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Finding inputs that trigger floating-point exceptions in heterogeneous computing via Bayesian optimization.

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Title: Finding inputs that trigger floating-point exceptions in heterogeneous computing via Bayesian optimization.
Authors: Laguna, Ignacio1 (AUTHOR) lagunaperalt1@llnl.gov, Tran, Anh2 (AUTHOR), Gopalakrishnan, Ganesh2 (AUTHOR)
Source: Parallel Computing. Sep2023, Vol. 117, pN.PAG-N.PAG. 1p.
Subjects: Heterogeneous computing, Information resources
Abstract: Testing code for floating-point exceptions is crucial as exceptions can quickly propagate and produce unreliable numerical answers. The state-of-the-art to test for floating-point exceptions in heterogeneous systems is quite limited and solutions require the application's source code, which precludes their use in accelerated libraries where the source is not publicly available. We present an approach to find inputs that trigger floating-point exceptions in black-box CPU or GPU functions, i.e., functions where the source code and information about input bounds are unavailable. Our approach is the first to use Bayesian optimization (BO) to identify such inputs and uses novel strategies to overcome the challenges that arise in applying BO to this problem. We implement our approach in the Xscope framework and demonstrate it on 58 functions from the CUDA Math Library and 81 functions from the Intel Math Library. Xscope is able to identify inputs that trigger exceptions in about 73% of the tested functions. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Testing code for floating-point exceptions is crucial as exceptions can quickly propagate and produce unreliable numerical answers. The state-of-the-art to test for floating-point exceptions in heterogeneous systems is quite limited and solutions require the application's source code, which precludes their use in accelerated libraries where the source is not publicly available. We present an approach to find inputs that trigger floating-point exceptions in black-box CPU or GPU functions, i.e., functions where the source code and information about input bounds are unavailable. Our approach is the first to use Bayesian optimization (BO) to identify such inputs and uses novel strategies to overcome the challenges that arise in applying BO to this problem. We implement our approach in the Xscope framework and demonstrate it on 58 functions from the CUDA Math Library and 81 functions from the Intel Math Library. Xscope is able to identify inputs that trigger exceptions in about 73% of the tested functions. [ABSTRACT FROM AUTHOR]
ISSN:01678191
DOI:10.1016/j.parco.2023.103042