Power and energy efficiency are among the most crucial requirements in high-performance and other computing platforms. In this work, extensive experimental methods and procedures were used to assess the power and energy efficiency of fundamental hardware building blocks inside a typical high-performance CPU, focusing on the dynamic branch predictor (DBP). The investigation relied on the Running Average Power Limit (RAPL) interface from Intel, a software tool for credibly reporting the power and energy based on instrumentation inside the CPU. We used well-known microbenchmarks under various run conditions to explore potential pitfalls and to develop precautions to raise the precision of the measurements obtained from RAPL for more reliable power estimation. The authors discuss the factors that affect the measurements and share the difficulties encountered and the lessons learned.

Power and energy efficiency are among the most crucial requirements in high-performance and other computing platforms. This work examines through extensive experimentation methods and procedures suitable for assessing the power and energy efficiency of fundamental hardware building blocks inside a typical high-performance CPU, focusing on the dynamic branch predictor (DBP). The investigation relies on the Running Average Power Limit (RAPL) interface from Intel, a software tool for credibly reporting the power and energy based on instrumentation inside the CPU. We use well-known microbenchmarks under various run conditions to explore potential pitfalls and to develop precautions to raise the precision of the measurements obtained from RAPL for more reliable power estimation. The authors discuss the factors that affect measurements and share the difficulties encountered and the lessons learned.

Spectre variants 1 and 2 pose grave security threats to dynamic branch predictors in modern CPUs. While extensive research has focused on mitigating these attacks, little attention has been given to their energy and power implications. This study presents an empirical analysis of how compiler-based Spectre mitigation techniques influence energy consumption. We collect fine-grained energy readings from an HPC-class CPU via embedded sensors, allowing us to quantify the trade-offs between security and power efficiency. By utilizing a standard suite of microbenchmarks, we evaluate the impact of Spectre mitigations across three widely used compilers, comparing them to a no-mitigation baseline. The results show that energy consumption varies significantly depending on the compiler and workload characteristics. Loop unrolling influences power consumption by altering branch distribution, while speculative execution, when unrestricted, plays a role in conserving energy. Since Spectre mitigations inherently limit speculative execution, they should be applied selectively to vulnerable code patterns to optimize both security and power efficiency. Unlike previous studies that primarily focus on security effectiveness, this work uniquely evaluates the energy costs associated with Spectre mitigations at the compiler level, offering insights for power-efficient security strategies. Our findings underscore the importance of tailoring mitigation techniques to application needs, balancing performance, energy consumption, and security. The study provides practical recommendations for compiler developers to build more secure and energy-efficient software.

Spectre variants 1 and 2 pose grave security threats to the dynamic branch predictors in CPUs. This study examines experimentally the impact of mitigating those threats on energy and power consumption. The research obtained empirical data from an HPC-class CPU via embedded sensors with fine-grained energy readings. This approach black-boxed otherwise complex behaviors in and around the CPU. It was crucial to the concrete energy estimates reported here. The study identified factors that impacted the energy behaviors of branching (the attack vector for Spectre). It focused on compiler-based mitigation strategies vs. a no-mitigation baseline on three main compilers in wide use. It utilized a standard suite of microbenchmarks to gauge the consumption likely due to branch prediction under mixed branch and mitigation conditions. Results showed that the energy consumption varied significantly across the compilers, depending on the tasks. Loop unrolling affected the power consumption as it altered the number and distribution of branches. Speculative execution had a role in conserving energy. Reducing execution time was accompanied by an increase in instantaneous power. Since Spectre mitigations limit that feature, they should be applied selectively to the code patterns vulnerable to attacks. This work hopes to create an awareness of the energy costs of securing CPUs against Spectre attacks. It suggests directions for better energy-aware mitigation strategies. It highlights the importance of fitting compiler and optimization settings to application needs to balance security, performance, and power. The study provides compiler writers with recommendations that help build secure and power-efficient software.