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Data movement is a key aspect of energy consumption in modern computing systems. As computation becomes more energy efficient, the cost of data movement gradually becomes a more relevant issue, especially in high-performance computing systems. The relevance of data movement can be studied at different scales, ranging from microcontrollers and microarchitectures to future Exascale systems. The goal of this work is to analyze the power costs of performing I/O operations and intra-node data movement, focusing on the operating system's I/O stack. Our approach combines the hardware instrumentation, software instrumentation, and data analysis techniques to gain insights into how different I/O patterns make use of system resources, including electrical power. We synthesize this data-driven process into a methodology and present the results of applying this methodology on sequential read and write patterns. As a result, we identify the key system metrics that contribute to I/O-related power usage and discover how the system makes transitions between different power and performance regimes based on the I/O patterns.
HPC; I/O operations; Power analysis; System metrics; Statistical analysis; Model