Autotuning Apache TVM-based Scientific Applications Using Bayesian
  Optimization
Xingfu Wu Praveen Paramasivam Valerie Taylor
Apache 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 and AI (Artificial Intelligence) 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.