The Running Average Power Limit (RAPL) interface is widely used to estimate software energy consumption via CPU and DRAM counters, but tool design differences and high-frequency polling can introduce measurement overhead, namely, extra time and energy consumed by the tool itself.This paper quantifies the impact of RAPL-based tools on high-frequency (1 kHz) energy monitoring and investigates mitigation strategies. We conduct two controlled experiments: the first evaluates seven tools, including a user-space application and a kernel module developed by the authors, against a no-tool baseline, using six NAS Benchmark functions to quantify overhead. The second experiment isolates and times key functions for polling Model-Specific Registers (MSRs) (rdmsr and sys/proc_read) to estimate their execution latencies and identify potential slowdowns. The results show that existing user-space tools can introduce substantial time overhead at 1 kHz, whereas our tools significantly reduce system call overhead and inline math overhead. The time overhead of existing tools ranges from 0.25% to 46.75%. Our solutions maintain time overhead levels close to the baseline. We also find that system calls are slower than rdmsr, which in turn is slower than traditionally long-running instructions like cpuid. These findings indicate that RAPL-based energy measurement can be substantially improved by simplifying tool design and employing lower-level instructions to access RAPL values. Our findings provide guidance for practitioners on how to develop high-frequency energy profiling tools, show possible situations that can skew energy values, and demonstrate that access to RAPL values can be faster using specific techniques.

翻译：Running Average Power Limit（RAPL）接口广泛用于通过CPU和DRAM计数器估算软件能耗，但工具设计差异和高频轮询会引入测量开销，即工具本身消耗的额外时间和能量。本文量化了基于RAPL工具在高频（1 kHz）能量监测中的影响，并探讨缓解策略。我们开展两项对照实验：第一项实验在无工具的基线条件下评估七种工具（包括作者开发的用户空间应用和内核模块），使用六组NAS基准函数量化开销；第二项实验隔离并计时轮询模型特定寄存器（MSR）的关键函数（rdmsr与sys/proc_read），估算其执行延迟并识别潜在瓶颈。结果表明，现有用户空间工具在1 kHz频率下可能引入显著时间开销，而我们的工具大幅降低系统调用开销和内联数学运算开销。现有工具的时间开销范围为0.25%至46.75%，我们的解决方案将时间开销维持在接近基线水平。此外发现系统调用慢于rdmsr，而rdmsr慢于传统长耗时指令（如cpuid）。这些发现表明，通过简化工具设计并采用底层指令访问RAPL值，可显著改进基于RAPL的能耗测量。本研究为开发者提供高频能耗分析工具的构建指导，揭示可能扭曲能耗值的典型场景，并证明通过特定技术能更快获取RAPL值。