LSim: Fine-Grained Simulation Framework for Large-Scale Performance Evaluation
As large-scale workloads with massive parallelism emerge, the demand for large-scale systems such as datacenters and supercomputers is rising sharply. To accurately design a large-scale system, architects heavily rely on performance modeling at design phases. However, modeling a large-scale workload...
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Veröffentlicht in: | IEEE computer architecture letters 2022-01, Vol.21 (1), p.25-28 |
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Sprache: | eng |
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Zusammenfassung: | As large-scale workloads with massive parallelism emerge, the demand for large-scale systems such as datacenters and supercomputers is rising sharply. To accurately design a large-scale system, architects heavily rely on performance modeling at design phases. However, modeling a large-scale workload without a large-scale system is a challenging problem. This paper presents LSim, a framework for large-scale performance evaluation. Based on the captured behavior within small-scale workload traces, LSim extrapolates the behavior of the workload on a large-scale system. To do so, we propose two techniques: (1) representative trace model and (2) function latency model to synthesize a trace and to predict the latency of functions in the synthesized trace, respectively. |
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ISSN: | 1556-6056 1556-6064 |
DOI: | 10.1109/LCA.2022.3168831 |