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Autotuning Apache TVM-Based Scientific Applications Using Bayesian Optimization

SessionWorkshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S)

DescriptionApache TVM (Tensor Virtual Machine), an open source machine learning compiler framework designed to optimize computations across various hardware platforms, provides an opportunity to improve the performance of dense matrix factorizations such as LU (Lower Upper) decomposition and Cholesky decomposition on GPUs, FPGAs, ASICs, and AI accelerators. In this paper, we propose a new TVM autotuning framework using Bayesian Optimization and use the TVM tensor expression language to implement linear algebra kernels such as LU, Cholesky, and 3mm. We use these scientific computation kernels to evaluate the effectiveness of our methods on a GPU cluster, called Swing, at Argonne National Laboratory. We compare the proposed autotuning framework with the TVM autotuning framework AutoTVM with four tuners and find that our framework outperforms AutoTVM in most cases.

Author/Presenters

Xingfu Wu

Argonne National Laboratory

University of Chicago

Praveen Paramasivam

University of South Dakota