Dynamic Optimizations in GPU using Roofline Model

Massively parallel processors such as graphics processesing units (GPUs) often face the challenge of resource underutilization due to varying resource proclivity of workloads. Running multiple applications on a GPU has been an efficient and known alternative to mitigate underutilization. This paper proposes a multi-application oriented framework that carries out dynamic optimizations based on the operational intensities of various applications. Our framework analyzes applications based on operational intensities to identify their bottleneck resources using Roofline model. We demonstrate that the proposed optimizations improve the utilization and system-wide throughput of the GPU co-running applications with irregular resource demands. The dynamic optimizations improve the performance by 14.8% on average and up to 72.4% over a state-of-the-art spatial multitasking technique.