Dynamic Task-Scheduling and Resource Management for GPU Accelerators in Medical Imaging
For medical imaging applications, a timely execution of tasks is essential. Hence, running multiple applications on the same system, scheduling with the capability of task preemption and prioritization becomes mandatory. Using GPUs as accelerators in this domain, imposes new challenges since GPU’s common FIFO scheduling does not support task prioritization and preemption. As a remedy, this paper investigates the employment of resource management and scheduling techniques for applications from the medical domain for GPU accelerators. A scheduler supporting both, priority-based and LDF scheduling is added to the system such that high-priority tasks can interrupt tasks already enqueued for execution. The scheduler is capable of utilizing multiple GPUs in a system to minimize the average response time of applications. Moreover, it supports simultaneous execution of multiple tasks to hide data transfers latencies. We show that the scheduler interrupts scheduled and already enqueued applications to fulfill the timing requirements of high-priority dynamic tasks.
KeywordsDynamic task-scheduling Resource management GPU CUDA Medical imaging
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