2017/12/23 by Waqar Ali, Ali, Waqar, Heechul Yun +1 · 1 citation
Computer Science · #Distributed systems and fault tolerance #FOS: Computer and information sciences #Operating Systems (cs.OS) #Parallel Computing and Optimization Techniques #Performance (cs.PF) #Real-Time Systems Scheduling
paper · pdf · doi:10.48550/arxiv.1712.08738
openalex publication_date 2017/12/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Integrated CPU-GPU architecture provides excellent acceleration capabilities for data parallel applications on embedded platforms while meeting the size, weight and power (SWaP) requirements. However, sharing of main memory between CPU applications and GPU kernels can severely affect the execution of GPU kernels and diminish the performance gain provided by GPU. For example, in the NVIDIA Tegra K1 platform which has the integrated CPU-GPU architecture, we noticed that in the worst case scenario, the GPU kernels can suffer as much as 4X slowdown in the presence of co-running memory intensive CPU applications compared to their solo execution. In this paper, we propose a software mechanism, which we call BWLOCK++, to protect the performance of GPU kernels from co-scheduled memory intensive CPU applications.