GPU Scheduling for Real-Time Multi-Tasking Environments
TimeGraph: GPU Scheduling for Real-Time Multi-Tasking Environments Shinpei Kato† ‡, Karthik Lakshmanan†, and Ragunathan (Raj) Rajkumar†, Yutaka Ishikawa‡ † Department of Electrical and Computer Engineering, Carnegie Mellon University ‡ Department of Computer Science, The University of Tokyo Abstract transition. Especially recent trends on 3-D browser and desktop applications, such as SpaceTime, Web3D, 3D- The Graphics Processing Unit (GPU) is now commonly Desktop, Compiz Fusion, BumpTop, Cooliris, and Win- used for graphics and data-parallel computing. As more dows Aero, are all intriguing possibilities for future user and more applications tend to accelerate on the GPU in interfaces. GPUs are also leveraged in various domains multi-tasking environments where multiple tasks access of general-purpose GPU (GPGPU) processing to facili- the GPU concurrently, operating systems must provide tate data-parallel compute-intensive applications. prioritization and isolation capabilities in GPU resource Real-time multi-tasking support is a key requirement management, particularly in real-time setups. for such emerging GPU applications. For example, users We present TimeGraph, a real-time GPU scheduler could launch multiple GPU applications concurrently in at the device-driver level for protecting important GPU their desktop computers, including games, video play- workloads from performance interference. TimeGraph ers, web browsers, and live messengers, sharing the same adopts a new event-driven model that synchronizes the GPU. In such a case, quality-aware soft real-time ap- GPU with the CPU to monitor GPU commands issued plications like games and video players should be pri- from the user space and control GPU resource usage in oritized over live messengers and any other applications a responsive manner.
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