Analytic Roofline Modeling and Energy Analysis of LULESH Proxy Application on Multi-Core Clusters
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929626980286464 |
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| author | Afzal, Ayesha Hager, Georg Wellein, Gerhard |
| author_facet | Afzal, Ayesha Hager, Georg Wellein, Gerhard |
| contents | We present a thorough performance and energy consumption analysis of the LULESH proxy application in its OpenMP and MPI variants on two different clusters based on Intel Ice Lake (ICL) and Sapphire Rapids (SPR) CPUs. We first study the strong scaling and power consumption characteristics of the six hot spot functions in the code on the node level, with a special focus on memory bandwidth utilization. We then proceed with the construction of a detailed Roofline performance model for each memory-bound hot spot, which we validate using hardware performance counter measurements. We also comment on the observed discrepancies between the analytical model and the observations. To discern the influence of the programming model from the influence of implementation of the code, we compare the performance of OpenMP and MPI based on problem size, examining if the underlying implementation is equivalent for large problems, and if differences in overheads are more significant at smaller problem sizes. We also conduct an analysis of the power dissipation, energy to solution, and energy-delay product (EDP) of the hot spots, quantifying the influence of problem size, core and uncore clock frequency, and number of active cores per ccNUMA domain. Relevant energy savings are only possible for memory-bound functions by using fewer cores per ccNUMA domain and/or reducing the core clock speed. A major issue is the very high extrapolated baseline power on both chips, which makes concurrency throttling less effective. In terms of energy-delay product (EDP), on SPR only memory-bound workloads offer lower EDP compared to Ice Lake. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08792 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Analytic Roofline Modeling and Energy Analysis of LULESH Proxy Application on Multi-Core Clusters Afzal, Ayesha Hager, Georg Wellein, Gerhard Distributed, Parallel, and Cluster Computing Performance We present a thorough performance and energy consumption analysis of the LULESH proxy application in its OpenMP and MPI variants on two different clusters based on Intel Ice Lake (ICL) and Sapphire Rapids (SPR) CPUs. We first study the strong scaling and power consumption characteristics of the six hot spot functions in the code on the node level, with a special focus on memory bandwidth utilization. We then proceed with the construction of a detailed Roofline performance model for each memory-bound hot spot, which we validate using hardware performance counter measurements. We also comment on the observed discrepancies between the analytical model and the observations. To discern the influence of the programming model from the influence of implementation of the code, we compare the performance of OpenMP and MPI based on problem size, examining if the underlying implementation is equivalent for large problems, and if differences in overheads are more significant at smaller problem sizes. We also conduct an analysis of the power dissipation, energy to solution, and energy-delay product (EDP) of the hot spots, quantifying the influence of problem size, core and uncore clock frequency, and number of active cores per ccNUMA domain. Relevant energy savings are only possible for memory-bound functions by using fewer cores per ccNUMA domain and/or reducing the core clock speed. A major issue is the very high extrapolated baseline power on both chips, which makes concurrency throttling less effective. In terms of energy-delay product (EDP), on SPR only memory-bound workloads offer lower EDP compared to Ice Lake. |
| title | Analytic Roofline Modeling and Energy Analysis of LULESH Proxy Application on Multi-Core Clusters |
| topic | Distributed, Parallel, and Cluster Computing Performance |
| url | https://arxiv.org/abs/2412.08792 |