Search for: All records

The concurrent execution of deep neural networks (DNN) inference tasks on intermittently-powered batteryless devices (IPDs) has recently garnered much attention due to its potential in a broad range of smart sensing applications. While the checkpointing mechanisms (CMs) provided by the state-of-the-art make this possible, scheduling inference tasks on IPDs is still a complex problem due to significant performance variations across DNN layers and CM choices. This complexity is further accentuated by dynamic environmental conditions and inherent resource constraints of IPDs. To tackle these challenges, we present MII, a framework designed for intermittence-aware inference and scheduling on IPDs. MII formulates the shutdown and live time functions of an IPD from profiling data, which our offline intermittence-aware search scheme uses to find optimal layer-wise CMs for each task. At runtime, MII enhances job success rates by dynamically making scheduling decisions to mitigate workload losses from power interruptions and adjusting these CMs in response to actual energy patterns. Our evaluation demonstrates the superiority of MII over the state-of-the-art. In controlled environments, MII achieves an average increase of 21% and 39% in successful jobs under stable and dynamic energy patterns. In real-world settings, MII achieves 33% and 24% more successful jobs indoors and outdoors. 

Scheduling real-time tasks that utilize GPUs with analyzable guarantees poses a significant challenge due to the intricate interaction between CPU and GPU resources, as well as the complex GPU hardware and software stack. While much research has been conducted in the real-time research community, several limitations persist, including the absence or limited availability of GPU-level preemption, extended blocking times, and/or the need for extensive modifications to program code. In this paper, we propose GCAPS, a GPU Context-Aware Preemptive Scheduling approach for real-time GPU tasks. Our approach exerts control over GPU context scheduling at the device driver level and enables preemption of GPU execution based on task priorities by simply adding one-line macros to GPU segment boundaries. In addition, we provide a comprehensive response time analysis of GPU-using tasks for both our proposed approach as well as the default Nvidia GPU driver scheduling that follows a work-conserving round-robin policy. Through empirical evaluations and case studies, we demonstrate the effectiveness of the proposed approaches in improving taskset schedulability and response time. The results highlight significant improvements over prior work as well as the default scheduling approach, with up to 40% higher schedulability, while also achieving predictable worst-case behavior on Nvidia Jetson embedded platforms.