all AI news
Cost-Effective Methodology for Complex Tuning Searches in HPC: Navigating Interdependencies and Dimensionality
March 14, 2024, 4:42 a.m. | Adrian Perez Dieguez, Min Choi, Mahmut Okyay, Mauro Del Ben, Bryan M. Wong, Khaled Z. Ibrahim
cs.LG updates on arXiv.org arxiv.org
Abstract: Tuning searches are pivotal in High-Performance Computing (HPC), addressing complex optimization challenges in computational applications. The complexity arises not only from finely tuning parameters within routines but also potential interdependencies among them, rendering traditional optimization methods inefficient. Instead of scrutinizing interdependencies among parameters and routines, practitioners often face the dilemma of conducting independent tuning searches for each routine, thereby overlooking interdependence, or pursuing a more resource-intensive joint search for all routines. This decision is driven …
abstract applications arxiv challenges complexity computational computing cost cs.dc cs.lg dimensionality hpc methodology optimization parameters performance pivotal rendering them type
More from arxiv.org / cs.LG updates on arXiv.org
The Perception-Robustness Tradeoff in Deterministic Image Restoration
2 days, 12 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne