Data-Aware Task Scheduling on Multi-accelerator Based Platforms

To fully tap into the potential of heterogeneous machines composed of multicore processors and multiple accelerators, simple offloading approaches in which the main trunk of the application runs on regular cores while only specific parts are offloaded on accelerators are not sufficient. The real challenge is to build systems where the application would permanently spread across the entire machine, that is, where parallel tasks would be dynamically scheduled over the full set of available processing units. To face this challenge, we previously proposed StarPU, a runtime system capable of scheduling tasks over multicore machines equipped with GPU accelerators. StarPU uses a software virtual shared memory (VSM) that provides a highlevel programming interface and automates data transfers between processing units so as to enable a dynamic scheduling of tasks. We now present how we have extended StarPU to minimize the cost of transfers between processing units in order to efficiently cope wi...

• Type: Journal
• Year: 2010
• Where: ICPADS
• Authors: Cédric Augonnet, Jérôme Clet-Ortega, Samuel Thibault, Raymond Namyst
• Keywords: Distributed And Parallel Computing, ICPADS 2010, Machines, Multicore Machines, Multiple Accelerators