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High-performance computing (HPC) resources are used in a wide range of scientic and engineering calculations. These resources have high initial and running costs. Thus, their optimal performance is crucial. There are a number of strategies to ensure the optimal state. One of them is continuous performance monitoring, where a set of applications and input parameters are executed regularly to identify performance issues proactively. Some sites hesitate to use such a strategy as it takes away the CPU cycles from actual users. The goal of this work is to identify node availability, both size- and time-wise, on busy HPC systems. Such availability spots can be used to tailor test jobs to minimize user impact. Two systems were analyzed: small - 118 nodes from the Center for Computational Research at the University at Bualo and large - 1,160 nodes from the Texas Advanced Computing Center. It was found that for days with 90% utilization and above, there are plenty of opportunities for test jobs. For example, on a small cluster, 8 nodes for 30 minutes are available for an average of 2.3 hours throughout the day. That is 9.6% of the day the scheduler has the opportunity to schedule such a job. On a large system, 32 nodes for 30 minutes were available on average 9.2 hours a day (or 38% of day). Thus, there is a space for test jobs, but it is not evident that the scheduler can benet from it, and a proper strategy must be used, for example, by lowering test job priorities.
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This content will become publicly available on December 8, 2025
HPCMod: Agent-Based Modeling Framework for Modeling Users on High-Performance Computing Resources
High-performance computing (HPC) resources are used for compute-demanding calculations in various fields of science and engineering. They are large computational facilities utilized by many users simultaneously. High utilization often leads to high waiting times. Simulating users' behavior on such a system can help with future system design, develop user interventions, and ultimately improve the user’s experience and resource utilization. Here, we present HPCMod, an Agent-Based Modeling Framework for Modeling Users on HPC Resources. The key concept of the framework is the representation of the user's computational needs: the user project is represented as a collection of possibly dependent compute tasks. Each task can be executed as a single compute job or a series of jobs, depending on the task size. Some tasks can be too big to be executed in one chunk; such a situation often occurs during molecular dynamics simulation. There are multiple ways in which tasks can be split into jobs, and users will make their decisions based on previous experience, application parallel scalability, and available resources. For example, a user's compute task requires 32 node hours; it can be executed in multiple ways: a single 32-hour job on one node, two sequential 16-hour jobs on one node, one 16-hour job on two nodes, and so on. In the HPCMod, we implemented three models: 1) historical replay of compute jobs, 2) simulation of reconstituted compute tasks using historical job sizes, and 3) adaptive compute tasks splitting where users can modify jobs parameters given available resources till the execution of the next job in line. The framework was tested on a ten-node test system and a larger 1,736-node system modeled after a portion of TACC Stampede-2. The HPC resource model implements a first in first out (FIFO) scheduler with backfill scheduling. The initial results showed that on a tiny system, adaptive task-splitting is beneficial for the user but leads to a larger number of jobs. On a large system, the adaptive task-splitting was also very beneficial, decreasing waiting times for users using this strategy almost two times; however, other users got a 5% increase in their wait time. Further investigation is needed as the current task reconstitution algorithm is deterministic and does not allow quantification of job recombination uncertainties. The Julia-based implementation is fast: five years of historic workflow consisting of a million jobs and a one-hour stepping took around three minutes.
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- Award ID(s):
- 2004954
- PAR ID:
- 10637351
- Publisher / Repository:
- Accelerating Open Access Science in Human Factors Engineering and Human-Centered Computing
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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