Slurm
Slurm (Simple Linux Utility for Resource Management) is an open-source resource management and job scheduling system for high-performance computing (HPC) clusters, widely used in supercomputing centers and research institutions. It allocates computing resources across multi-node clusters, manages job queues, and schedules task execution. Slurm supports topology-aware scheduling, priority policy configuration, and concurrent multi-user management, offering high scalability and fault tolerance.
Timeline (last 90 days)
Slurm and PBS are the two most mainstream job scheduling systems for high-performance computing clusters in academia.
OpenScience can send longer-running jobs to Modal, user-owned servers, or Slurm/PBS clusters.
Slurm (Simple Linux Utility for Resource Management) is the most widely used open-source job scheduling system in academic and national supercomputing centers.
Unlike Kubernetes's always-on service model, Slurm uses the batch job as its basic unit, with resources exclusively occupied for the duration of a job and immediately released upon its completion.
FLARE supports deployment to three mainstream infrastructure options: Docker, Kubernetes, and Slurm.
Slurm's queuing mechanism may prevent parties' jobs from starting simultaneously, so FLARE's support for Slurm requires synchronizing the startup states of all participants through job dependencies or signal files on a shared file system.
The same FLARE training code can communicate via local networking in Docker Compose, discover each other via Service in K8s Pods, and exchange model updates via gRPC persistent connections between Slurm job nodes.
Slurm is an open-source cluster management and job scheduling system developed by Lawrence Livermore National Laboratory.
SkyPilot is an open-source project that provides a unified scheduling abstraction layer for managing Slurm, Kubernetes, and cloud GPU resources.
ML teams' compute resources are typically scattered across multiple siloed systems, including Slurm clusters, Kubernetes clusters, and cloud GPUs.
All Facts (14)
Slurm支持拓扑感知作业调度功能，可用于优化GB200 NVL72系统的工作负载放置
90%Unverified传统Slurm调度器以节点为基本单位分配资源，在NVL72架构下会导致资源碎片化和通信效率下降问题
85%UnverifiedSlurm的topology plugin可以将NVL72的NVLink拓扑信息注入调度器，实现拓扑感知的分配决策
85%Unverified相比默认调度策略，块调度可以将系统整体利用率提升15%-25%
60%UnverifiedSlurm and PBS are the two most mainstream job scheduling systems for high-performance computing clusters in academia.
50%UnverifiedOpenScience can send longer-running jobs to Modal, user-owned servers, or Slurm/PBS clusters.
50%UnverifiedThe same FLARE training code can communicate via local networking in Docker Compose, discover each other via Service in K8s Pods, and exchange model updates via gRPC persistent connections between Slurm job nodes.
50%UnverifiedFLARE supports deployment to three mainstream infrastructure options: Docker, Kubernetes, and Slurm.
50%UnverifiedSlurm (Simple Linux Utility for Resource Management) is the most widely used open-source job scheduling system in academic and national supercomputing centers.
50%UnverifiedUnlike Kubernetes's always-on service model, Slurm uses the batch job as its basic unit, with resources exclusively occupied for the duration of a job and immediately released upon its completion.
50%UnverifiedSlurm's queuing mechanism may prevent parties' jobs from starting simultaneously, so FLARE's support for Slurm requires synchronizing the startup states of all participants through job dependencies or signal files on a shared file system.
50%UnverifiedSlurm is an open-source cluster management and job scheduling system developed by Lawrence Livermore National Laboratory.
50%UnverifiedSkyPilot is an open-source project that provides a unified scheduling abstraction layer for managing Slurm, Kubernetes, and cloud GPU resources.
50%UnverifiedML teams' compute resources are typically scattered across multiple siloed systems, including Slurm clusters, Kubernetes clusters, and cloud GPUs.
50%