Accurate and Scalable Many-Node Simulation

Fuente: arXiv
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Autores principales: Eyerman, Stijn, Heirman, Wim, Bois, Kristof Du, Hur, Ibrahim
Formato: Preprint
Publicado: 2024
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author Eyerman, Stijn
Heirman, Wim
Bois, Kristof Du
Hur, Ibrahim
author_facet Eyerman, Stijn
Heirman, Wim
Bois, Kristof Du
Hur, Ibrahim
contents Accurate performance estimation of future many-node machines is challenging because it requires detailed simulation models of both node and network. However, simulating the full system in detail is unfeasible in terms of compute and memory resources. State-of-the-art techniques use a two-phase approach that combines detailed simulation of a single node with network-only simulation of the full system. We show that these techniques, where the detailed node simulation is done in isolation, are inaccurate because they ignore two important node-level effects: compute time variability, and inter-node communication. We propose a novel three-stage simulation method to allow scalable and accurate many-node simulation, combining native profiling, detailed node simulation and high-level network simulation. By including timing variability and the impact of external nodes, our method leads to more accurate estimates. We validate our technique against measurements on a multi-node cluster, and report an average 6.7% error on 64 nodes (maximum error of 12%), compared to on average 27% error and up to 54% when timing variability and the scaling overhead are ignored. At higher node counts, the prediction error of ignoring variable timings and scaling overhead continues to increase compared to our technique, and may lead to selecting the wrong optimal cluster configuration. Using our technique, we are able to accurately project performance to thousands of nodes within a day of simulation time, using only a single or a few simulation hosts. Our method can be used to quickly explore large many-node design spaces, including node micro-architecture, node count and network configuration.
format Preprint
id arxiv_https___arxiv_org_abs_2401_09877
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Accurate and Scalable Many-Node Simulation
Eyerman, Stijn
Heirman, Wim
Bois, Kristof Du
Hur, Ibrahim
Performance
Accurate performance estimation of future many-node machines is challenging because it requires detailed simulation models of both node and network. However, simulating the full system in detail is unfeasible in terms of compute and memory resources. State-of-the-art techniques use a two-phase approach that combines detailed simulation of a single node with network-only simulation of the full system. We show that these techniques, where the detailed node simulation is done in isolation, are inaccurate because they ignore two important node-level effects: compute time variability, and inter-node communication. We propose a novel three-stage simulation method to allow scalable and accurate many-node simulation, combining native profiling, detailed node simulation and high-level network simulation. By including timing variability and the impact of external nodes, our method leads to more accurate estimates. We validate our technique against measurements on a multi-node cluster, and report an average 6.7% error on 64 nodes (maximum error of 12%), compared to on average 27% error and up to 54% when timing variability and the scaling overhead are ignored. At higher node counts, the prediction error of ignoring variable timings and scaling overhead continues to increase compared to our technique, and may lead to selecting the wrong optimal cluster configuration. Using our technique, we are able to accurately project performance to thousands of nodes within a day of simulation time, using only a single or a few simulation hosts. Our method can be used to quickly explore large many-node design spaces, including node micro-architecture, node count and network configuration.
title Accurate and Scalable Many-Node Simulation
topic Performance
url https://arxiv.org/abs/2401.09877