Four energy measurement methods were compared on Kubernetes with Nextflow; shell-script and plugin approaches are effective, but concurrent tasks require heuristics for per-task attribution.
Energy Efficiency Aspects of the AMD Zen 2 Architecture
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abstract
In High Performance Computing, systems are evaluated based on their computational throughput. However, performance in contemporary server processors is primarily limited by power and thermal constraints. Ensuring operation within a given power envelope requires a wide range of sophisticated control mechanisms. While some of these are handled transparently by hardware control loops, others are controlled by the operating system. A lack of publicly disclosed implementation details further complicates this topic. However, understanding these mechanisms is a prerequisite for any effort to exploit the full computing capability and to minimize the energy consumption of today's server systems. This paper highlights the various energy efficiency aspects of the AMD Zen 2 microarchitecture to facilitate system understanding and optimization. Key findings include qualitative and quantitative descriptions regarding core frequency transition delays, workload-based frequency limitations, effects of I/O die P-states on memory performance as well as discussion on the built-in power monitoring capabilities and its limitations. Moreover, we present specifics and caveats of idle states, wakeup times as well as the impact of idling and inactive hardware threads and cores on the performance of active resources such as other cores.
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cs.DC 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Strategies to Measure Energy Consumption Using RAPL During Workflow Execution on Commodity Clusters
Four energy measurement methods were compared on Kubernetes with Nextflow; shell-script and plugin approaches are effective, but concurrent tasks require heuristics for per-task attribution.