RAO-SS: A Prototype of Run-time Auto-tuning Facility for Sparse Direct Solvers
<|reference_start|>RAO-SS: A Prototype of Run-time Auto-tuning Facility for Sparse Direct Solvers: 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.<|reference_end|>
@article{katagiri2024rao-ss:,
    title={RAO-SS: A Prototype of Run-time Auto-tuning Facility for Sparse Direct
  Solvers},
    author={Takahiro Katagiri, Yoshinori Ishii, Hiroki Honda},
    journal={arXiv preprint arXiv:2408.11880},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11880},
    primaryClass={cs.MS cs.PF}
}