GMA Instrumentation of the Athena Framework using NetLogger
Craig E. Tull, Dan Gunter, Wim Lavrijsen, David Quarrie, Brian Tierney
Abstract
Grid applications are, by their nature, wide-area distributed applications. This WAN aspect of Grid applications makes the use of conventional monitoring and instrumentation tools (such as top, gprof, LSF Monitor, etc) impractical for verification that the application is running correctly and efficiently. To be effective, monitoring data must be "end-to-end", meaning that all components between the Grid application endpoints must be monitored. Instrumented applications can generate a large amount of monitoring data, so typically the instrumentation is off by default. For jobs running on a Grid, there needs to be a general mechanism to remotely activate the instrumentation in running jobs. The NetLogger Toolkit Activation Service provides this mechanism. To demonstrate this, we have instrumented the ATLAS Athena Framework with NetLogger to generate monitoring events. We then use a GMA-based activation service to control NetLogger's trigger mechanism. The NetLogger trigger mechanism allows one to easily start, stop, or change the logging level of a running program by modifying a trigger file. We present here details of the design of the NetLogger implementation of the GMA-based activation service and the instrumentation service for Athena. We also describe how this activation service allows us to non-intrusively collect and visualize the ATLAS Athena Framework monitoring data.
Create a lesson
Related papers
PixelFlow: Token-Level Workload Management for Efficient Distributed DiT Serving
Zhexiang Zhang, Minchen Yu, Yifan Sun et al.
A Kubernetes-Native Request Router for Quality-Aware Inference Serving in the Computing Continuum
Ignjat Karanovic, Pantelis A. Frangoudis, Ivan Čilić et al.
Accelerating Sharded Data Parallelism at Scale with Federated Learning
Gianluca Mittone, Marco Aldinucci
Distributed Edge Inference: an Experimental Study on Multiview Detection
Gianluca Mittone, Giulio Malenza, Marco Aldinucci et al.
P-GADMM: Parallel Group-Based ADMM for Asynchronous Optimization in Heterogeneous Edge Networks
Gaiguo Wei, Qingying Zhang, Heqiang Wang et al.
VERA: Reinforcement Learning for Dynamic Memory Scaling of HPC Workloads in Kubernetes
Ade Pramono, Jie Ren, Ivy Peng