Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications
Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications
Dynamic Warp Formation and Scheduling for Efficient GPU Control Flow
Proceedings of the 40th Annual IEEE/ACM International Symposium on Microarchitecture
Understanding the efficiency of ray traversal on GPUs
Proceedings of the Conference on High Performance Graphics 2009
CheCUDA: A Checkpoint/Restart Tool for CUDA Applications
PDCAT '09 Proceedings of the 2009 International Conference on Parallel and Distributed Computing, Applications and Technologies
NVCR: A Transparent Checkpoint-Restart Library for NVIDIA CUDA
IPDPSW '11 Proceedings of the 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum
Frameworks for GPU Accelerators: A comprehensive evaluation using 2D/3D image registration
SASP '11 Proceedings of the 2011 IEEE 9th Symposium on Application Specific Processors
Cooperative multitasking for heterogeneous accelerators in the Linux Completely Fair Scheduler
ASAP '11 Proceedings of the ASAP 2011 - 22nd IEEE International Conference on Application-specific Systems, Architectures and Processors
Voxel-based 2-D/3-D registration of fluoroscopy images and CT scans for image-guided surgery
IEEE Transactions on Information Technology in Biomedicine
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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.