XSched: Preemptive Scheduling for Diverse XPUs
Weihang Shen, Mingcong Han, Jialong Liu, Rong Chen, Haibo Chen
Abstract
XPUs, such as GPUs, NPUs, ASICs, and FPGAs, lack flexible scheduling capabilities, failing to meet rich application requirements (e.g., priority and fairness) in multitasking environments. This paper presents XSched, a scheduling framework that enables preemptive scheduling on diverse XPUs with flexible policies. XSched provides unified interfaces for scheduling XPU tasks through a preemptible command queue abstraction (XQueue). The key challenge in implementing the abstraction is adapting to XPUs with diverse and evolving hardware capabilities and software stacks. XSched proposes a multi-level hardware model that enables mature, advanced XPUs to achieve optimal scheduling performance while maintaining compatibility with emerging, wimpy XPUs. To demonstrate the generalizability of XSched, we adapted it to ten XPUs of different types, brands, and generations across seven software platforms and implemented two hardwareagnostic scheduling policies. We further evaluated XSched through three case studies of multitasking workloads on XPUs. XSched effectively achieves various scheduling objectives using its efficient and flexible preemption mechanisms.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 59439499-23b2-4f1c-a357-9c7c8c79ea36Cited by top-tier papers6
- Prism: Cost-Efficient Multi-LLM Serving via GPU Memory BallooningShan Yu, Yifan Qiao, Mingyuan Ma, Yangmin Li et al.OSDI 2026 · 33 citations
- Colocating ML Inference and Training with Fast GPU Memory HandoverJiali Wang, Yankui Wang, Mingcong Han, Rong ChenUSENIX ATC 2025 · 12 citations
- Characterizing Mobile SoC for Accelerating Heterogeneous LLM InferenceLe Chen, Dahu Feng, Erhu Feng, Yingrui Wang et al.SOSP 2025 · 3 citations
- Nixie: Efficient, Transparent Temporal Multiplexing for Consumer GPUsYechen Xu, Yifei Wang, Nathanael Ren, Yiran Chen et al.OSDI 2026 · 2 citations
- Weave: Efficient Co-Scheduling for Disaggregated RL Post-TrainingTianyuan Wu, Lunxi Cao, Yining Wei, Wei Gao et al.OSDI 2026
Builds on25
- Serving DNNs like Clockwork: Performance Predictability from the Bottom UpArpan Gujarati, Reza Karimi, Safya Alzayat, Wei Hao et al.OSDI 2020 · 392 citations
- AntMan: Dynamic Scaling on GPU Clusters for Deep LearningWencong Xiao, Shiru Ren, Yong Li, Yang Zhang et al.OSDI 2020 · 260 citations
- SHEPHERD: Serving DNNs in the WildHong Zhang, Yupeng Tang, Anurag Khandelwal, Ion StoicaNSDI 2023 · 161 citations
- Microsecond-scale Preemption for Concurrent GPU-accelerated DNN InferencesMingcong Han, Hanze Zhang, Rong Chen, Haibo ChenOSDI 2022 · 153 citations
- PipeSwitch: Fast Pipelined Context Switching for Deep Learning ApplicationsZhihao Bai, Zhen Zhang, Yibo Zhu, Xin JinOSDI 2020 · 152 citations
Related papers
- GFS: A Preemption-aware Scheduling Framework for GPU Clusters with Predictive Spot Instance ManagementJiaang Duan, Shenglin Xu, Shiyou Qian, Dingyu Yang et al.ASPLOS 2026 · 1 citation
- Balancing Energy Efficiency and Real-Time Performance in GPU SchedulingYidi Wang, Mohsen Karimi, Yecheng Xiang, Hyoseung KimRTSS 2021 · 29 citations
- Hardware-Assisted Virtualization of Neural Processing Units for Cloud PlatformsYuqi Xue, Yiqi Liu, Lifeng Nai, Jian HuangMICRO 2024 · 10 citations
- Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning WorkloadsDeepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee et al.OSDI 2020 · 286 citations
- Towards Energy-Efficient Real-Time Scheduling of Heterogeneous Multi-GPU SystemsYidi Wang, Mohsen Karimi, Hyoseung KimRTSS 2022 · 8 citations
