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LLload: Simplifying Real-Time Job Monitoring for HPC Users

Chansup Byun, Julia Mullen, Albert Reuther, William Arcand, William Bergeron, David Bestor, Daniel Burrill, Vijay Gadepally, Michael Houle, Matthew Hubbell, Hayden Jananthan, Michael Jones, Peter Michaleas, Guillermo Morales, Andrew Prout, Antonio Rosa, Charles Yee, Jeremy Kepner, Lauren Milechin

TL;DR

The paper introduces LLload, a lightweight, real-time, per-user resource monitoring tool for HPC clusters designed to lower the barrier to effective performance tuning. It leverages standard tools (SLURM and vendor utilities) to collect CPU, GPU, and memory metrics and presents them in a human-readable format with minimal overhead. The authors describe the design, implementation details, and integration into research facilitation and training programs, including best-practice guidance for resource requests. The work demonstrates practical impact by enabling researchers and operators to diagnose utilization patterns and incrementally optimize allocations in real time.

Abstract

One of the more complex tasks for researchers using HPC systems is performance monitoring and tuning of their applications. Developing a practice of continuous performance improvement, both for speed-up and efficient use of resources is essential to the long term success of both the HPC practitioner and the research project. Profiling tools provide a nice view of the performance of an application but often have a steep learning curve and rarely provide an easy to interpret view of resource utilization. Lower level tools such as top and htop provide a view of resource utilization for those familiar and comfortable with Linux but a barrier for newer HPC practitioners. To expand the existing profiling and job monitoring options, the MIT Lincoln Laboratory Supercomputing Center created LLoad, a tool that captures a snapshot of the resources being used by a job on a per user basis. LLload is a tool built from standard HPC tools that provides an easy way for a researcher to track resource usage of active jobs. We explain how the tool was designed and implemented and provide insight into how it is used to aid new researchers in developing their performance monitoring skills as well as guide researchers in their resource requests.

LLload: Simplifying Real-Time Job Monitoring for HPC Users

TL;DR

The paper introduces LLload, a lightweight, real-time, per-user resource monitoring tool for HPC clusters designed to lower the barrier to effective performance tuning. It leverages standard tools (SLURM and vendor utilities) to collect CPU, GPU, and memory metrics and presents them in a human-readable format with minimal overhead. The authors describe the design, implementation details, and integration into research facilitation and training programs, including best-practice guidance for resource requests. The work demonstrates practical impact by enabling researchers and operators to diagnose utilization patterns and incrementally optimize allocations in real time.

Abstract

One of the more complex tasks for researchers using HPC systems is performance monitoring and tuning of their applications. Developing a practice of continuous performance improvement, both for speed-up and efficient use of resources is essential to the long term success of both the HPC practitioner and the research project. Profiling tools provide a nice view of the performance of an application but often have a steep learning curve and rarely provide an easy to interpret view of resource utilization. Lower level tools such as top and htop provide a view of resource utilization for those familiar and comfortable with Linux but a barrier for newer HPC practitioners. To expand the existing profiling and job monitoring options, the MIT Lincoln Laboratory Supercomputing Center created LLoad, a tool that captures a snapshot of the resources being used by a job on a per user basis. LLload is a tool built from standard HPC tools that provides an easy way for a researcher to track resource usage of active jobs. We explain how the tool was designed and implemented and provide insight into how it is used to aid new researchers in developing their performance monitoring skills as well as guide researchers in their resource requests.
Paper Structure (6 sections, 2 figures)

This paper contains 6 sections, 2 figures.

Table of Contents

  1. Introduction
  2. LLload
  3. Design
  4. Implementation
  5. Integrating LLload into Research Facilitation
  6. Summary

Figures (2)

  • Figure 1: The default behavior of LLload.
  • Figure 2: Typical output of LLload with the -g option.