RAO-SS: A Prototype of Run-time Auto-tuning Facility for Sparse Direct Solvers
Mathematical Software
2024-08-23 v1 Performance
Abstract
In this paper, a run-time auto-tuning method for performance parameters according to input matrices is proposed. RAO-SS (Run-time Auto-tuning Optimizer for Sparse Solvers), which is a prototype of auto-tuning software using the proposed method, is also evaluated. The RAO-SS is implemented with the Autopilot, which is middle-ware to support run-time auto-tuning with fuzzy logic function. The target numerical library is the SuperLU, which is a sparse direct solver for linear equations. The result indicated that: (1) the speedup factors of 1.2 for average and 3.6 for maximum to default executions were obtained; (2) the software overhead of the Autopilot can be ignored in RAO-SS.
Cite
@article{arxiv.2408.11880,
title = {RAO-SS: A Prototype of Run-time Auto-tuning Facility for Sparse Direct Solvers},
author = {Takahiro Katagiri and Yoshinori Ishii and Hiroki Honda},
journal= {arXiv preprint arXiv:2408.11880},
year = {2024}
}