Close

Presentation

JUmPER: Performance Data Monitoring, Instrumentation and Visualization for Jupyter Notebooks
DescriptionComputational performance, e.g. CPU or GPU utilization, is crucial for analyzing machine learning (ML) applications and their resource-efficient deployment. However, the ML community often lacks accessible tools for holistic performance engineering, especially during exploratory programming such as implemented by Jupyter. Therefore, we present JUmPER, a Jupyter kernel that supports coarse-grained performance monitoring and fine-grained analysis tasks of user code in Jupyter.

JUmPER collects system metrics and stores them alongside executed user code. Additionally, code instrumentation can be enabled to collect performance events using Score-P. Built-in Jupyter magic commands provide visualizations of the monitored performance data directly in Jupyter. In addition, JUmPER preserves the exploratory programming experience by seamlessly integrating with Jupyter and reducing kernel runtime overhead through in-memory (pipe) communication and parallel marshalling of Python's interpreter state for the Score-P execution.

JUmPER thus provides a low-hurdle infrastructure for performance engineering in Jupyter and supports resource-efficient ML applications.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Doctoral Showcase
Posters
TimeTuesday, 19 November 202412pm - 5pm EST
LocationB302-B305
Registration Categories
TP
XO/EX