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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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DAPT: A package enabling distributed automated parameter testing
Modern agent-based models (ABM) and other simulation models require evaluation and testing of many different parameters. Managing that testing for large scale parameter sweeps (grid searches), as well as storing simulation data, requires multiple, potentially customizable steps that may vary across simulations. Furthermore, parameter testing, processing, and analysis are slowed if simulation and processing jobs cannot be shared across teammates or computational resources. While high-performance computing (HPC) has become increasingly available, models can often be tested faster with the use of multiple computers and HPC resources. To address these issues, we created the Distributed Automated Parameter Testing (DAPT) Python package. By hosting parameters in an online (and often free) “database”, multiple individuals can run parameter sets simultaneously in a distributed fashion, enabling ad hoc crowdsourcing of computational power. Combining this with a flexible, scriptable tool set, teams can evaluate models and assess their underlying hypotheses quickly. Here, we describe DAPT and provide an example demonstrating its use.
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- Award ID(s):
- 1735095
- PAR ID:
- 10290192
- Date Published:
- Journal Name:
- Gigabyte
- Volume:
- 2021
- ISSN:
- 2709-4715
- Page Range / eLocation ID:
- 1 to 10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.46471/gigabyte.22
