Tenspiler: A Verified Lifting-Based Compiler for Tensor Operations
This program is tentative and subject to change.
Tensor processing infrastructures such as deep learning frameworks and specialized hardware accelerators have revolutionized how computationally intensive code from domains such as deep learning and image processing is executed and optimized. These infrastructures provide powerful and expressive abstractions while ensuring high performance. However, to utilize them, code must be written specifically using the APIs / ISAs of such software frameworks or hardware accelerators. Importantly, given the fast pace of innovation in these domains, code written today quickly becomes legacy as new frameworks and accelerators are developed, and migrating such legacy code manually is a considerable effort.
To enable developers in leveraging such DSLs while preserving their current programming paradigm, we describe our experience in building Tenspiler, a verified lifting-based compiler that uses program synthesis to translate sequential programs written in general-purpose programming languages (e.g., C++ or Python code that does not leverage any specialized framework or accelerator) into tensor operations. Central to Tenspiler is our carefully crafted yet simple intermediate language, named TensIR, that expresses tensor operations. TensIR enables efficient lifting, verification, and code generation. Unlike classical pattern-matching-based compilers, Tenspiler uses program synthesis to translate input code into TensIR, which is then compiled to the target API / ISA. Currently, Tenspiler already supports \textbf{six} distinct DSLs, spanning a broad spectrum of software and hardware environments. Furthermore, we show that new backends can be easily supported by Tenspiler by adding simple pattern-matching rules for TensIR. Using 10 real-world code benchmark suites, our experimental evaluation shows that by translating code to be executed on 6 different software frameworks and hardware devices, Tenspiler offers on average \textbf{105$\times$} kernel and \textbf{9.65$\times$} end-to-end execution time improvement over the fully-optimized sequential implementation of the same benchmarks.
This program is tentative and subject to change.
Wed 18 SepDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
13:30 - 15:00 | |||
13:30 15mTalk | A Dynamic Logic for Symbolic Execution for the Smart Contract Programming Language Michelson Technical Papers Barnabas Arvay University of Freiburg, Thi Thu Ha Doan University of Freiburg, Peter Thiemann University of Freiburg, Germany | ||
13:45 15mTalk | Qafny: A Quantum-Program Verifier Technical Papers Liyi Li Iowa State University, Mingwei Zhu University of Maryland, College Park, Rance Cleaveland University of Maryland, Alexander Nicolellis Iowa State University, Yi Lee University of Maryland, College Park, Le Chang University of Maryland, College Park, Xiaodi Wu University of Maryland | ||
14:00 15mTalk | Verifying Lock-free Search Structure Templates Technical Papers Nisarg Patel New York University, Dennis Shasha New York University, Thomas Wies New York University | ||
14:15 15mTalk | Mover Logic: A Concurrent Program Logic for Reduction and Rely-Guarantee Reasoning Technical Papers | ||
14:30 15mTalk | Compositional Symbolic Execution for Correctness and Incorrectness Reasoning Technical Papers Andreas Lööw Imperial College London, Daniele Nantes-Sobrinho Imperial College London, Sacha-Élie Ayoun Imperial College London, Caroline Cronjäger Ruhr-Universität Bochum, Petar Maksimović Imperial College London, UK, Philippa Gardner Imperial College London | ||
14:45 15mTalk | Tenspiler: A Verified Lifting-Based Compiler for Tensor Operations Technical Papers Jie Qiu Duolingo, Colin Cai University of California, Berkeley, Sahil Bhatia University of California, Berkeley, Niranjan Hasabnis Intel Labs, Sanjit Seshia UC Berkeley, Alvin Cheung University of California at Berkeley |