Login


TitleTask Management for Irregular-Parallel Workloads on the GPU (In Proceedings)
inHigh Performance Graphics
Author(s) Stanley Tzeng, Anjul Patney, John D. Owens
Editor(s) Michael Doggett, Samuli Laine, Warren Hunt
Keyword(s)Task-management, Scheduling, Programmable Pipelines, Irregular-parallel workloads
Year 2010
LocationSaarbruecken, Germany
DateJune 25-27, 2010
PublisherEurographics Association
URLhttp://tinyurl.com/sched-hpg
Pages29--37
Download
BibTeX
Abstract We explore software mechanisms for managing irregular tasks on graphics processing units (GPUs). We demonstrate that dynamic scheduling and efficient memory management are critical problems in achieving high efficiency on irregular workloads. We experiment with several task-management techniques, ranging from the use of a single monolithic task queue to distributed queuing with task stealing and donation. On irregular workloads, we show that both centralized and distributed queues have more than 100 times as much idle times as our task-stealing and -donation queues. Our preferred choice is task-donation because of comparable performance to task-stealing while using less memory overhead. To help in this analysis, we use an artificial task-management system that monitors performance and memory usage to quantify the impact of these different techniques. We validate our results by implementing a Reyes renderer with its irregular split-and-dice workload that is able to achieve real-time framerates on a single GPU.