ADAPT: Algorithmic Differentiation Applied to Floating-Point Precision Tuning

Authors: Harshitha Menon (Lawrence Livermore National Laboratory), Michael O. Lam (James Madison University, Lawrence Livermore National Laboratory), Daniel Osei-Kuffuor (Lawrence Livermore National Laboratory), Markus Schordan (Lawrence Livermore National Laboratory), Scott Lloyd (Lawrence Livermore National Laboratory), Kathryn Mohror (Lawrence Livermore National Laboratory), Jeffrey Hittinger (Lawrence Livermore National Laboratory)

Abstract: HPC applications extensively use floating point arithmetic operations to solve computational problems in various domains. Mixed precision computing, use of lowest precision data type sufficient to achieve a desired accuracy, have been explored to improve performance, reduce power consumption and data movement. Manually optimizing the program to use mixed precision is challenging. In this work, we present ADAPT, an approach for mixed precision analysis on HPC workloads while providing guarantees about the final output error. Our approach uses algorithmic differentiation to accurately estimate the output error for mixed precision configuration. ADAPT provides floating-point precision sensitivity of programs, which highlights regions of the code that that can potentially be converted to lower precision, is used to make algorithmic choices and develop mixed precision configurations. We evaluate ADAPT on six benchmarks and a proxy application and show that we are able to achieve a speedup of 1.2x on the proxy application, LULESH.


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