In the research, the authors introduce a generalized list-scheduling algorithm to analyze the impact of various algorithmic components on performance and runtime. They evaluate 72 unique algorithms on different datasets, highlighting how individual components affect scheduling efficiency. The study reveals that certain combinations of components are pareto-optimal, showcasing their effectiveness in specific scenarios. The results emphasize the importance of considering task graph structure and communication intensity when selecting algorithmic components for scheduling tasks.
In eine andere Sprache
aus dem Quellinhalt
arxiv.org
Wichtige Erkenntnisse aus
by Jared Colema... um arxiv.org 03-13-2024
https://arxiv.org/pdf/2403.07112.pdfTiefere Fragen