标题： 重新思考超分配下的线程调度：一种用于协调多运行时和多进程工作负载的用户空间框架 显示英文标题

标题： Rethinking Thread Scheduling under Oversubscription: A User-Space Framework for Coordinating Multi-runtime and Multi-process Workloads

摘要： 高性能计算（HPC）与人工智能（AI）的融合正在推动日益复杂的并行应用程序和工作负载的出现。这些工作负载通常在同一应用程序内或在相邻作业之间结合多个并行运行时，产生了对传统操作系统调度器造成重大压力的调度需求。当过度分配（就绪线程数量多于核心数量）时，操作系统调度器依赖周期性抢占来共享核心，这通常会引入可能降低性能的干扰。在本文中，我们提出：（1）用户空间调度框架（USF），一种完全在用户空间实现的新型无缝进程调度框架。USF使用户能够在不需要特殊权限的情况下实现自己的进程调度算法。我们通过其默认的协作策略（2）SCHED_COOP对其进行了评估，该策略通过仅在阻塞时切换线程来减少干扰。这种方法有效缓解了众所周知的问题，如锁持有者抢占（LHP）、锁等待者抢占（LWP）和可扩展性崩溃。我们通过扩展GNU C库中的nOS-V运行时实现了USF和SCHED_COOP，从而在不需侵入性应用更改的情况下，在多个运行时（例如OpenMP）之间实现无缝协调。评估结果显示，在过度分配的多进程场景中，性能提升高达2.4倍，包括嵌套的BLAS工作负载、带有LLaMA-3的多进程PyTorch推理以及分子动力学（MD）模拟。 显示英文摘要

摘要： The convergence of high-performance computing (HPC) and artificial intelligence (AI) is driving the emergence of increasingly complex parallel applications and workloads. These workloads often combine multiple parallel runtimes within the same application or across co-located jobs, creating scheduling demands that place significant stress on traditional OS schedulers. When oversubscribed (there are more ready threads than cores), OS schedulers rely on periodic preemptions to multiplex cores, often introducing interference that may degrade performance. In this paper, we present: (1) The User-space Scheduling Framework (USF), a novel seamless process scheduling framework completely implemented in user-space. USF enables users to implement their own process scheduling algorithms without requiring special permissions. We evaluate USF with its default cooperative policy, (2) SCHED_COOP, designed to reduce interference by switching threads only upon blocking. This approach mitigates well-known issues such as Lock-Holder Preemption (LHP), Lock-Waiter Preemption (LWP), and scalability collapse. We implement USF and SCHED_COOP by extending the GNU C library with the nOS-V runtime, enabling seamless coordination across multiple runtimes (e.g., OpenMP) without requiring invasive application changes. Evaluations show gains up to 2.4x in oversubscribed multi-process scenarios, including nested BLAS workloads, multi-process PyTorch inference with LLaMA-3, and Molecular Dynamics (MD) simulations.