FIKIT: Priority-Based Real-time GPU Multi-tasking Scheduling with Kernel Identification

Highly parallelized workloads like machine learning training, inferences and general HPC tasks are greatly accelerated using GPU devices. In a cloud computing cluster, serving a GPU's computation power through multi-tasks sharing is highly demanded since there are always more task requests than the number of GPU available. Existing GPU sharing solutions focus on reducing task-level waiting time or task-level switching costs when multiple jobs competing for a single GPU. Non-stopped computation requests come with different priorities, having non-symmetric impact on …

cloud cloud computing cluster computation computing cs.dc cs.lg devices general gpu hpc identification inferences kernel machine machine learning power real-time scheduling solutions tasks through training workloads