Precise Runahead Execution

• Published in: 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA) pp. 397 - 410
• Main Authors: Naithani, Ajeya, Feliu, Josue, Adileh, Almutaz, Eeckhout, Lieven
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-02-2020
• Subjects: Buffer storage; Memory management; Microarchitecture; Out of order; Pipelines; Prefetching; Proposals; Registers; runahead execution; single core performance
• ISSN: 2378-203X
• DOI: 10.1109/HPCA47549.2020.00040

Runahead execution improves processor performance by accurately prefetching long-latency memory accesses. When a long-latency load causes the instruction window to fill up and halt the pipeline, the processor enters runahead mode and keeps speculatively executing code to trigger accurate prefetches. A recent improvement tracks the chain of instructions that leads to the long-latency load, stores it in a runahead buffer, and executes only this chain during runahead execution, with the purpose of generating more prefetch requests. Unfortunately, all prior runahead proposals have shortcomings that limit performance and energy efficiency because they release processor state when entering runahead mode and then need to refill the pipeline to restart normal operation. Moreover, runahead buffer limits prefetch coverage by tracking only a single chain of instructions that leads to the same long-latency load. We propose precise runahead execution (PRE) which builds on the key observation that when entering runahead mode, the processor has enough issue queue and physical register file resources to speculatively execute instructions. This mitigates the need to release and re-fill processor state in the ROB, issue queue, and physical register file. In addition, PRE pre-executes only those instructions in runahead mode that lead to full-window stalls, using a novel register renaming mechanism to quickly free physical registers in runahead mode, further improving efficiency and effectiveness. Finally, PRE optionally buffers decoded runahead micro-ops in the frontend to save energy. Our experimental evaluation using a set of memory-intensive applications shows that PRE achieves an additional 18.2% performance improvement over the recent runahead proposals while at the same time reducing energy consumption by 6.8%.