[Paper] What Is the Cost of Energy Monitoring? An Empirical Study on the Overhead of RAPL-Based Tools

Source: arXiv - 2604.26815v1

Overview

The paper investigates a hidden cost of one of the most popular ways to measure software energy usage on modern Intel CPUs: the Running Average Power Limit (RAPL) interface. While RAPL gives developers a convenient way to read power‑draw estimates for the CPU and DRAM, the authors show that the tooling used to poll these counters at high frequency (≈1 kHz) can itself consume a non‑trivial amount of time and energy, potentially skewing the very measurements it is meant to capture.

Key Contributions

• Empirical quantification of the runtime and energy overhead introduced by seven existing RAPL‑based monitoring tools when sampling at 1 kHz.
• Design and implementation of two low‑overhead alternatives (a user‑space app and a kernel module) that dramatically reduce measurement intrusion.
• Micro‑benchmarking of the core primitives used to read Model‑Specific Registers (MSRs) – rdmsr, /proc reads, and system calls – revealing their relative latencies.
• Guidelines for tool developers on how to minimize polling overhead (e.g., inline assembly, avoiding unnecessary system calls).
• Practical “what‑if” scenarios illustrating how measurement overhead can distort energy‑aware scheduling, power‑capping, and performance‑per‑watt decisions.

Methodology

1. Controlled benchmark suite – Six NAS Parallel Benchmarks (NPB) kernels (e.g., bt, lu, sp) were run under a no‑tool baseline and under each of the seven monitoring tools.
2. High‑frequency polling – All tools were configured to read RAPL counters at 1 kHz, a rate commonly used for fine‑grained power profiling.
3. Overhead isolation – The authors built a minimal kernel module and a stripped‑down user‑space program that directly invoke the rdmsr instruction, eliminating layers such as libpfm, Python wrappers, or heavy I/O.
4. Latency measurement – Individual code paths (rdmsr, read from /proc, generic syscall) were timed using rdtsc to obtain nanosecond‑scale execution costs.
5. Statistical analysis – Overhead percentages were computed by comparing wall‑clock time and energy reported by the tools against the baseline runs.

Results & Findings

The study shows that most of the overhead comes from the cost of crossing the user‑kernel boundary (system calls) rather than the raw MSR read itself. By moving the read into kernel space or using inline assembly, the measurement intrusion can be reduced to a few nanoseconds per sample, keeping the overall profiling impact close to the no‑tool baseline.

Practical Implications

• Energy‑aware schedulers that rely on per‑core power estimates can now trust high‑frequency data without inadvertently throttling performance due to measurement overhead.
• Power‑capping and DVFS algorithms can be tuned more aggressively; the extra 0.5 % overhead of a lean tool is unlikely to affect thermal budgets.
• DevOps and CI pipelines that embed power profiling (e.g., to enforce “green” SLAs) should prefer low‑overhead libraries or kernel modules rather than generic command‑line tools.
• Tool developers can adopt the paper’s recommendations—inline rdmsr, batch reads, and avoiding per‑sample syscalls—to build next‑generation profilers that scale to multi‑GHz sampling rates.
• Hardware vendors get concrete data showing that exposing a fast, low‑latency API (e.g., a memory‑mapped RAPL register) could further reduce software overhead.

Limitations & Future Work

• The experiments focus on Intel CPUs with RAPL; ARM’s Energy‑Aware Scheduling (EAS) or AMD’s equivalents were not examined.
• Only single‑socket, non‑virtualized environments were used; virtualization layers could add additional latency.
• The study measures overhead at a fixed 1 kHz rate; behavior at higher frequencies (e.g., 10 kHz) remains an open question.
• Future work could explore adaptive sampling (varying frequency based on workload phase) and integration with existing performance‑monitoring frameworks (e.g., perf, eBPF) to provide a unified low‑overhead telemetry stack.

Authors

• Jeremy Diamond
• Vincenzo Stoico

Paper Information

• arXiv ID: 2604.26815v1
• Categories: cs.SE, cs.PF
• Published: April 29, 2026
• PDF: Download PDF