Constable: Improving Performance and Power Efficiency by Safely Eliminating Load Instruction Execution (2406.18786v1)

Abstract: Load instructions often limit instruction-level parallelism (ILP) in modern processors due to data and resource dependences they cause. Prior techniques like Load Value Prediction (LVP) and Memory Renaming (MRN) mitigate load data dependence by predicting the data value of a load instruction. However, they fail to mitigate load resource dependence as the predicted load instruction gets executed nonetheless. Our goal in this work is to improve ILP by mitigating both load data dependence and resource dependence. To this end, we propose a purely-microarchitectural technique called Constable, that safely eliminates the execution of load instructions. Constable dynamically identifies load instructions that have repeatedly fetched the same data from the same load address. We call such loads likely-stable. For every likely-stable load, Constable (1) tracks modifications to its source architectural registers and memory location via lightweight hardware structures, and (2) eliminates the execution of subsequent instances of the load instruction until there is a write to its source register or a store or snoop request to its load address. Our extensive evaluation using a wide variety of 90 workloads shows that Constable improves performance by 5.1% while reducing the core dynamic power consumption by 3.4% on average over a strong baseline system that implements MRN and other dynamic instruction optimizations (e.g., move and zero elimination, constant and branch folding). In presence of 2-way simultaneous multithreading (SMT), Constable's performance improvement increases to 8.8% over the baseline system. When combined with a state-of-the-art load value predictor (EVES), Constable provides an additional 3.7% and 7.8% average performance benefit over the load value predictor alone, in the baseline system without and with 2-way SMT, respectively.

• The paper introduces Constable, which dynamically identifies and safely eliminates likely-stable load instructions to mitigate both data and resource dependencies.
• It leverages lightweight hardware structures, RMT and AMT, to monitor register and memory stability, converting loads into efficient register moves.
• Extensive evaluations on 90 workloads show an average 5.1% performance improvement and 3.4% reduction in power, with up to 8.8% gains in SMT configurations.

Overview of Constable: Improving Performance and Power Efficiency by Safely Eliminating Load Instruction Execution

The paper presents Constable, an innovative microarchitectural technique aimed at enhancing Instruction-Level Parallelism (ILP) in modern processors by safely eliminating the execution of load instructions. The need for such a mechanism arises from the inherent limitations posed by load instructions in terms of both data and resource dependencies. Existing techniques like Load Value Prediction (LVP) and Memory Renaming (MRN) primarily address data dependencies but still require execution of the predicted load instructions, thereby consuming critical pipeline resources. Constable seeks to mitigate both data and resource dependencies, thereby unlocking the potential for greater performance improvements and energy efficiency.

Key Contributions

1. Identification of Likely-Stable Loads: Constable dynamically identifies load instructions that consistently fetch the same data from the same memory address, termed as "likely-stable" loads. This identification is based on a stability confidence mechanism that tracks the execution outcomes of load instructions over time.
2. Elimination Mechanism: Once a load instruction is identified as likely-stable, Constable safely eliminates its execution by leveraging two key hardware structures: the Register Monitor Table (RMT) and the Address Monitor Table (AMT). RMT monitors changes to the source architectural registers, whereas AMT tracks modifications to the memory locations, ensuring that no changes occur between successive instances of the likely-stable load instructions.
3. Performance and Power Evaluation: Through extensive evaluation using 90 diverse workloads, Constable demonstrates an average performance improvement of 5.1% and a reduction in core dynamic power consumption by 3.4% over a strong baseline system. The performance benefits are even more pronounced in a 2-way simultaneous multithreading (SMT) configuration, achieving up to 8.8% improvement.
4. Integration with Existing Techniques: Constable also shows compatibility and additional performance benefits when combined with state-of-the-art load value predictors like EVES. Combined with EVES, Constable provides an additional 3.7% and 7.8% average performance benefits in non-SMT and SMT configurations, respectively.

Detailed Insights

Motivation and Design Choices

The paper articulates that load instructions, due to their dual-component operations (address computation and data fetch), often become bottlenecks in the pipeline by causing both data and resource dependencies. LVP and MRN mitigate only the data dependencies, leaving a gap that Constable aims to fill by also addressing resource dependencies.

Identification and Elimination Mechanism

The identification of likely-stable load instructions is a critical aspect of Constable. It employs a program counter (PC)-indexed table called Stable Load Detector (SLD) to track the past behavior of load instructions. If a load instruction repeatedly fetches the same value from the same address, it is marked as likely-stable upon meeting a stability confidence threshold.

For elimination, Constable introduces lightweight hardware structures: RMT and AMT. RMT ensures no modifications to source registers, and AMT tracks memory location changes. By maintaining these conditions, Constable safely translates the load instruction into a register move operation that bypasses the traditional load execution path, thereby mitigating resource dependency.

Implications and Future Directions

The implementation of Constable has several significant practical and theoretical implications. On the practical side, it offers a viable path to improve the performance and energy efficiency of contemporary processors without substantial hardware overhead. The paper reports a modest storage overhead of only 12.4 KB per core, making it a highly cost-effective solution.

Theoretically, Constable opens up new avenues for research in microarchitectural optimization. Future developments could explore further refinements in stability detection mechanisms, approaches to handle a broader range of load instructions, and integration with more complex multi-threaded and multi-core processor architectures. Additionally, investigating the long-term impact of load elimination on system reliability and exploring software-level optimizations to complement hardware-based load elimination are interesting directions for further research.

Conclusion

Constable represents a noteworthy advancement in microarchitectural techniques aimed at improving processor performance and power efficiency. By addressing both data and resource dependencies associated with load instructions, it achieves significant performance gains and power savings. The evaluation results substantiate the feasibility and practicality of the proposed technique, demonstrating its potential for integration into future high-performance processor designs. As hardware resource scaling continues to present challenges, techniques like Constable that optimize resource usage at the microarchitectural level are likely to become increasingly valuable.